revlon case study analysis

Revlon SWOT Analysis

Before we dive deep into the SWOT analysis, let’s get the business overview of Revlon. Revlon, Inc. is a multinational American beauty company specializing in cosmetics, skincare, perfume, and personal care products. Established in New York City in 1932 by Charles and Joseph Revson and chemist Charles Lachman, Revlon has become a global leader in the beauty industry, selling its products in over 150 countries.

The company’s extensive product portfolio includes iconic brands and is designed to cater to a diverse range of beauty needs across different segments of the market, such as Revlon for the mass market, Princess Marcella Borghese for upscale/international markets, and Ultima II for premium segments, among others.

Revlon’s journey has included various strategic moves, such as segmenting its operations into different divisions in the 1960s, similar to the strategy used by General Motors, each targeting a specific customer segment. The company’s history of acquisitions has played a significant role in its growth, including the acquisition of the Mitchum line of deodorants in 1970 and the major acquisition of Elizabeth Arden in 2016 for $870 million, expanding its portfolio in the fragrance and skincare categories.

The company has faced challenges, including financial restructuring, but has continued to evolve and adapt to the changing beauty landscape. Revlon manufactures and markets its products under several well-known brand names, such as Revlon, Almay, SinfulColors, and Mitchum, offering a wide range of products from lip care to skincare and fragrances.

Financial Performance :

Consolidated net sales in the year ended December 31, 2022, were $1,980.4 million, a $98.3 million decrease, or 4.7%, compared to $2,078.7 million in the year ended December 31, 2021.
The operating income was $79.9 million in 2022, compared to $103.2 million in the prior year.

Here is the SWOT analysis for Revlon

A SWOT analysis is a strategic planning tool used to evaluate the Strengths, Weaknesses, Opportunities, and Threats of a business, project, or individual. It involves identifying the internal and external factors that can affect a venture’s success or failure and analyzing them to develop a strategic plan. In this article, we do a SWOT Analysis of Revlon.

SWOT Analysis: Meaning, Importance, and Examples

Brand Recognition and Heritage : Revlon is one of the most recognized names in the beauty industry, with a long history dating back to 1932. This legacy has built a strong brand equity, translating into consumer trust and loyalty.
Diverse Product Portfolio : The company offers various products across several beauty categories, including cosmetics, skincare, hair care, and fragrances. This diversity allows Revlon to cater to a broad spectrum of consumer needs and preferences.
Global Presence : Revlon products are sold in more than 150 countries, enabling the company to tap into various markets and consumer bases worldwide. This extensive distribution network supports global brand visibility and revenue generation.
Innovation and Research : Revlon is known for its commitment to innovation. It continually introduces new and improved products to meet consumers’ evolving needs. This focus on innovation helps the brand stay relevant and competitive in a fast-paced industry.
Strategic Acquisitions : Over the years, Revlon has acquired other companies and brands to expand its product offerings and market reach. Notable acquisitions include the purchase of Elizabeth Arden, which significantly enhanced Revlon’s skincare and fragrance segments.
Marketing and Advertising : Revlon has historically invested in high-profile advertising campaigns featuring renowned models and celebrities, effectively building and maintaining the brand’s glamorous image. This robust marketing approach continues to enhance brand recognition and consumer appeal.
Ethical and Responsible Operations : Revlon strongly emphasizes operating responsibly. It is committed to ethical business practices, sustainability, and social responsibility. These values resonate with today’s consumers, who increasingly prefer brands that align with their beliefs and values.
Market Competition : The beauty industry is highly competitive, with numerous brands vying for market share. Revlon competes with established giants and emerging brands, making maintaining and growing its market position challenging.
Dependency on Brick-and-Mortar Channels : While Revlon has an online presence, its sales are significantly dependent on traditional retail channels, which have been impacted by the rise of e-commerce and changing consumer shopping habits.
Product Recalls and Quality Issues : Like any company in the consumer goods sector, Revlon is susceptible to product recalls and quality issues, damaging its reputation and leading to financial losses.
Slow Response to Market Trends : The rapidly evolving beauty industry requires brands to quickly adapt to new trends, such as the growing demand for clean, vegan, and cruelty-free products. Revlon’s response to some of these trends has been slower than its more agile competitors.

Opportunities

Digital Transformation : Increasing its digital footprint through e-commerce and digital marketing can help Revlon reach a broader audience and enhance customer engagement. Leveraging online platforms for direct-to-consumer sales is particularly relevant in today’s increasingly digital market.
Sustainability Initiatives : Consumers are becoming more environmentally conscious, and there’s a growing demand for sustainable and eco-friendly products. Revlon can capitalize on this trend by developing sustainable products, adopting eco-friendly packaging, and implementing sustainable practices.
Expansion into Emerging Markets : Revlon has the opportunity to expand its presence further in emerging markets, where the beauty and personal care sectors are experiencing rapid growth. Tailoring products and marketing strategies to meet these markets’ unique needs and preferences can drive growth.
Innovation in Product Development : Continuing to innovate in product formulation and packaging can help Revlon meet consumers’ evolving needs. This includes tapping into trends such as clean beauty, natural ingredients, and personalized beauty products.
Brand Collaborations and Partnerships : Collaborating with influencers, celebrities, and other brands can help Revlon reach new audiences and refresh its brand image. Limited edition collections and partnerships can generate buzz and attract younger consumers.
Enhancing Customer Experience : Investing in technologies like augmented reality (AR) for virtual try-ons and personalized beauty consultations can enhance the online shopping experience, making it more interactive and personalized for consumers.
Diversification into Adjacent Categories : Exploring opportunities in adjacent categories, such as wellness and health-related beauty products, can diversify Revlon’s portfolio and tap into new consumer segments.

Intense Competition : The beauty and cosmetics industry is highly competitive, with numerous brands vying for market share. Revlon competes against both established multinational corporations and emerging indie brands, each constantly innovating and expanding its product lines.
Changing Consumer Preferences : Trends in the beauty industry are continually evolving, with a growing demand for clean, sustainable, and cruelty-free products. Revlon must adapt to these changing preferences to remain relevant and appealing to consumers.
Economic Uncertainties : Global economic conditions, such as recessions, inflation, and changes in consumer spending habits, can negatively affect sales. Economic downturns can lead to reduced discretionary spending on beauty products.
Regulatory Challenges : The beauty industry is subject to stringent regulations regarding product ingredients, packaging, and marketing claims. Regulation changes could impact Revlon’s product formulations, labeling, and sales, particularly in key markets.
Supply Chain Disruptions : Global events like pandemics, natural disasters, or geopolitical tensions can disrupt supply chains, affecting the availability of raw materials and the production and distribution of products. This can lead to delays, increased costs, and inventory challenges.
Digital Transformation Pressures : The rise of e-commerce and digital marketing requires Revlon to continually invest in digital platforms and technologies to engage with consumers online effectively. Keeping up with digital transformation trends can be resource-intensive.
Reputation Risks : In the age of social media, negative reviews or controversies can quickly damage a brand’s reputation. Revlon must manage its brand image carefully to maintain consumer trust and loyalty.

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Strategic Management: A Competitive Advantage Approach, Concepts and Cases, 16/e by Fred R. David, Forest R. David

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Revlon, Inc., 2015

www.revlon.com , REV

Headquartered in New York City, Revlon is a large beauty and personal care products company and is a subsidiary of MacAndrews & Forbes Holding Inc. Popular Revlon products include lipsticks, skin care products, deodorant, blush, makeup, hair and nail products, and much more, marketed under such brands as Almay, SinfulColors, Pure Ice, Revlon ColorSilk, Charlie, Jean Naté, Mitchum, Gatineau, and Ultima II. Revlon sells products worldwide through its sales force, sales representatives, and independent distributors, and licenses its trademarks to select manufacturers for complementary beauty-related products and accessories. For the quarter that ended June 30, 2015, Revlon’s revenues were $482 million, up from ...

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ANALYSIS: Why Revlon filing for bankruptcy signals the need for corporate innovation and foresight

Revlon, the American cosmetics and skin care giant, filed for Chapter 11 bankruptcy protection citing supply chain issues and rising inflation last June 15, 2022.

Revlon’s Chief Restructuring Officer Robert Caruso wrote in a court filing that the company’s revenues decreased due to the global supply chain disruption. This problem made it difficult for the cosmetics firm to produce its products to meet consumer demand.

Caruso wrote that the company is facing the brunt of inflation which increased its manufacturing costs.

Furthermore, Caruso also wrote that Revlon faced intensified competition for the raw materials they need to manufacture its products.

Revlon seems to be the latest corporate giant to face difficulties due to the disruptions brought by today’s volatile, uncertain, complex, and ambiguous (VUCA) world.

Tackling global crises through corporate foresight

Revlon is not the lone company that was disrupted by the global supply chain crisis. Companies internationally faced port congestion, raw material shortages, and rising transportation costs as a result of this disruption.

For example, Ikea had to increase its prices by 9% on average in 2022 to account for increased transportation and raw material costs brought by this disruption, CNN reported .

Interestingly, however, many organizations in the past have weathered and even experienced unprecedented growth amid global crises. Those that survived global crises usually have a strong corporate foresight team within their organization.

Corporate foresight is the use of strategic foresight and futures thinking to methodically identify, anticipate and prepare for the changes in the future. Corporations around the world used this to analyze today’s world to create scenarios of how the future might unfold.

By doing this, organizations are in a better position to respond to future disruptions that may affect their business such as the Covid-19 pandemic and the supply chain crisis.

Shell , an international energy conglomerate, is one good example of a company that successfully weathered a crisis with the help of corporate foresight. Its corporate foresight team was able to foresee and prepare the company for the 1973 oil crisis. This helped the company weather the crisis and recover more rapidly than its competitors.

However, corporate foresight is often not enough to respond to disruptions and competitors. This needs to be combined with a strong corporate innovation practice.

Challenging competing startups through corporate innovation

While crises may strike organizations at any time, businesses also have to contend with new and more nimble competitors such as startups vying to capture their market share.

Revlon, for example, faced fierce competition from popular celebrity-backed cosmetic startups such as Kylie Jenner’s Kylie Cosmetics and Rihanna’s Fenty Beauty. These startups edged out Revlon in this highly competitive consumer market, Reuters reported .

One way organizations can ensure that their businesses are not disrupted by the competition is through corporate innovation .

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Another way for organizations to innovate is by investing in promising startups. Here, corporations identify startups they can invest in which have the potential for significant returns on their investment.

There are many more ways to innovate to grow your organization and cement your place as the market leader in your industry.

Schedule a strategy session with our team and see how you can prepare for future crises through corporate innovation and corporate foresight.

Revlon Inc. – Failure Case Study

All the vital news, analysis, and commentary curated by our industry experts.

Published: September 21, 2022 Report Code: GDCS220024SF-ST

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Report Overview
Key Players

This report is part of GlobalData's Success and Failures case study series which explores the reasons underpinning success or failure, and key learnings. Recognized widely across the globe, Revlon, Inc. has established a strong presence in the cosmetics and toiletries industry. This study looks at the failure of Revlon, Inc. in its current form and what key the future holds for the firm after its bankruptcy filing.

Founded in 1932, Revlon, Inc. is a leading global beauty company with a broad portfolio of well-established brands which operate predominantly across the make-up, haircare, fragrance, and skincare sectors. Revlon, Inc. was experiencing financial challenges which only intensified due to the pandemic as it faced supply chain disruption and rising raw material costs. It cited these factors as inhibiting its ability to meet customer orders. Changes in consumer behavior have also compounded the logistical challenges which persist due to the pandemic. Revlon, Inc. has the option to overhaul its business operations but the prospect of a buyout or the acquisition of some of its brands by a third party is also possible.

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What Was the Real Story with the Revlon S/4HANA Failure?

Table of Contents: Select a Link to be Taken to That Section

Executive Summary

Revlon failed at a S/4HANA implementation back in Feb of 2017, and this failure came to light in a quarterly call in March of 2019.
We cover this failed S/4HANA case study.

Introduction (Skip if You Watched the Video)

We have been warning people about S/4HANA’s implementation problems since early 2017. We covered this in the article Why Did SAP Fake S/4HANA Maturity So Aggressively? and explained how SAP and consulting firms had significantly misrepresented S/4HANA’s maturity. We covered how senior members of SAP consulting firms misrepresented S/4HANA in a separate article titled Analysis of Mark Chalfen’s Article on S/4HANA Maturity . And Revlon is now a perfect case study of these problems that we predicted. You will learn how these software maturity problems likely ended up biting a major company and how this company appeared to hide these investors’ problems.

Our References for This Article

If you want to see our references for this article and other related Brightwork articles, see this link.

Notice of Lack of Financial Bias: We have no financial ties to SAP or any other entity mentioned in this article.

This is published by a research entity, not some lowbrow entity that is part of the SAP ecosystem.
Second, no one paid for this article to be written, and it is not pretending to inform you while being rigged to sell you software or consulting services. Unlike nearly every other article you will find from Google on this topic, it has had no input from any company's marketing or sales department. As you are reading this article, consider how rare this is. The vast majority of information on the Internet on SAP is provided by SAP, which is filled with false claims and sleazy consulting companies and SAP consultants who will tell any lie for personal benefit. Furthermore, SAP pays off all IT analysts -- who have the same concern for accuracy as SAP. Not one of these entities will disclose their pro-SAP financial bias to their readers.

How S/4HANA Operationally and Financially Damaged Revlon

Notice this quote from Revlon’s Q4 2018 call (which occurred in May of 2019).

As highlighted previously, the company has identified a material weakness in it’s internal controls, primarily related to the lack of design and makings of effective controls in connection with the implementation of its new SAP ERP system in the U.S. We have already developed and begun to implement a remediation plan to address this finding and we’ll continue to enhance our internal control environment as we move forward. The company expects that this matter will not result in any changes to its financial results.”

The company’s problems with the ERP system surfaced shortly after Revlon launched S/4HANA in February 2017.

The 2017 Statement from Revlon About the S/4HANA Implementation

Such systems are designed to integrate everything from a company’s inventory of manufacturing goods to its customer relationships. In its annual report for the fiscal year 2017, Revlon revealed the difficulties at its Oxford, North Carolina, manufacturing facility:

[T]he Company launched the new ERP system in the U.S., which caused its Oxford, N.C. manufacturing facility to experience service level disruptions that have impacted the Company’s ability to manufacture certain quantities of finished goods and fulfill shipments to several large retail customers in the U.S. The Company cannot provide assurances that it will remedy the ERP systems issues in time to fully recover these sales and/or that the ERP implementation will not continue to disrupt the Company’s operations and its ability to fulfill customer orders.

The disruptions have continued, apparently, and Revlon’s warning a year ago has come true:

To the extent that these disruptions occur in larger magnitudes or continue to persist over time, it could negatively impact the Company’s competitive position and its relationships with its customers and thus could have a material adverse effect on the Company’s business, prospects, results of operations, financial condition and/or cash flows.

The Remediation Plan?

I am unsure what the remediation plan will be for S/4HANA. However, any ERP system that has been taken live running the business in production will cause significant issues, according to the quotes from ComputerWeekly.

She attributed $54m of direct cost to remediating the SAP disruption.

The actual number will most likely be a good deal higher than this. Budgets for such remediation nearly always underestimate the total costs.

However, Revlon also stated the following.

There is not a plan for future implementations at this point.

This sounds like the project has failed. And this caused investors to note an inconsistency.

According to the class action lawsuit, the company made false or misleading statements and failed to disclose the extent of its issues with the SAP implementation .

How Revlon Could Have Avoided the S/4HANA Failure

It turns out it could have done so very quickly.

We will cover something about this story that the mainline IT media entities, consulting firms, and IT analysts will not touch — and the reason is that nearly every entity that reports on SAP is also financially connected to SAP. And they do not declare their financial connections to SAP, appearing to be independent.

Revlon could have stayed entirely out of this by not listening to their consulting firm.
This software was implemented in 2017, so the project would have begun in 2016. This was when the chance of taking S/4HANA live in a production setting would have been close to zero . However, Revlon was encouraged to implement this software by both SAP and the consulting partner. Since S/4HANA was first introduced, SAP and the consulting ecosystem have made every attempt to hide the maturity of S/4HANA from potential customers. As we covered in the article Why Did SAP Fake S/4HANA Maturity So Aggressively?

Let us review a sample of the public statements around S/4HANA by some of the largest SAP implementation firms.

Getting Inaccurate Information About S/4HANA from SAP Consulting Firm’s Website

The deloitte website on s/4hana.

Deloitte’s website, as is the material on all significant SAP consulting companies’ websites, leaves its maturity.

Consulting companies want S/4HANA implementations, which causes them to leave out the application’s maturity entirely. Other claims untethered to the maturity of S/4HANA are also highly dubious.

Let us look at two examples:

S/4HANA “is central to many major digital transformation projects today.” The number of ongoing projects depends on what “many means,” but it is not widely implemented. Secondly, the number of S/4HANA projects that go live is deficient. Is the system’s success measured by how many ongoing projects (and billing hours), or what percentage goes live? For Deloitte, the measure is naturally the number billing.
How is S/4HANA a “game-changer” if it has no new functionality over ECC, its performance is not very good due to the mismatch between HANA and TP , and Fiori is rarely implemented?

The CapGemini Website

Many of the statements by the SAP consulting firms around S/4HANA are nonsensical. Companies that implement S/4HANA are replacing previous legacy or ERP systems; they are not moving from paper to digital and are therefore not “digitizing” their business. The digitization process for most companies that can afford a Capgemini implementation of S/4HANA would have most likely occurred in the 1970s or 1980s.

We covered the illogical term of digital transformation in the article The Problem with the Term Digital Transformation .

In working with several SAP consulting firms and reviewing others’ material, it is clear that they have little interest in either knowing or communicating the maturity issues with S/4HANA. This is reminiscent of a quote from Dave McComb’s book Software Wasteland.

One of the main problems with our industry is that there is far more money to be made by being incompetent than there is for being competent. There are still far too many contractors who make far more money not implementing systems that there are contractors that can implement productively.

Dave McComb’s analysis is that consulting companies may even prefer if the applications they implement are immature. They are paid hourly, so whether the project goes live does not matter. If they can sell the project, they can get paid even more as the innumerable problems require the client to keep the consulting company around even longer. And this translates to more billing hours.

The consulting companies have some maturity issues they would like to hide from you. This is why they can’t be listened to — the only thing they look out for is their interests. They are happy to recommend inappropriate applications and create miniature disasters for their clients if they meet their financial needs.

If you talk to senior members of consulting firms in private (as I have), they will not acknowledge any responsibility except to maximize the consulting companies’ revenues. The approach is that they present whatever they want, and the buyer must do their research to fact-check the consulting firm.

If we look at the legal defense of WiPro versus National Grid, WiPro stated that while the references they provided were fake, it was National Grid’s responsibility to check them. WiPro had no obligation to provide accurate references, as we covered in the article How to Understand Wipro’s Position on Lying.

The Problems with Financial Bias in the S/4HANA Decision

Financial Bias : Revlon, based on financially biased information and perhaps financial bias within IT, implemented an ERP system with the highest risk associated with any ERP system we track.
Financial Bias of IT and Consulting Firms: The financial bias of SAP consulting companies is indisputable, but IT departments can appear to act as agents of vendors rather than of the companies they “work for.” This can be due to senior decision-makers revolving door between implementing companies and consulting firms and the idea that implementing or overseeing the implementation of the most recent SAP applications is right for their career.
The Established Pattern with S/4HANA Implementations : The Revlon failure fits into a long-term pattern of S/4HANA project failures being suppressed. This is covered in the article How S/4HANA Cost Overruns and Failures are Suppressed?

Revlon’s Failure with S/4HANA Had “Nothing to Do with S/4HANA’s Maturity?”

Several commenters specializing in ERP failure analysis have stated that Revlon’s problems with S/4HANA had nothing to do with S/4HANA’s maturity issues .

As we are the only entity that we are aware of that honestly tracks S/4HANA maturity (see our critique of ASUG’s coverage in How Accurate Was ASUG on its S/HANA Poll? ). As these individuals do not work on S/4HANA implementations, seeing how they know this is challenging . It is virtually impossible to find any industry commenters who will point out the maturity of products that fail as they look to vendors for payment or promotion. Therefore, without performing any research, they will offer the view that the application is reliable.

The previous version of S/4HANA, ECC, was mature — but this says nothing about S/4HANA. S/4HANA made many changes, particularly to the application’s technical backdrop, significantly reducing its implement-ability.

This is not to say that Revlon’s implementation management was not problematic (we don’t know if it was or wasn’t) or if the implementation company did a poor job (they usually do). However, if the product itself is not ready to be implemented, it is nonsensical to remove this issue from consideration as a prime contributor to the failure.

The S/4HANA Market is Filled with Entities Providing False Information About S/4HANA Readiness for Implementation

Virtually every SAP consultant that publishes or comments underestimates the difficulty with implementing S/4HANA and misrepresents S/4HANA to prospects. We have discussed whether these consulting companies are implementing projects they know will fail or have the highest probability of failure but don’t care. We covered this in the article, Is it Right to Lead Clients into SAP Software Failure?

If you are a customer interested in implementing S/4HANA and want the worst possible advice, don’t forget to check our SAP Deception and Corruption Quadrant. Any of these firms will lie to you about S/4HANA, but they are all SAP Certified partners, so they are your approved sources, and you can choose from any of them. All of them will rob you. But there is a choice. As my previous clients have said…

“We know the consulting firm is lying to us, but they have been recommended to us by SAP.”

Many characters like “Grima Wormtongue” consulting firms provide false information necessary for a Gandalf-type figure to bring technical accuracy and reality to IT departments.

The Revlon S/4HANA case study provides valuable insight into how implementation failures are covered up.

It almost appears the release of information by Revlon was 1/2 unplanned. The failure was not presented as a failure, but rather, the failure was presented within the context of a ready-made plan to remediate failure. This shows the management as totally in “control of the situation,” however, a more accurate depiction is they have been played for fools by SAP and their consulting partner. We can be pretty confident that if we were to cultivate contacts with those who know Revlon, they would tell us a very different story than what was said in the quarterly call. The amount of the remediation costs combined with the statements indicate that S/4HANA was cutover into production prematurely and that Revlon has had long-term problems with S/4HANA.

Revlon said several inconsistent things: first, they lost money because SAP prevented them from fulfilling orders, and then that SAP was not stopping them from performing production. If Revlon is restricted in achieving, it is impacting production if it isn’t stopping production. Furthermore, an ERP can’t “stop” production. It does not have a body that can turn the production line “off.” Therefore, the statement here is misleading. The ERP system can order incorrect amounts of materials and, generally, create havoc in the manufacturing process.

Furthermore, Revlon would naturally be incurring all other types of internal costs. When National Grid failed in its SAP implementation (not S/4HANA), National Grid claimed the following remediation costs in its lawsuit against Wipro.

The problems were so profound that the cleanup took more than two years to complete with a calculated cost of $585M, more than $150% of the cost of implementation. – How National Grid’s SAP Implementation Damaged a Company

The investor lawsuit was initiated because some investors felt they had not been informed of the SAP implementation problems.

Revlon is unified with SAP in hiding the failure of this S/4HANA implementation from investors. We have added Revlon to our current research into S/4HANA implementations.

Sunday, August 04, 2019

Labels: Case Studies , Project Failure

Case Study 6: How Revlon Got Sued by Its Own Shareholders Because of a Failed Sap Implementation

Case Study: How Revlon got sued by its shareholders because of a failed SAP implementation

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Modi-Revlon

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Modi-revlon description.

The head of the Indian subsidiary of cosmetics firm Revlon faces a crucial turnaround situation for the company. After a high-profile product launch, sales were very disappointing and Revlon was trying to decide whether it should pull out of India. The Indian majority partners in the joint venture were determined to save the company by reexamining the price-value equation and the need for continuous product innovation tailored to the local consumer needs.

Case Description Modi-Revlon

Strategic managment tools used in case study analysis of modi-revlon, step 1. problem identification in modi-revlon case study, step 2. external environment analysis - pestel / pest / step analysis of modi-revlon case study, step 3. industry specific / porter five forces analysis of modi-revlon case study, step 4. evaluating alternatives / swot analysis of modi-revlon case study, step 5. porter value chain analysis / vrio / vrin analysis modi-revlon case study, step 6. recommendations modi-revlon case study, step 7. basis of recommendations for modi-revlon case study, quality & on time delivery.

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Case Analysis of Modi-Revlon

Modi-Revlon is a Harvard Business (HBR) Case Study on Sales & Marketing , Texas Business School provides HBR case study assignment help for just $9. Texas Business School(TBS) case study solution is based on HBR Case Study Method framework, TBS expertise & global insights. Modi-Revlon is designed and drafted in a manner to allow the HBR case study reader to analyze a real-world problem by putting reader into the position of the decision maker. Modi-Revlon case study will help professionals, MBA, EMBA, and leaders to develop a broad and clear understanding of casecategory challenges. Modi-Revlon will also provide insight into areas such as – wordlist , strategy, leadership, sales and marketing, and negotiations.

Case Study Solutions Background Work

Modi-Revlon case study solution is focused on solving the strategic and operational challenges the protagonist of the case is facing. The challenges involve – evaluation of strategic options, key role of Sales & Marketing, leadership qualities of the protagonist, and dynamics of the external environment. The challenge in front of the protagonist, of Modi-Revlon, is to not only build a competitive position of the organization but also to sustain it over a period of time.

Strategic Management Tools Used in Case Study Solution

The Modi-Revlon case study solution requires the MBA, EMBA, executive, professional to have a deep understanding of various strategic management tools such as SWOT Analysis, PESTEL Analysis / PEST Analysis / STEP Analysis, Porter Five Forces Analysis, Go To Market Strategy, BCG Matrix Analysis, Porter Value Chain Analysis, Ansoff Matrix Analysis, VRIO / VRIN and Marketing Mix Analysis.

Texas Business School Approach to Sales & Marketing Solutions

In the Texas Business School, Modi-Revlon case study solution – following strategic tools are used - SWOT Analysis, PESTEL Analysis / PEST Analysis / STEP Analysis, Porter Five Forces Analysis, Go To Market Strategy, BCG Matrix Analysis, Porter Value Chain Analysis, Ansoff Matrix Analysis, VRIO / VRIN and Marketing Mix Analysis. We have additionally used the concept of supply chain management and leadership framework to build a comprehensive case study solution for the case – Modi-Revlon

Step 1 – Problem Identification of Modi-Revlon - Harvard Business School Case Study

The first step to solve HBR Modi-Revlon case study solution is to identify the problem present in the case. The problem statement of the case is provided in the beginning of the case where the protagonist is contemplating various options in the face of numerous challenges that Revlon Modi is facing right now. Even though the problem statement is essentially – “Sales & Marketing” challenge but it has impacted by others factors such as communication in the organization, uncertainty in the external environment, leadership in Revlon Modi, style of leadership and organization structure, marketing and sales, organizational behavior, strategy, internal politics, stakeholders priorities and more.

Step 2 – External Environment Analysis

Texas Business School approach of case study analysis – Conclusion, Reasons, Evidences - provides a framework to analyze every HBR case study. It requires conducting robust external environmental analysis to decipher evidences for the reasons presented in the Modi-Revlon. The external environment analysis of Modi-Revlon will ensure that we are keeping a tab on the macro-environment factors that are directly and indirectly impacting the business of the firm.

What is PESTEL Analysis? Briefly Explained

PESTEL stands for political, economic, social, technological, environmental and legal factors that impact the external environment of firm in Modi-Revlon case study. PESTEL analysis of " Modi-Revlon" can help us understand why the organization is performing badly, what are the factors in the external environment that are impacting the performance of the organization, and how the organization can either manage or mitigate the impact of these external factors.

How to do PESTEL / PEST / STEP Analysis? What are the components of PESTEL Analysis?

As mentioned above PESTEL Analysis has six elements – political, economic, social, technological, environmental, and legal. All the six elements are explained in context with Modi-Revlon macro-environment and how it impacts the businesses of the firm.

How to do PESTEL Analysis for Modi-Revlon

To do comprehensive PESTEL analysis of case study – Modi-Revlon , we have researched numerous components under the six factors of PESTEL analysis.

Political Factors that Impact Modi-Revlon

Political factors impact seven key decision making areas – economic environment, socio-cultural environment, rate of innovation & investment in research & development, environmental laws, legal requirements, and acceptance of new technologies.

Government policies have significant impact on the business environment of any country. The firm in “ Modi-Revlon ” needs to navigate these policy decisions to create either an edge for itself or reduce the negative impact of the policy as far as possible.

Data safety laws – The countries in which Revlon Modi is operating, firms are required to store customer data within the premises of the country. Revlon Modi needs to restructure its IT policies to accommodate these changes. In the EU countries, firms are required to make special provision for privacy issues and other laws.

Competition Regulations – Numerous countries have strong competition laws both regarding the monopoly conditions and day to day fair business practices. Modi-Revlon has numerous instances where the competition regulations aspects can be scrutinized.

Import restrictions on products – Before entering the new market, Revlon Modi in case study Modi-Revlon" should look into the import restrictions that may be present in the prospective market.

Export restrictions on products – Apart from direct product export restrictions in field of technology and agriculture, a number of countries also have capital controls. Revlon Modi in case study “ Modi-Revlon ” should look into these export restrictions policies.

Foreign Direct Investment Policies – Government policies favors local companies over international policies, Revlon Modi in case study “ Modi-Revlon ” should understand in minute details regarding the Foreign Direct Investment policies of the prospective market.

Corporate Taxes – The rate of taxes is often used by governments to lure foreign direct investments or increase domestic investment in a certain sector. Corporate taxation can be divided into two categories – taxes on profits and taxes on operations. Taxes on profits number is important for companies that already have a sustainable business model, while taxes on operations is far more significant for companies that are looking to set up new plants or operations.

Tariffs – Chekout how much tariffs the firm needs to pay in the “ Modi-Revlon ” case study. The level of tariffs will determine the viability of the business model that the firm is contemplating. If the tariffs are high then it will be extremely difficult to compete with the local competitors. But if the tariffs are between 5-10% then Revlon Modi can compete against other competitors.

Research and Development Subsidies and Policies – Governments often provide tax breaks and other incentives for companies to innovate in various sectors of priority. Managers at Modi-Revlon case study have to assess whether their business can benefit from such government assistance and subsidies.

Consumer protection – Different countries have different consumer protection laws. Managers need to clarify not only the consumer protection laws in advance but also legal implications if the firm fails to meet any of them.

Political System and Its Implications – Different political systems have different approach to free market and entrepreneurship. Managers need to assess these factors even before entering the market.

Freedom of Press is critical for fair trade and transparency. Countries where freedom of press is not prevalent there are high chances of both political and commercial corruption.

Corruption level – Revlon Modi needs to assess the level of corruptions both at the official level and at the market level, even before entering a new market. To tackle the menace of corruption – a firm should have a clear SOP that provides managers at each level what to do when they encounter instances of either systematic corruption or bureaucrats looking to take bribes from the firm.

Independence of judiciary – It is critical for fair business practices. If a country doesn’t have independent judiciary then there is no point entry into such a country for business.

Government attitude towards trade unions – Different political systems and government have different attitude towards trade unions and collective bargaining. The firm needs to assess – its comfort dealing with the unions and regulations regarding unions in a given market or industry. If both are on the same page then it makes sense to enter, otherwise it doesn’t.

Economic Factors that Impact Modi-Revlon

Social factors that impact modi-revlon, technological factors that impact modi-revlon, environmental factors that impact modi-revlon, legal factors that impact modi-revlon, step 3 – industry specific analysis, what is porter five forces analysis, step 4 – swot analysis / internal environment analysis, step 5 – porter value chain / vrio / vrin analysis, step 6 – evaluating alternatives & recommendations, step 7 – basis for recommendations, references :: modi-revlon case study solution.

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SWOT Analysis of Revlon

February 27, 2019 | By Hitesh Bhasin | Filed Under: SWOT of Brands

Revlon is an American cosmetics and makeup company that is headquartered in New York. The company which is a specialist in makeup has a wide range of products like foundation, concealer, compact, face powder, blush, eye shadow, eye pencil, eyebrow pencil, mascara, eyeliner, nail paint, lipstick and lip gloss amongst others.

Since the company caters to customers across the world. Revlon has makeup that suits multiple skin types and complexions. In addition to makeup essentials, Revlon also has the presence in the hair care segment where they sell shampoos, conditioners, hair serums, hair color, and smoothness. The company registered an annual turnover of 2.3 billion USD and is amongst the top cosmetics and makeup companies in the world.

Revlon was started in the year 1932 as a company that manufactures nail polishes. Their products range was wider and the company gave more choices of shades than their competition thereby capturing the attention of the customer. Since then there has been no looking back for Revlon who continued to spell success in every move.

Table of Contents

Strengths in the SWOT analysis of Revlon

The following are the strengths of Revlon:

History of the brand : Revlon which started off by selling nail polish through a unique marketing strategy emerged as a multi-million dollar business in just a matter of six years. The spirit of innovation and deep understanding of the customer which had helped them reach success then is still deeply integrated into the culture of the company which is what is helping the company even today.
High customer engagement : Revlon has always tried to interact on a regular basis with their customer taking feedback and improving on their products. In addition to the 24-hour online helpdesk. Revlon also has Revlon woman online an online platform where their customers are encouraged to express themselves and build online personalities
Brand endorsements: Revlon has always made it a point to associate with prominent celebrities who epitomize beauty. Some of their brand ambassadors include Halle Berry, Emma Stone, Ashley Graham, Raquel Zimmerman etc.
Advertising strategy: Revlon has always followed a think global act local strategy in advertising which improves the customer connect. The content is usually global for each advertisement but the setting, the storyline and the actors in the commercials would cater to local tastes and audiences.
Commitment to charity: Revlon has always focused on giving back to societies and communities. Many of their philanthropic activities aim at empowering underprivileged women and looks at improving the health and well-being of women all over the world through free health check- ups , mobile cancer screening camps etc.

Weaknesses in the SWOT analysis of Revlon

Some of the key weaknesses of Revlon are :

Acquisition of Elizabeth Arden: Revlon acquired the cosmetics major Elizabeth Arden for a whopping sum of 420 million USD in 2016. The company registered losses after the acquisition which did not give them the required results.
Consumer Behaviours changes: The surge in specialty cosmetics stores and e- commerce stores has changed the way manner in which customers shop for cosmetics as well as their preferences. This change in consumer behavior is mandating a change of strategy especially for global companies like Revlon.
Mounting Debt: Revlon is suffering from growing losses for the past few years.The losses which have been on a steep increase has created debts to the tune of 3 billion USD.
Limited focus on younger generation: Revlon has been focusing more on an older and mature population for business. However, in the recent years, cosmetic companies have been acquiring youth brands to capture the mindshare of a younger audience something that Revlon has not been able to do successfully.

Opportunities in the SWOT analysis of Revlon

Some of the opportunities include :

Threats in the SWOT analysis of Revlon

Some of the threats include:

Competition: The main competitor of Revlon is Loreal, Estee Lauder , Mac, Sephora .
Chemical content: Customers are growing more apprehensive about the chemical content in makeup and this is making them look more at natural makeup or mineral makeup. Revlon is yet to establish a strong presence in organic and mineral makeup.

Liked this post? Check out the complete series on SWOT

SWOT Analysis of Dettol – Dettol SWOT Analysis
SWOT Analysis of Kodak – Kodak SWOT analysis
SWOT Analysis of Hindustan Unilever – HUL SWOT Analysis
SWOT Analysis of Procter and Gamble – P & G SWOT analysis (Updated 2024)
SWOT Analysis of Pepsi – PepsiCo SWOT analysis
SWOT Analysis of Youtube 2023
SWOT analysis of Whirlpool – Whirlpool SWOT analysis
SWOT Analysis of Bulgari
SWOT Analysis of AMD (Advanced Micro Devices)
SWOT Analysis of Rado

About Hitesh Bhasin

Hitesh Bhasin is the CEO of Marketing91 and has over a decade of experience in the marketing field. He is an accomplished author of thousands of insightful articles, including in-depth analyses of brands and companies. Holding an MBA in Marketing, Hitesh manages several offline ventures, where he applies all the concepts of Marketing that he writes about.

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Modi-revlon case study analysis & solution, harvard business case studies solutions - assignment help.

Modi-Revlon is a Harvard Business (HBR) Case Study on Sales & Marketing , Fern Fort University provides HBR case study assignment help for just $11. Our case solution is based on Case Study Method expertise & our global insights.

Sales & Marketing Case Study | Authors :: Rohit Deshpande, Seth M. Schulman

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[10 Steps] Case Study Analysis & Solution

Step 1 - reading up harvard business review fundamentals on the sales & marketing.

Even before you start reading a business case study just make sure that you have brushed up the Harvard Business Review (HBR) fundamentals on the Sales & Marketing. Brushing up HBR fundamentals will provide a strong base for investigative reading. Often readers scan through the business case study without having a clear map in mind. This leads to unstructured learning process resulting in missed details and at worse wrong conclusions. Reading up the HBR fundamentals helps in sketching out business case study analysis and solution roadmap even before you start reading the case study. It also provides starting ideas as fundamentals often provide insight into some of the aspects that may not be covered in the business case study itself.

Step 2 - Reading the Modi-Revlon HBR Case Study

To write an emphatic case study analysis and provide pragmatic and actionable solutions, you must have a strong grasps of the facts and the central problem of the HBR case study. Begin slowly - underline the details and sketch out the business case study description map. In some cases you will able to find the central problem in the beginning itself while in others it may be in the end in form of questions. Business case study paragraph by paragraph mapping will help you in organizing the information correctly and provide a clear guide to go back to the case study if you need further information. My case study strategy involves -

Marking out the protagonist and key players in the case study from the very start.
Drawing a motivation chart of the key players and their priorities from the case study description.
Refine the central problem the protagonist is facing in the case and how it relates to the HBR fundamentals on the topic.
Evaluate each detail in the case study in light of the HBR case study analysis core ideas.

Step 3 - Modi-Revlon Case Study Analysis

Once you are comfortable with the details and objective of the business case study proceed forward to put some details into the analysis template. You can do business case study analysis by following Fern Fort University step by step instructions -

Company history is provided in the first half of the case. You can use this history to draw a growth path and illustrate vision, mission and strategic objectives of the organization. Often history is provided in the case not only to provide a background to the problem but also provide the scope of the solution that you can write for the case study.
HBR case studies provide anecdotal instances from managers and employees in the organization to give a feel of real situation on the ground. Use these instances and opinions to mark out the organization's culture, its people priorities & inhibitions.
Make a time line of the events and issues in the case study. Time line can provide the clue for the next step in organization's journey. Time line also provides an insight into the progressive challenges the company is facing in the case study.

Step 4 - SWOT Analysis of Modi-Revlon

Once you finished the case analysis, time line of the events and other critical details. Focus on the following -

Zero down on the central problem and two to five related problems in the case study.
Do the SWOT analysis of the Modi-Revlon . SWOT analysis is a strategic tool to map out the strengths, weakness, opportunities and threats that a firm is facing.
SWOT analysis and SWOT Matrix will help you to clearly mark out - Strengths Weakness Opportunities & Threats that the organization or manager is facing in the Modi-Revlon
SWOT analysis will also provide a priority list of problem to be solved.
You can also do a weighted SWOT analysis of Modi-Revlon HBR case study.

Step 5 - Porter 5 Forces / Strategic Analysis of Industry Analysis Modi-Revlon

In our live classes we often come across business managers who pinpoint one problem in the case and build a case study analysis and solution around that singular point. Business environments are often complex and require holistic solutions. You should try to understand not only the organization but also the industry which the business operates in. Porter Five Forces is a strategic analysis tool that will help you in understanding the relative powers of the key players in the business case study and what sort of pragmatic and actionable case study solution is viable in the light of given facts.

Step 6 - PESTEL, PEST / STEP Analysis of Modi-Revlon

Another way of understanding the external environment of the firm in Modi-Revlon is to do a PESTEL - Political, Economic, Social, Technological, Environmental & Legal analysis of the environment the firm operates in. You should make a list of factors that have significant impact on the organization and factors that drive growth in the industry. You can even identify the source of firm's competitive advantage based on PESTEL analysis and Organization's Core Competencies.

Step 7 - Organizing & Prioritizing the Analysis into Modi-Revlon Case Study Solution

Once you have developed multipronged approach and work out various suggestions based on the strategic tools. The next step is organizing the solution based on the requirement of the case. You can use the following strategy to organize the findings and suggestions.

Build a corporate level strategy - organizing your findings and recommendations in a way to answer the larger strategic objective of the firm. It include using the analysis to answer the company's vision, mission and key objectives , and how your suggestions will take the company to next level in achieving those goals.
Business Unit Level Solution - The case study may put you in a position of a marketing manager of a small brand. So instead of providing recommendations for overall company you need to specify the marketing objectives of that particular brand. You have to recommend business unit level recommendations. The scope of the recommendations will be limited to the particular unit but you have to take care of the fact that your recommendations are don't directly contradict the company's overall strategy. For example you can recommend a low cost strategy but the company core competency is design differentiation.
Case study solutions can also provide recommendation for the business manager or leader described in the business case study.

Step 8 -Implementation Framework

The goal of the business case study is not only to identify problems and recommend solutions but also to provide a framework to implement those case study solutions. Implementation framework differentiates good case study solutions from great case study solutions. If you able to provide a detailed implementation framework then you have successfully achieved the following objectives -

Detailed understanding of the case,
Clarity of HBR case study fundamentals,
Analyzed case details based on those fundamentals and
Developed an ability to prioritize recommendations based on probability of their successful implementation.

Implementation framework helps in weeding out non actionable recommendations, resulting in awesome Modi-Revlon case study solution.

Step 9 - Take a Break

Once you finished the case study implementation framework. Take a small break, grab a cup of coffee or whatever you like, go for a walk or just shoot some hoops.

Step 10 - Critically Examine Modi-Revlon case study solution

After refreshing your mind, read your case study solution critically. When we are writing case study solution we often have details on our screen as well as in our head. This leads to either missing details or poor sentence structures. Once refreshed go through the case solution again - improve sentence structures and grammar, double check the numbers provided in your analysis and question your recommendations. Be very slow with this process as rushing through it leads to missing key details. Once done it is time to hit the attach button.

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4 Lessons Learned from the Revlon ERP Failure

by Bill Baumann | Dec 23, 2020

In March 2019, cosmetics giant Revlon announced that it would be late filing its annual financial report for the year prior. The reason behind the delay? The company’s year-old SAP ERP system had been riddled with issues ever since implementation. The resulting fallout cost Revlon valuable retail sales and hindered operations. Upon the announcement of the Revlon ERP failure, the company’s stock fell 6.9% in 24 hours.

So what happened with the Revlon ERP SAP implementation failure? How did this derailment happen to one of the most well-known and high-profile consumer brands in the world? Today, we’re sharing the Revlon ERP failure case study and a few of the key lessons learned from this story. We’ll also explain how your organization can avoid falling victim to the same mishaps.

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The Revlon ERP Failure Case Study

Revlon first implemented its ERP system in February 2018. Almost immediately, issues became apparent at the company’s manufacturing facility in Oxford, NC.

Among the complaints? Issues with the system hindered Revlon’s ability to successfully manufacture sufficient quantities of finished goods. This rendered them incapable of fulfilling all the orders placed by their major retail clients.

To counter these effects and keep customers satisfied, Revlon expedited product shipping. This resulted in sky-high fees, along with other unanticipated expenses.

At the same time, the Revlon ERP implementation failure created a massive financial backslide. In the fourth quarter of 2018, the company’s net loss reached $70.3 million .

4 Lessons Learned from this Revlon ERP Implementation Failure

1. risk identification is key.

A careful risk analysis can help organizations ensure they are ready to undergo an ERP project or any type of business transformation .

In Revlon’s case, issues became obvious right after the system went live in Oxford, NC. The rollout resulted in service level disruptions, which directly affected manufacturing and shipping capabilities.

In an announcement, Revlon explained that if these disruptions persisted, they could affect their competitive position. They could also impact their customer relationships, prospects, financial condition and cash flow.

A thorough risk assessment could have prepared the organization for such setbacks. There are inherent risks to any ERP implementation and expecting to go live without any issues is unrealistic.

This assessment can help organizations not only identify possible risks but also quantify them and take steps to mitigate them. This way, they can ensure that go-live doesn’t negatively affect their operations.

2. Design and Controls are Essential

Revlon cited that one of the issues behind its ERP failure was a lack of design. As a result, the company experienced “material weaknesses” in its internal controls.

To minimize the risk of material operational disruption, it’s important to take a close look at your business processes. You may even need to improve them as required to be more efficient.

When improving your processes, be sure to invest in organizational change management to ensure that everyone understands and embraces the changes. This guide can help you understand the basics of change management.

Once you’ve defined your future state and prepared your employees, you should ensure that the implementation team understands these requirements so they can properly configure and test the software.

3. Organizational Instability can Derail an ERP Project

There might not be a perfect time to roll out new software. Still, this certainly isn’t an effort to undertake when your organization is already on shaky ground.

It might seem that an ERP project can serve as a last-ditch effort to rectify instability. However, it’s more likely that the new system will only exacerbate underlying issues.

Before beginning its project in 2018, Revlon was already experiencing operational and financial issues . Most of these were related to its 2016 acquisition of the Elizabeth Arden brand. After the restructuring, Revlon struggled to integrate the disparate processes into one cohesive unit.

Before starting a major project, the brand should have taken the time to resolve these inefficiencies. Doing so could have created a stronger foundation that may not have quaked so easily under the pressures of an inefficient rollout.

Has your company recently experienced a major change? If there are outstanding issues that need to be resolved, we recommend resolving them before investing in new ERP software .

4. ROI is a Critical Consideration

Any time you implement new technology, it’s critical to calculate the anticipated return on investment. Even the most promising solution could put your company in the red.

Revlon experienced this reality as its SAP ERP project resulted in a negative ROI. This led to lost sales that it could never fully recover. These financial setbacks can be devastating, and they’re more common than you might realize.

As their bottom line gave out, Revlon’s customer service levels plummeted. Operational controls broke down, and production issues skyrocketed. Employees became laser-focused on digging the company out.

In return, other company priorities fell by the wayside, including essential reporting and filing requirements.

The lesson learned? A solid financial return on your investment is a must. You should be able to project this result before implementation. Often, this will require developing ERP benefits realization plan. While this effort can take time, it’s the link that can help your company stay focused on achieving organizational goals.

Avoid These ERP Mistakes in Your Own Project

The Revlon ERP SAP failure was a major public misstep for the brand that underscored the importance of ERP implementation best practices that are all too easy to overlook.

From risk identification to benefits realization planning, many factors contribute to a successful implementation. You can learn from Revlon’s red flags and avoid the same issues in your own project.

Our ERP consultants can leverage their ERP expert witness experience to ensure your project goes off without a hitch. Contact us below to request a free consultation.

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Machine Learning and image analysis towards improved energy management in Industry 4.0: a practical case study on quality control

Original Article
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Published: 13 May 2024
Volume 17 , article number 48 , ( 2024 )

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Mattia Casini 1 ,
Paolo De Angelis 1 ,
Marco Porrati 2 ,
Paolo Vigo 1 ,
Matteo Fasano 1 ,
Eliodoro Chiavazzo 1 &
Luca Bergamasco ORCID: orcid.org/0000-0001-6130-9544 1

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With the advent of Industry 4.0, Artificial Intelligence (AI) has created a favorable environment for the digitalization of manufacturing and processing, helping industries to automate and optimize operations. In this work, we focus on a practical case study of a brake caliper quality control operation, which is usually accomplished by human inspection and requires a dedicated handling system, with a slow production rate and thus inefficient energy usage. We report on a developed Machine Learning (ML) methodology, based on Deep Convolutional Neural Networks (D-CNNs), to automatically extract information from images, to automate the process. A complete workflow has been developed on the target industrial test case. In order to find the best compromise between accuracy and computational demand of the model, several D-CNNs architectures have been tested. The results show that, a judicious choice of the ML model with a proper training, allows a fast and accurate quality control; thus, the proposed workflow could be implemented for an ML-powered version of the considered problem. This would eventually enable a better management of the available resources, in terms of time consumption and energy usage.

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Introduction

An efficient use of energy resources in industry is key for a sustainable future (Bilgen, 2014 ; Ocampo-Martinez et al., 2019 ). The advent of Industry 4.0, and of Artificial Intelligence, have created a favorable context for the digitalisation of manufacturing processes. In this view, Machine Learning (ML) techniques have the potential for assisting industries in a better and smart usage of the available data, helping to automate and improve operations (Narciso & Martins, 2020 ; Mazzei & Ramjattan, 2022 ). For example, ML tools can be used to analyze sensor data from industrial equipment for predictive maintenance (Carvalho et al., 2019 ; Dalzochio et al., 2020 ), which allows identification of potential failures in advance, and thus to a better planning of maintenance operations with reduced downtime. Similarly, energy consumption optimization (Shen et al., 2020 ; Qin et al., 2020 ) can be achieved via ML-enabled analysis of available consumption data, with consequent adjustments of the operating parameters, schedules, or configurations to minimize energy consumption while maintaining an optimal production efficiency. Energy consumption forecast (Liu et al., 2019 ; Zhang et al., 2018 ) can also be improved, especially in industrial plants relying on renewable energy sources (Bologna et al., 2020 ; Ismail et al., 2021 ), by analysis of historical data on weather patterns and forecast, to optimize the usage of energy resources, avoid energy peaks, and leverage alternative energy sources or storage systems (Li & Zheng, 2016 ; Ribezzo et al., 2022 ; Fasano et al., 2019 ; Trezza et al., 2022 ; Mishra et al., 2023 ). Finally, ML tools can also serve for fault or anomaly detection (Angelopoulos et al., 2019 ; Md et al., 2022 ), which allows prompt corrective actions to optimize energy usage and prevent energy inefficiencies. Within this context, ML techniques for image analysis (Casini et al., 2024 ) are also gaining increasing interest (Chen et al., 2023 ), for their application to e.g. materials design and optimization (Choudhury, 2021 ), quality control (Badmos et al., 2020 ), process monitoring (Ho et al., 2021 ), or detection of machine failures by converting time series data from sensors to 2D images (Wen et al., 2017 ).

Incorporating digitalisation and ML techniques into Industry 4.0 has led to significant energy savings (Maggiore et al., 2021 ; Nota et al., 2020 ). Projects adopting these technologies can achieve an average of 15% to 25% improvement in energy efficiency in the processes where they were implemented (Arana-Landín et al., 2023 ). For instance, in predictive maintenance, ML can reduce energy consumption by optimizing the operation of machinery (Agrawal et al., 2023 ; Pan et al., 2024 ). In process optimization, ML algorithms can improve energy efficiency by 10-20% by analyzing and adjusting machine operations for optimal performance, thereby reducing unnecessary energy usage (Leong et al., 2020 ). Furthermore, the implementation of ML algorithms for optimal control can lead to energy savings of 30%, because these systems can make real-time adjustments to production lines, ensuring that machines operate at peak energy efficiency (Rahul & Chiddarwar, 2023 ).

In automotive manufacturing, ML-driven quality control can lead to energy savings by reducing the need for redoing parts or running inefficient production cycles (Vater et al., 2019 ). In high-volume production environments such as consumer electronics, novel computer-based vision models for automated detection and classification of damaged packages from intact packages can speed up operations and reduce waste (Shahin et al., 2023 ). In heavy industries like steel or chemical manufacturing, ML can optimize the energy consumption of large machinery. By predicting the optimal operating conditions and maintenance schedules, these systems can save energy costs (Mypati et al., 2023 ). Compressed air is one of the most energy-intensive processes in manufacturing. ML can optimize the performance of these systems, potentially leading to energy savings by continuously monitoring and adjusting the air compressors for peak efficiency, avoiding energy losses due to leaks or inefficient operation (Benedetti et al., 2019 ). ML can also contribute to reducing energy consumption and minimizing incorrectly produced parts in polymer processing enterprises (Willenbacher et al., 2021 ).

Here we focus on a practical industrial case study of brake caliper processing. In detail, we focus on the quality control operation, which is typically accomplished by human visual inspection and requires a dedicated handling system. This eventually implies a slower production rate, and inefficient energy usage. We thus propose the integration of an ML-based system to automatically perform the quality control operation, without the need for a dedicated handling system and thus reduced operation time. To this, we rely on ML tools able to analyze and extract information from images, that is, deep convolutional neural networks, D-CNNs (Alzubaidi et al., 2021 ; Chai et al., 2021 ).

Sample 3D model (GrabCAD ) of the considered brake caliper: (a) part without defects, and (b) part with three sample defects, namely a scratch, a partially missing letter in the logo, and a circular painting defect (shown by the yellow squares, from left to right respectively)

A complete workflow for the purpose has been developed and tested on a real industrial test case. This includes: a dedicated pre-processing of the brake caliper images, their labelling and analysis using two dedicated D-CNN architectures (one for background removal, and one for defect identification), post-processing and analysis of the neural network output. Several different D-CNN architectures have been tested, in order to find the best model in terms of accuracy and computational demand. The results show that, a judicious choice of the ML model with a proper training, allows to obtain fast and accurate recognition of possible defects. The best-performing models, indeed, reach over 98% accuracy on the target criteria for quality control, and take only few seconds to analyze each image. These results make the proposed workflow compliant with the typical industrial expectations; therefore, in perspective, it could be implemented for an ML-powered version of the considered industrial problem. This would eventually allow to achieve better performance of the manufacturing process and, ultimately, a better management of the available resources in terms of time consumption and energy expense.

Different neural network architectures: convolutional encoder (a) and encoder-decoder (b)

The industrial quality control process that we target is the visual inspection of manufactured components, to verify the absence of possible defects. Due to industrial confidentiality reasons, a representative open-source 3D geometry (GrabCAD ) of the considered parts, similar to the original one, is shown in Fig. 1 . For illustrative purposes, the clean geometry without defects (Fig. 1 (a)) is compared to the geometry with three possible sample defects, namely: a scratch on the surface of the brake caliper, a partially missing letter in the logo, and a circular painting defect (highlighted by the yellow squares, from left to right respectively, in Fig. 1 (b)). Note that, one or multiple defects may be present on the geometry, and that other types of defects may also be considered.

Within the industrial production line, this quality control is typically time consuming, and requires a dedicated handling system with the associated slow production rate and energy inefficiencies. Thus, we developed a methodology to achieve an ML-powered version of the control process. The method relies on data analysis and, in particular, on information extraction from images of the brake calipers via Deep Convolutional Neural Networks, D-CNNs (Alzubaidi et al., 2021 ). The designed workflow for defect recognition is implemented in the following two steps: 1) removal of the background from the image of the caliper, in order to reduce noise and irrelevant features in the image, ultimately rendering the algorithms more flexible with respect to the background environment; 2) analysis of the geometry of the caliper to identify the different possible defects. These two serial steps are accomplished via two different and dedicated neural networks, whose architecture is discussed in the next section.

Convolutional Neural Networks (CNNs) pertain to a particular class of deep neural networks for information extraction from images. The feature extraction is accomplished via convolution operations; thus, the algorithms receive an image as an input, analyze it across several (deep) neural layers to identify target features, and provide the obtained information as an output (Casini et al., 2024 ). Regarding this latter output, different formats can be retrieved based on the considered architecture of the neural network. For a numerical data output, such as that required to obtain a classification of the content of an image (Bhatt et al., 2021 ), e.g. correct or defective caliper in our case, a typical layout of the network involving a convolutional backbone, and a fully-connected network can be adopted (see Fig. 2 (a)). On the other hand, if the required output is still an image, a more complex architecture with a convolutional backbone (encoder) and a deconvolutional head (decoder) can be used (see Fig. 2 (b)).

As previously introduced, our workflow targets the analysis of the brake calipers in a two-step procedure: first, the removal of the background from the input image (e.g. Fig. 1 ); second, the geometry of the caliper is analyzed and the part is classified as acceptable or not depending on the absence or presence of any defect, respectively. Thus, in the first step of the procedure, a dedicated encoder-decoder network (Minaee et al., 2021 ) is adopted to classify the pixels in the input image as brake or background. The output of this model will then be a new version of the input image, where the background pixels are blacked. This helps the algorithms in the subsequent analysis to achieve a better performance, and to avoid bias due to possible different environments in the input image. In the second step of the workflow, a dedicated encoder architecture is adopted. Here, the previous background-filtered image is fed to the convolutional network, and the geometry of the caliper is analyzed to spot possible defects and thus classify the part as acceptable or not. In this work, both deep learning models are supervised , that is, the algorithms are trained with the help of human-labeled data (LeCun et al., 2015 ). Particularly, the first algorithm for background removal is fed with the original image as well as with a ground truth (i.e. a binary image, also called mask , consisting of black and white pixels) which instructs the algorithm to learn which pixels pertain to the brake and which to the background. This latter task is usually called semantic segmentation in Machine Learning and Deep Learning (Géron, 2022 ). Analogously, the second algorithm is fed with the original image (without the background) along with an associated mask, which serves the neural networks with proper instructions to identify possible defects on the target geometry. The required pre-processing of the input images, as well as their use for training and validation of the developed algorithms, are explained in the next sections.

Image pre-processing

Machine Learning approaches rely on data analysis; thus, the quality of the final results is well known to depend strongly on the amount and quality of the available data for training of the algorithms (Banko & Brill, 2001 ; Chen et al., 2021 ). In our case, the input images should be well-representative for the target analysis and include adequate variability of the possible features to allow the neural networks to produce the correct output. In this view, the original images should include, e.g., different possible backgrounds, a different viewing angle of the considered geometry and a different light exposure (as local light reflections may affect the color of the geometry and thus the analysis). The creation of such a proper dataset for specific cases is not always straightforward; in our case, for example, it would imply a systematic acquisition of a large set of images in many different conditions. This would require, in turn, disposing of all the possible target defects on the real parts, and of an automatic acquisition system, e.g., a robotic arm with an integrated camera. Given that, in our case, the initial dataset could not be generated on real parts, we have chosen to generate a well-balanced dataset of images in silico , that is, based on image renderings of the real geometry. The key idea was that, if the rendered geometry is sufficiently close to a real photograph, the algorithms may be instructed on artificially-generated images and then tested on a few real ones. This approach, if properly automatized, clearly allows to easily produce a large amount of images in all the different conditions required for the analysis.

In a first step, starting from the CAD file of the brake calipers, we worked manually using the open-source software Blender (Blender ), to modify the material properties and achieve a realistic rendering. After that, defects were generated by means of Boolean (subtraction) operations between the geometry of the brake caliper and ad-hoc geometries for each defect. Fine tuning on the generated defects has allowed for a realistic representation of the different defects. Once the results were satisfactory, we developed an automated Python code for the procedures, to generate the renderings in different conditions. The Python code allows to: load a given CAD geometry, change the material properties, set different viewing angles for the geometry, add different types of defects (with given size, rotation and location on the geometry of the brake caliper), add a custom background, change the lighting conditions, render the scene and save it as an image.

In order to make the dataset as varied as possible, we introduced three light sources into the rendering environment: a diffuse natural lighting to simulate daylight conditions, and two additional artificial lights. The intensity of each light source and the viewing angle were then made vary randomly, to mimic different daylight conditions and illuminations of the object. This procedure was designed to provide different situations akin to real use, and to make the model invariant to lighting conditions and camera position. Moreover, to provide additional flexibility to the model, the training dataset of images was virtually expanded using data augmentation (Mumuni & Mumuni, 2022 ), where saturation, brightness and contrast were made randomly vary during training operations. This procedure has allowed to consistently increase the number and variety of the images in the training dataset.

The developed automated pre-processing steps easily allows for batch generation of thousands of different images to be used for training of the neural networks. This possibility is key for proper training of the neural networks, as the variability of the input images allows the models to learn all the possible features and details that may change during real operating conditions.

Examples of the ground truth for the two target tasks: background removal (a) and defects recognition (b)

The first tests using such virtual database have shown that, although the generated images were very similar to real photographs, the models were not able to properly recognize the target features in the real images. Thus, in a tentative to get closer to a proper set of real images, we decided to adopt a hybrid dataset, where the virtually generated images were mixed with the available few real ones. However, given that some possible defects were missing in the real images, we also decided to manipulate the images to introduce virtual defects on real images. The obtained dataset finally included more than 4,000 images, where 90% was rendered, and 10% was obtained from real images. To avoid possible bias in the training dataset, defects were present in 50% of the cases in both the rendered and real image sets. Thus, in the overall dataset, the real original images with no defects were 5% of the total.

Along with the code for the rendering and manipulation of the images, dedicated Python routines were developed to generate the corresponding data labelling for the supervised training of the networks, namely the image masks. Particularly, two masks were generated for each input image: one for the background removal operation, and one for the defect identification. In both cases, the masks consist of a binary (i.e. black and white) image where all the pixels of a target feature (i.e. the geometry or defect) are assigned unitary values (white); whereas, all the remaining pixels are blacked (zero values). An example of these masks in relation to the geometry in Fig. 1 is shown in Fig. 3 .

All the generated images were then down-sampled, that is, their resolution was reduced to avoid unnecessary large computational times and (RAM) memory usage while maintaining the required level of detail for training of the neural networks. Finally, the input images and the related masks were split into a mosaic of smaller tiles, to achieve a suitable size for feeding the images to the neural networks with even more reduced requirements on the RAM memory. All the tiles were processed, and the whole image reconstructed at the end of the process to visualize the overall final results.

Confusion matrix for accuracy assessment of the neural networks models

Choice of the model

Within the scope of the present application, a wide range of possibly suitable models is available (Chen et al., 2021 ). In general, the choice of the best model for a given problem should be made on a case-by-case basis, considering an acceptable compromise between the achievable accuracy and computational complexity/cost. Too simple models can indeed be very fast in the response yet have a reduced accuracy. On the other hand, more complex models can generally provide more accurate results, although typically requiring larger amounts of data for training, and thus longer computational times and energy expense. Hence, testing has the crucial role to allow identification of the best trade-off between these two extreme cases. A benchmark for model accuracy can generally be defined in terms of a confusion matrix, where the model response is summarized into the following possibilities: True Positives (TP), True Negatives (TN), False Positives (FP) and False Negatives (FN). This concept can be summarized as shown in Fig. 4 . For the background removal, Positive (P) stands for pixels belonging to the brake caliper, while Negative (N) for background pixels. For the defect identification model, Positive (P) stands for non-defective geometry, whereas Negative (N) stands for defective geometries. With respect to these two cases, the True/False statements stand for correct or incorrect identification, respectively. The model accuracy can be therefore assessed as Géron ( 2022 )

Based on this metrics, the accuracy for different models can then be evaluated on a given dataset, where typically 80% of the data is used for training and the remaining 20% for validation. For the defect recognition stage, the following models were tested: VGG-16 (Simonyan & Zisserman, 2014 ), ResNet50, ResNet101, ResNet152 (He et al., 2016 ), Inception V1 (Szegedy et al., 2015 ), Inception V4 and InceptionResNet V2 (Szegedy et al., 2017 ). Details on the assessment procedure for the different models are provided in the Supplementary Information file. For the background removal stage, the DeepLabV3 $+$ (Chen et al., 2018 ) model was chosen as the first option, and no additional models were tested as it directly provided satisfactory results in terms of accuracy and processing time. This gives preliminary indication that, from the point of view of the task complexity of the problem, the defect identification stage can be more demanding with respect to the background removal operation for the case study at hand. Besides the assessment of the accuracy according to, e.g., the metrics discussed above, additional information can be generally collected, such as too low accuracy (indicating insufficient amount of training data), possible bias of the models on the data (indicating a non-well balanced training dataset), or other specific issues related to missing representative data in the training dataset (Géron, 2022 ). This information helps both to correctly shape the training dataset, and to gather useful indications for the fine tuning of the model after its choice has been made.

Background removal

An initial bias of the model for background removal arose on the color of the original target geometry (red color). The model was indeed identifying possible red spots on the background as part of the target geometry as an unwanted output. To improve the model flexibility, and thus its accuracy on the identification of the background, the training dataset was expanded using data augmentation (Géron, 2022 ). This technique allows to artificially increase the size of the training dataset by applying various transformations to the available images, with the goal to improve the performance and generalization ability of the models. This approach typically involves applying geometric and/or color transformations to the original images; in our case, to account for different viewing angles of the geometry, different light exposures, and different color reflections and shadowing effects. These improvements of the training dataset proved to be effective on the performance for the background removal operation, with a validation accuracy finally ranging above 99% and model response time around 1-2 seconds. An example of the output of this operation for the geometry in Fig. 1 is shown in Fig. 5 .

While the results obtained were satisfactory for the original (red) color of the calipers, we decided to test the model ability to be applied on brake calipers of other colors as well. To this, the model was trained and tested on a grayscale version of the images of the calipers, which allows to completely remove any possible bias of the model on a specific color. In this case, the validation accuracy of the model was still obtained to range above 99%; thus, this approach was found to be particularly interesting to make the model suitable for background removal operation even on images including calipers of different colors.

Target geometry after background removal

Defect recognition

An overview of the performance of the tested models for the defect recognition operation on the original geometry of the caliper is reported in Table 1 (see also the Supplementary Information file for more details on the assessment of different models). The results report on the achieved validation accuracy ( $A_v$ ) and on the number of parameters ( $N_p$ ), with this latter being the total number of parameters that can be trained for each model (Géron, 2022 ) to determine the output. Here, this quantity is adopted as an indicator of the complexity of each model.

Accuracy (a) and loss function (b) curves for the Resnet101 model during training

As the results in Table 1 show, the VGG-16 model was quite unprecise for our dataset, eventually showing underfitting (Géron, 2022 ). Thus, we decided to opt for the Resnet and Inception families of models. Both these families of models have demonstrated to be suitable for handling our dataset, with slightly less accurate results being provided by the Resnet50 and InceptionV1. The best results were obtained using Resnet101 and InceptionV4, with very high final accuracy and fast processing time (in the order $\sim $ 1 second). Finally, Resnet152 and InceptionResnetV2 models proved to be slightly too complex or slower for our case; they indeed provided excellent results but taking longer response times (in the order of $\sim $ 3-5 seconds). The response time is indeed affected by the complexity ( $N_p$ ) of the model itself, and by the hardware used. In our work, GPUs were used for training and testing all the models, and the hardware conditions were kept the same for all models.

Based on the results obtained, ResNet101 model was chosen as the best solution for our application, in terms of accuracy and reduced complexity. After fine-tuning operations, the accuracy that we obtained with this model reached nearly 99%, both in the validation and test datasets. This latter includes target real images, that the models have never seen before; thus, it can be used for testing of the ability of the models to generalize the information learnt during the training/validation phase.

The trend in the accuracy increase and loss function decrease during training of the Resnet101 model on the original geometry are shown in Fig. 6 (a) and (b), respectively. Particularly, the loss function quantifies the error between the predicted output during training of the model and the actual target values in the dataset. In our case, the loss function is computed using the cross-entropy function and the Adam optimiser (Géron, 2022 ). The error is expected to reduce during the training, which eventually leads to more accurate predictions of the model on previously-unseen data. The combination of accuracy and loss function trends, along with other control parameters, is typically used and monitored to evaluate the training process, and avoid e.g. under- or over-fitting problems (Géron, 2022 ). As Fig. 6 (a) shows, the accuracy experiences a sudden step increase during the very first training phase (epochs, that is, the number of times the complete database is repeatedly scrutinized by the model during its training (Géron, 2022 )). The accuracy then increases in a smooth fashion with the epochs, until an asymptotic value is reached both for training and validation accuracy. These trends in the two accuracy curves can generally be associated with a proper training; indeed, being the two curves close to each other may be interpreted as an absence of under-fitting problems. On the other hand, Fig. 6 (b) shows that the loss function curves are close to each other, with a monotonically-decreasing trend. This can be interpreted as an absence of over-fitting problems, and thus of proper training of the model.

Final results of the analysis on the defect identification: (a) considered input geometry, (b), (c) and (d) identification of a scratch on the surface, partially missing logo, and painting defect respectively (highlighted in the red frames)

Finally, an example output of the overall analysis is shown in Fig. 7 , where the considered input geometry is shown (a), along with the identification of the defects (b), (c) and (d) obtained from the developed protocol. Note that, here the different defects have been separated in several figures for illustrative purposes; however, the analysis yields the identification of defects on one single image. In this work, a binary classification was performed on the considered brake calipers, where the output of the models allows to discriminate between defective or non-defective components based on the presence or absence of any of the considered defects. Note that, fine tuning of this discrimination is ultimately with the user’s requirements. Indeed, the model output yields as the probability (from 0 to 100%) of the possible presence of defects; thus, the discrimination between a defective or non-defective part is ultimately with the user’s choice of the acceptance threshold for the considered part (50% in our case). Therefore, stricter or looser criteria can be readily adopted. Eventually, for particularly complex cases, multiple models may also be used concurrently for the same task, and the final output defined based on a cross-comparison of the results from different models. As a last remark on the proposed procedure, note that here we adopted a binary classification based on the presence or absence of any defect; however, further classification of the different defects could also be implemented, to distinguish among different types of defects (multi-class classification) on the brake calipers.

Energy saving

Illustrative scenarios.

Given that the proposed tools have not yet been implemented and tested within a real industrial production line, we analyze here three perspective scenarios to provide a practical example of the potential for energy savings in an industrial context. To this, we consider three scenarios, which compare traditional human-based control operations and a quality control system enhanced by the proposed Machine Learning (ML) tools. Specifically, here we analyze a generic brake caliper assembly line formed by 14 stations, as outlined in Table 1 in the work by Burduk and Górnicka ( 2017 ). This assembly line features a critical inspection station dedicated to defect detection, around which we construct three distinct scenarios to evaluate the efficacy of traditional human-based control operations versus a quality control system augmented by the proposed ML-based tools, namely:

First Scenario (S1): Human-Based Inspection. The traditional approach involves a human operator responsible for the inspection tasks.

Second Scenario (S2): Hybrid Inspection. This scenario introduces a hybrid inspection system where our proposed ML-based automatic detection tool assists the human inspector. The ML tool analyzes the brake calipers and alerts the human inspector only when it encounters difficulties in identifying defects, specifically when the probability of a defect being present or absent falls below a certain threshold. This collaborative approach aims to combine the precision of ML algorithms with the experience of human inspectors, and can be seen as a possible transition scenario between the human-based and a fully-automated quality control operation.

Third Scenario (S3): Fully Automated Inspection. In the final scenario, we conceive a completely automated defect inspection station powered exclusively by our ML-based detection system. This setup eliminates the need for human intervention, relying entirely on the capabilities of the ML tools to identify defects.

For simplicity, we assume that all the stations are aligned in series without buffers, minimizing unnecessary complications in our estimations. To quantify the beneficial effects of implementing ML-based quality control, we adopt the Overall Equipment Effectiveness (OEE) as the primary metric for the analysis. OEE is a comprehensive measure derived from the product of three critical factors, as outlined by Nota et al. ( 2020 ): Availability (the ratio of operating time with respect to planned production time); Performance (the ratio of actual output with respect to the theoretical maximum output); and Quality (the ratio of the good units with respect to the total units produced). In this section, we will discuss the details of how we calculate each of these factors for the various scenarios.

To calculate Availability ( A ), we consider an 8-hour work shift ( $t_{shift}$ ) with 30 minutes of breaks ( $t_{break}$ ) during which we assume production stop (except for the fully automated scenario), and 30 minutes of scheduled downtime ( $t_{sched}$ ) required for machine cleaning and startup procedures. For unscheduled downtime ( $t_{unsched}$ ), primarily due to machine breakdowns, we assume an average breakdown probability ( $\rho _{down}$ ) of 5% for each machine, with an average repair time of one hour per incident ( $t_{down}$ ). Based on these assumptions, since the Availability represents the ratio of run time ( $t_{run}$ ) to production time ( $t_{pt}$ ), it can be calculated using the following formula:

with the unscheduled downtime being computed as follows:

where N is the number of machines in the production line and $1-\left( 1-\rho _{down}\right) ^{N}$ represents the probability that at least one machine breaks during the work shift. For the sake of simplicity, the $t_{down}$ is assumed constant regardless of the number of failures.

Table 2 presents the numerical values used to calculate Availability in the three scenarios. In the second scenario, we can observe that integrating the automated station leads to a decrease in the first factor of the OEE analysis, which can be attributed to the additional station for automated quality-control (and the related potential failure). This ultimately increases the estimation of the unscheduled downtime. In the third scenario, the detrimental effect of the additional station compensates the beneficial effect of the automated quality control on reducing the need for pauses during operator breaks; thus, the Availability for the third scenario yields as substantially equivalent to the first one (baseline).

The second factor of OEE, Performance ( P ), assesses the operational efficiency of production equipment relative to its maximum designed speed ( $t_{line}$ ). This evaluation includes accounting for reductions in cycle speed and minor stoppages, collectively termed as speed losses . These losses are challenging to measure in advance, as performance is typically measured using historical data from the production line. For this analysis, we are constrained to hypothesize a reasonable estimate of 60 seconds of time lost to speed losses ( $t_{losses}$ ) for each work cycle. Although this assumption may appear strong, it will become evident later that, within the context of this analysis – particularly regarding the impact of automated inspection on energy savings – the Performance (like the Availability) is only marginally influenced by introducing an automated inspection station. To account for the effect of automated inspection on the assembly line speed, we keep the time required by the other 13 stations ( $t^*_{line}$ ) constant while varying the time allocated for visual inspection ( $t_{inspect}$ ). According to Burduk and Górnicka ( 2017 ), the total operation time of the production line, excluding inspection, is 1263 seconds, with manual visual inspection taking 38 seconds. For the fully automated third scenario, we assume an inspection time of 5 seconds, which encloses the photo collection, pre-processing, ML-analysis, and post-processing steps. In the second scenario, instead, we add an additional time to the pure automatic case to consider the cases when the confidence of the ML model falls below 90%. We assume this happens once in every 10 inspections, which is a conservative estimate, higher than that we observed during model testing. This results in adding 10% of the human inspection time to the fully automated time. Thus, when $t_{losses}$ are known, Performance can be expressed as follows:

The calculated values for Performance are presented in Table 3 , and we can note that the modification in inspection time has a negligible impact on this factor since it does not affect the speed loss or, at least to our knowledge, there is no clear evidence to suggest that the introduction of a new inspection station would alter these losses. Moreover, given the specific linear layout of the considered production line, the inspection time change has only a marginal effect on enhancing the production speed. However, this approach could potentially bias our scenario towards always favouring automation. To evaluate this hypothesis, a sensitivity analysis which explores scenarios where the production line operates at a faster pace will be discussed in the next subsection.

The last factor, Quality ( Q ), quantifies the ratio of compliant products out of the total products manufactured, effectively filtering out items that fail to meet the quality standards due to defects. Given the objective of our automated algorithm, we anticipate this factor of the OEE to be significantly enhanced by implementing the ML-based automated inspection station. To estimate it, we assume a constant defect probability for the production line ( $\rho _{def}$ ) at 5%. Consequently, the number of defective products ( $N_{def}$ ) during the work shift is calculated as $N_{unit} \cdot \rho _{def}$ , where $N_{unit}$ represents the average number of units (brake calipers) assembled on the production line, defined as:

To quantify defective units identified, we consider the inspection accuracy ( $\rho _{acc}$ ), where for human visual inspection, the typical accuracy is 80% (Sundaram & Zeid, 2023 ), and for the ML-based station, we use the accuracy of our best model, i.e., 99%. Additionally, we account for the probability of the station mistakenly identifying a caliper as with a defect even if it is defect-free, i.e., the false negative rate ( $\rho _{FN}$ ), defined as

In the absence of any reasonable evidence to justify a bias on one mistake over others, we assume a uniform distribution for both human and automated inspections regarding error preference, i.e. we set $\rho ^{H}_{FN} = \rho ^{ML}_{FN} = \rho _{FN} = 50\%$ . Thus, the number of final compliant goods ( $N_{goods}$ ), i.e., the calipers that are identified as quality-compliant, can be calculated as:

where $N_{detect}$ is the total number of detected defective units, comprising TN (true negatives, i.e. correctly identified defective calipers) and FN (false negatives, i.e. calipers mistakenly identified as defect-free). The Quality factor can then be computed as:

Table 4 summarizes the Quality factor calculation, showcasing the substantial improvement brought by the ML-based inspection station due to its higher accuracy compared to human operators.

Overall Equipment Effectiveness (OEE) analysis for three scenarios (S1: Human-Based Inspection, S2: Hybrid Inspection, S3: Fully Automated Inspection). The height of the bars represents the percentage of the three factors A : Availability, P : Performance, and Q : Quality, which can be interpreted from the left axis. The green bars indicate the OEE value, derived from the product of these three factors. The red line shows the recall rate, i.e. the probability that a defective product is rejected by the client, with values displayed on the right red axis

Finally, we can determine the Overall Equipment Effectiveness by multiplying the three factors previously computed. Additionally, we can estimate the recall rate ( $\rho _{R}$ ), which reflects the rate at which a customer might reject products. This is derived from the difference between the total number of defective units, $N_{def}$ , and the number of units correctly identified as defective, TN , indicating the potential for defective brake calipers that may bypass the inspection process. In Fig. 8 we summarize the outcomes of the three scenarios. It is crucial to note that the scenarios incorporating the automated defect detector, S2 and S3, significantly enhance the Overall Equipment Effectiveness, primarily through substantial improvements in the Quality factor. Among these, the fully automated inspection scenario, S3, emerges as a slightly superior option, thanks to its additional benefit in removing the breaks and increasing the speed of the line. However, given the different assumptions required for this OEE study, we shall interpret these results as illustrative, and considering them primarily as comparative with the baseline scenario only. To analyze the sensitivity of the outlined scenarios on the adopted assumptions, we investigate the influence of the line speed and human accuracy on the results in the next subsection.

Sensitivity analysis

The scenarios described previously are illustrative and based on several simplifying hypotheses. One of such hypotheses is that the production chain layout operates entirely in series, with each station awaiting the arrival of the workpiece from the preceding station, resulting in a relatively slow production rate (1263 seconds). This setup can be quite different from reality, where slower operations can be accelerated by installing additional machines in parallel to balance the workload and enhance productivity. Moreover, we utilized a literature value of 80% for the accuracy of the human visual inspector operator, as reported by Sundaram and Zeid ( 2023 ). However, this accuracy can vary significantly due to factors such as the experience of the inspector and the defect type.

Effect of assembly time for stations (excluding visual inspection), $t^*_{line}$ , and human inspection accuracy, $\rho _{acc}$ , on the OEE analysis. (a) The subplot shows the difference between the scenario S2 (Hybrid Inspection) and the baseline scenario S1 (Human Inspection), while subplot (b) displays the difference between scenario S3 (Fully Automated Inspection) and the baseline. The maps indicate in red the values of $t^*_{line}$ and $\rho _{acc}$ where the integration of automated inspection stations can significantly improve OEE, and in blue where it may lower the score. The dashed lines denote the breakeven points, and the circled points pinpoint the values of the scenarios used in the “Illustrative scenario” Subsection.

A sensitivity analysis on these two factors was conducted to address these variations. The assembly time of the stations (excluding visual inspection), $t^*_{line}$ , was varied from 60 s to 1500 s, and the human inspection accuracy, $\rho _{acc}$ , ranged from 50% (akin to a random guesser) to 100% (representing an ideal visual inspector); meanwhile, the other variables were kept fixed.

The comparison of the OEE enhancement for the two scenarios employing ML-based inspection against the baseline scenario is displayed in the two maps in Fig. 9 . As the figure shows, due to the high accuracy and rapid response of the proposed automated inspection station, the area representing regions where the process may benefit energy savings in the assembly lines (indicated in red shades) is significantly larger than the areas where its introduction could degrade performance (indicated in blue shades). However, it can be also observed that the automated inspection could be superfluous or even detrimental in those scenarios where human accuracy and assembly speed are very high, indicating an already highly accurate workflow. In these cases, and particularly for very fast production lines, short times for quality control can be expected to be key (beyond accuracy) for the optimization.

Finally, it is important to remark that the blue region (areas below the dashed break-even lines) might expand if the accuracy of the neural networks for defect detection is lower when implemented in an real production line. This indicates the necessity for new rounds of active learning and an augment of the ratio of real images in the database, to eventually enhance the performance of the ML model.

Conclusions

Industrial quality control processes on manufactured parts are typically achieved by human visual inspection. This usually requires a dedicated handling system, and generally results in a slower production rate, with the associated non-optimal use of the energy resources. Based on a practical test case for quality control on brake caliper manufacturing, in this work we have reported on a developed workflow for integration of Machine Learning methods to automatize the process. The proposed approach relies on image analysis via Deep Convolutional Neural Networks. These models allow to efficiently extract information from images, thus possibly representing a valuable alternative to human inspection.

The proposed workflow relies on a two-step procedure on the images of the brake calipers: first, the background is removed from the image; second, the geometry is inspected to identify possible defects. These two steps are accomplished thanks to two dedicated neural network models, an encoder-decoder and an encoder network, respectively. Training of these neural networks typically requires a large number of representative images for the problem. Given that, one such database is not always readily available, we have presented and discussed an alternative methodology for the generation of the input database using 3D renderings. While integration of the database with real photographs was required for optimal results, this approach has allowed fast and flexible generation of a large base of representative images. The pre-processing steps required for data feeding to the neural networks and their training has been also discussed.

Several models have been tested and evaluated, and the best one for the considered case identified. The obtained accuracy for defect identification reaches $\sim $ 99% of the tested cases. Moreover, the response of the models is fast (in the order of few seconds) on each image, which makes them compliant with the most typical industrial expectations.

In order to provide a practical example of possible energy savings when implementing the proposed ML-based methodology for quality control, we have analyzed three perspective industrial scenarios: a baseline scenario, where quality control tasks are performed by a human inspector; a hybrid scenario, where the proposed ML automatic detection tool assists the human inspector; a fully-automated scenario, where we envision a completely automated defect inspection. The results show that the proposed tools may help increasing the Overall Equipment Effectiveness up to $\sim $ 10% with respect to the considered baseline scenario. However, a sensitivity analysis on the speed of the production line and on the accuracy of the human inspector has also shown that the automated inspection could be superfluous or even detrimental in those cases where human accuracy and assembly speed are very high. In these cases, reducing the time required for quality control can be expected to be the major controlling parameter (beyond accuracy) for optimization.

Overall the results show that, with a proper tuning, these models may represent a valuable resource for integration into production lines, with positive outcomes on the overall effectiveness, and thus ultimately leading to a better use of the energy resources. To this, while the practical implementation of the proposed tools can be expected to require contained investments (e.g. a portable camera, a dedicated workstation and an operator with proper training), in field tests on a real industrial line would be required to confirm the potential of the proposed technology.

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Energy and climate transitions bear an inherent risk of replicating historically embedded unjust gendered norms in the current energy regimes. Positioning our work within critical feminist scholarship, our study emphasizes the embedded nature of energy technologies within respective socio-economic, institutional and cultural contexts. We use a combined lens of care and epistemic injustice to examine the case study of Solar Mamas in Barefoot College Zanzibar, highlighting the nuanced interplay of power relations in decentralized energy transitions. This approach helps comprehend and value gendered energy care work as involving skilled labour in everyday life. Our findings illustrate the need for energy transitions research, policy and practice to be deeply informed by lived experiences, diverse practices of care within the energy webs and valuing of multiple voices. We argue that interventions prioritizing care and knowledge in decentralized, locally managed energy provisioning have the potential to disrupt established gender relations.

Energy and climate transitions are deeply social and gendered in all contexts where they play out 1 , 2 . However, the scholarship on energy transitions remains dominated by techno-economic analyses that fail to recognize or problematize the gendered dimensions of energy policy, production, use and impacts 3 , 4 , 5 . Research that applies a gender lens to energy studies highlight the problem of women’s exclusion from energy services and from influence in decision-making in energy-related matters 6 , 7 , 8 . The literature also documents women’s roles as domestic energy providers and related physical drudgery. The lived reality and experiences of women globally are crucial to document and understand, yet the focus on women’s domestic work and family relations has dominated energy studies and rendered other roles that women play in public spheres and private sector less visible. Relatively little work exists on how gender roles shape energy entrepreneurship and who is considered an energy expert 9 , 10 . Studies that place emphasis on women’s exclusion and suffering without providing sufficient attention to women’s agency and contributions to various spheres of society, reproduce problematic assumptions around women as belonging primarily in the domestic realm and as lacking agency 11 .

We depart from two interlinked themes that are well grounded in the history of feminist theory: women’s knowledge and care work—and how these are (not) valued 12 , 13 . Within those, two specific debates have potential to contribute to alternative visions of energy transitions: theorizations of ‘care’ and ‘epistemic injustice’. In this Article, we integrate a unique framing of epistemic injustice with a care lens aiming to explore how power relations shape and get reshaped in processes of decentralized solar electricity provision in Zanzibar. Drawing on a qualitative, interview-based case study of the Barefoot College Zanzibar, undertaken in October 2022, we identify the strategies and actions that enabled a process of empowerment of women enrolled as solar technicians in a patriarchal societal context. This case serves as a distinct approach illustrating the imperative for energy transitions research, policy and practice to be deeply informed by lived experiences, practices of ‘care’ and valuing of multiple voices and narratives.

Positioning this work within critical feminist scholarship on energy transitions 14 , 15 , 16 , 17 , we argue that a more profound rethinking of the energy–gender nexus is necessary, including attention to intersecting relations of power, heterogeneous contexts and groups, a multiplicity of identities, arenas and norms. The diversity needs recognition without losing from view how gendered norms and institutions effectively impact and delimit people’s lives. Given that energy and gender research is dominated by Western theoretical perspectives 15 , 18 , there is need for more studies carefully grounded in other cultural contexts. In the feminist theoretical traditions, the dominance of Western perspectives is a point of concern with calls for pluralizing ontology and epistemology 19 , 20 . The question of who can speak, who shapes the narrative and whose experience counts as valid 21 speaks as much to energy sector practice as to scholarly practice.

Critical social science energy research 22 , 23 , 24 , 25 posits ‘care’ as the missing lens in illuminating the complex web of dependencies, interdependencies and the unequal power dynamics inherent in the unfolding of energy transitions. Our approach draws inspiration from these initial forays into the realm of care. Fisher and Tronto 26 define care as ‘A species activity that includes everything we do to maintain, continue and repair our world so that we can live in it as well as possible. That world includes our bodies, ourselves and our environment, all of which we seek to interweave in a complex, life-sustaining web’. Drawing on this definition, we introduce the concept of ‘energy care work’ to signify the daily practices undertaken at a household and community level for provisioning, sustaining, maintaining, repairing and ensuring the availability of energy carriers and services for day-to-day life. This approach to energy care work extends beyond caring for people to also caring for the infrastructures and technology and how these impact the environment. It is rooted in the lived experiences of dependence and relationships within energy webs, acknowledging the intricate connections between individuals, the energy systems and ecosystems that support their daily activities 23 , 24 , 27 . The diversity of energy-related practices and technologies are culturally and spatially embedded in specific ways in different contexts, which means that policy-induced changes play out differently as energy practices in homes and communities are entangled in intersecting power relationships that can be organized around gender, age, class, caste, ethnicity, religion and more. However, feminized practices of care even within the energy sector, especially provisioning for household energy, are often devalued 7 , 22 . Scholars have partly attributed this undervaluation of caregiving labour to its link with women, home and domestic responsibilities 4 , 28 .

Recent studies on energy transitions shed light on households as spaces where traditional gender norms intersect with new regulations and advancements in technology. For example, a study from Europe found that whereas activities such as energy renovations at home are often associated with men, most household tasks such as washing, food preparation, cleaning and caregiving are typically carried out by women 29 . Drawing attention to one unintended outcome, Johnson introduces the concept of ‘flexibility woman’ to underscore how women as a group are affected by policies introducing ‘smart’ hour-based energy pricing that incentivize users to engage in time-shifting practices of domestic chores (for example, to do laundry at night), as such activities predominantly are performed by women 30 . As energy policy increasingly targets domestic energy consumption, practices and responsibilities, it is crucial to understand how these are linked to context and technology-specific gender roles and work division. We argue that attention to energy care work is essential for predicting effectiveness and (unintended) consequences of interventions.

A common pattern across cultural contexts is that gendered norms surrounding care work in general also involve the associated devaluation of women’s contributions. The concept of ‘epistemic injustice’ supports our understanding of the devaluation of women’s care work. Defined by Miranda Fricker, it refers to the injustice and exclusion experienced by someone when they are specifically wronged in their capacity as a knower 21 . Fricker argues that the different social identities that one assumes, including, for example, gender, race and class, play a crucial role in shaping a person’s recognition or exclusion as a ‘knower’ 31 . Given that women are responsible for a lot of energy care work, we may expect epistemic injustice to be replicated as women take on new roles and tasks in new energy systems, also beyond the home.

The case—the Solar Mamas in Barefoot College—is highly relevant for understanding the importance of addressing epistemic injustice in the field of energy technologies as technology and engineering is historically coded as a male sphere and because the College supports the empowerment of rural women with little education by training them into experts and entrepreneurs. The combined lens of care and epistemic injustice provides an entry point to analysing energy transitions as involving skilled work in the everyday as much as in the socio-technical practices.

A conceptual analysis of gendered energy care work and epistemic injustice

Emerging critical literature on energy and climate change have been vocal around gendered norms surrounding care and unequal distribution of care activities 25 , 32 . Studies from the African and Asian contexts 33 , 34 , 35 , 36 , 37 have evidenced that the care work women perform is not just restricted to direct care responsibilities at the household level. Women are often responsible for provisioning work including subsistence agriculture, household energy supply and water supply. Such care practices often extend beyond the household level to care for the environment and village community spaces. Arora-Jonsson 38 introduce the concept of in-between spaces and in-between times to elaborate on women’s care work that challenges the boundaries between public work and private care, contributing valuable knowledge and skills. However, the globally skewed division of labour and dominant norms that value women’s paid or unpaid work less than men’s work, undermine the knowledge and experience possessed by women 38 .

We build on Miranda Fricker’s use of epistemic injustice to make sense of these experiences of exclusion. The concept of epistemic injustice as defined by Miranda Fricker 21 is linked to ‘care’ in how norms surrounding care work often brand caring as ‘women’s work’ and by consequence value it less than other kinds of work. Fricker identifies epistemic injustice as taking two shapes: testimonial and hermeneutical injustice 21 . Testimonial injustice signifies being dismissed as a credible and knowledgeable person. In the context of gendered energy care work, women—or for that matter people who do not fit norms about appropriate masculinity—may experience lack of credibility while participating in decision-making processes around matters related to energy programmes due to norms devaluing their experiences and technical competence. Hermeneutical injustice refers to a gap in collective interpretive abilities that disadvantage a socially marginalized group in their ability to make sense of their social experiences and valuing their own knowledge. A gendered manifestation of hermeneutical injustice in the energy space occurs when gendered roles, cultural norms and practices alienate women from technical spaces and/or women themselves restrict their participation in the decision-making process, often unaware of the potential contributions they could make.

Figure 1 illustrates the linkages among care work in general, the gendered nature of energy care work and epistemic injustice leading to women’s exclusion in the energy/technology related spaces. The connections depicted in Fig. 1 between the concepts are not suggesting linear causality; instead, they serve to illustrate the mutual shaping of gendered energy care work and epistemic injustice. Care and energy are intricately interwoven in, for example, the responsibilities for provision of domestic and community energy needs and performing manual or mechanized work in homes, public and private spaces. An understanding of energy care work and its gendered dimensions is crucial in the context of emerging energy transitions and movement towards decentralized energy systems and a less energy intensive lifestyle. Decentralized energy solutions that embed care for technology in the community and domestic routines holds potential for disturbing or reproducing gender–technology conventions 23 , 27 . These care practices often sit outside formal markets blurring the lines between public and private and paid and unpaid care practices 22 , 27 . Here the ability to care is contingent upon a combination of material resources, time and knowledge 23 , 27 . A feminist perspective as employed here challenges the devaluation of gendered energy care work performed by women and their associated knowledge. Instead, it presents the valuation of this work as an open question for those involved to answer instead of viewing people’s involvement in care work as a problem to be addressed by technological solutions.

A conceptual illustration of the linkages among care, gendered energy care work and epistemic injustice.

Care and epistemic justice in decentralized energy solutions

The Solar Mamas programme by Barefoot College showcases how a combination of innovative governance practices, a solar technician training programme embedded within the socio-cultural dynamics of the region and community conscientization initiatives has gradually led to gender inequality being addressed through electrification initiatives in rural Zanzibar. Initiated in 1997 by Barefoot College in the village of Tilonia, Rajasthan, India, the Solar Mamas programme is currently operational in more than 90 countries worldwide. The Solar Mamas—that is the ‘solar mothers’—programme in Zanzibar targets illiterate or semi-literate women who are long-term residents in their respective villages, preferably mothers. Pregnant women and women with young children are not included due to their difficulty to spend extended training periods away from home.

Table 1 describes the different stages of the Solar Mamas programme. Throughout the programme, the recruitment team proactively builds trust in the training process and activities among the community members. The training programme lasts for five months at the Barefoot College campus and comprises three major steps.

Step 1, the pre-training phase of the Solar Mamas programme, as illustrated in Table 1 , begins with a community meeting orchestrated by a collaborative effort between representatives from the government of Zanzibar and Barefoot College Zanzibar, facilitated with the assistance of the village head. During this community gathering, the recruitment team solicits volunteers willing to participate in the programme. A critical criterion guiding the selection of candidates for the programme is their immobility. The anticipated long-term presence of the Solar Mamas within their respective villages forms an integral aspect of the programme’s expectations, that these trained Mamas should reside permanently in their communities, rendering service to the villagers.

Subsequently, the identification and nomination of women for the training initiative is determined through community consensus established during the meeting. Following the selection, contracts are signed with the selected women and their partners, if they are married. The initiation date of the training is communicated during this contractual process. Despite the transparent and collective nature of the recruitment process, the team confronts substantial challenges due to the entrenched patriarchal dynamics prevalent within society. Women frequently withdraw from the training programme, influenced by pressure exerted by men, encompassing threats of divorce and other coercive measures. At this juncture, governmental intervention plays a pivotal role in community sensitization. The recruitment team, in parallel, places considerable emphasis on fostering trust in the training process and subsequent activities among the community members.

Step 2, the training phase of the Solar Mamas programme consists of three components that run in parallel. The first component is the Solar Engineer Training Program, where the classes are largely demonstrations and learning-by-doing sessions of assembling, installing, repairing and maintaining solar cells. The second component of the training programme is the ENRICHE module, which places a lot of emphasis on promoting critical thinking and building awareness around injustices encountered by Mamas in their everyday lives. This includes learning about sexual and reproductive health, legal rights and social support structures. Financial literacy is another important topic covered in this module. The women are trained in opening bank accounts, financial management and planning. The third component aims to diversify and strengthen the women’s income basket by training them in beekeeping or sewing, ensuring an additional income.

After the training (which corresponds to Step 3 in Table 1 ), the government supplies the graduated ‘Solar Mama Engineers’ with the solar panels and equipment for installing these in the villages free of charge. The Solar Mamas, formerly tasked with household energy provision, possess a keen understanding of the challenges faced by community members in energy provisioning. They draw upon their first-hand experiences to persuade the community about the advantages of transitioning to solar energy, emphasizing for example improved health, enhanced education for children and opportunities for shops to stay open late. The training they undergo enhances the credibility of their voices, playing a pivotal role in the electrification process of the villages. Installation, maintenance and repair of solar equipment are conducted exclusively by the Solar Mamas or the assistants they train using their acquired skills.

Customers pay a monthly fee of 6,000 Tanzanian shillings (TSH, approximately US$2.5) per household to the Mamas as a leasing agreement, thereby also financially valuing the energy care work. This amount is much cheaper than the kerosene typically used for household lighting, which is generally above 9,000 TSH (approximately US$3.5) per month. The programme implements a monitoring and evaluation plan that includes follow-up sessions with each solar engineer three months post-training and subsequently every six months. At the time of study (October 2022), the programme had led to the lighting of nearly 1,000 homes in 19 villages for at least 7,000 people on the island and mainland. In the sections below we examine the insights from the semi-structured interviews conducted with the stakeholders at Barefoot College Zanzibar. Drawing on the concept of care in conjunction with epistemic injustice, we bring forth a relational understanding of gendered energy care work encompassing socio-cultural aspects and power relations. The paragraphs below elucidate the insights from the semi-structured interviews conducted with the stakeholders at Barefoot College Zanzibar.

In the context of decentralized electricity systems, practices of care, including installation, monitoring and maintenance of technologies, are crucial for ensuring their sustained functioning 27 . These are also common challenges that have caused the failure of many small electricity systems in rural areas 23 . To invest in women who are likely to stay in the village rather than move to town after training is a proven concept 39 , 40 . In this context where the domestic reproductive labour is often taken for granted, the college works to make the value of women’s traditional care work visible to the women and the community. In addition, the ENRICHE module supports the integration of the Solar Mamas’ lived experiences with their newly acquired knowledge and technical expertise, demonstrating the tangible impact of acknowledging and leveraging women’s traditional care work in community development.

The programme makes an intervention in rural non-electrified villages and homes across the islands by offering affordable electricity access, altering the everyday lived experiences of the women who get training and changing community perceptions of solar technology. The Solar Mamas transition from provisioning for household energy through fuel wood and kerosene to becoming solar engineers and providers of electricity to households and businesses. As providers of and carers for electricity, the Solar Mamas enable further services that benefit all village households, including electricity at the school and health care centre, thus becoming providers of critical care-related infrastructure 41 . As shown by previous studies 42 , 43 , electrification leads to changes in daily work practices and use of public spaces. In Zanzibar, shops started to stay open late, and women occupied public spaces after 6 pm, slowly creating a shift in gendered norms and power relations around who belongs in what space and when.

However, the programme does not merely follow an individualistic approach to training women as solar engineers. Their role is imagined as embedded within the communities they come from, and the college staff are cognizant of the power relations and gendered norms surrounding work and knowledge. The Solar Mamas’ ability to care for their communities were dependent on their social connections within the community, their knowledge, skills and status. This makes the support from the college and government vital, through all stages.

The college sees community involvement and sensitization as a crucial element for the success of the programme. Norms and attitudes are collectively held and changed, so male relatives and community members are involved directly or indirectly. These include gendered norms around women as sole providers of care in the household, men as breadwinners and decision-makers and electricity as a masculine domain. The pre-training phase’s community meetings are important in manifesting the support from male leaders in government and for explaining the programme’s transformative potential. But also, the programme is set apart from many short-term projects by the care the government and college display for the women after training.

Some of the trained Solar Mamas still experience major challenges in certain villages when households default on monthly payments. Collective revenue from or imposing sanctions on neighbours is a well-known social challenge 44 . Even with government support for removing panels from households that have defaulted on payments three times, this is often difficult for the women in practice.

Sometimes the batteries are kept inside their bedrooms, and it is not easy to access them and uninstall when the men are present. Interview with Solar Mama

The college staff also notes that the problem is larger in villages where village leaders, always men, are less supportive or even opposed to the programme.

When the village leader is not supportive of the programme, the whole community tends to be resistant. Interview with Barefoot College staff member

Instances such as these lead to gaps between those giving (the Solar Mamas) and those receiving care (the community, which fails to care for the Solar Mamas in return) 27 , emphasizing the interdependence of actors in the decentralized energy web. The college and the Zanzibar government are currently exploring alternative business models to overcome this problem, such as a one-off payment at the time of installation and giving ownership of the solar panels to the households as such, but that would undermine the affordability of service.

Challenging traditional attitudes around knowledge and expertise, the Solar Mamas training programme questioned widespread assumptions around who could be a skilled technician.

The class is practically oriented. People who have gone to school for years are amazed at how we do this with no educational background and a training of just five months. Interview with Solar Mama

By specifically engaging women with limited formal education, the college confronts gendered norms associating technological expertise with masculinity and boosts the women’s self-esteem in addition to altering community perceptions. In addition to the technical knowledge the women acquire, the ENRICHE module provides a safe space for addressing motherhood, family and gender relations and jointly reimagining and unlearning a set of regressive gendered norms surrounding work division in the households and domestic relations. Taking women’s knowledge and lived experiences, their social and cultural contexts as starting point, the facilitators ask women to voice their experience, knowledge and fears developing a sense of stronger agency, belonging and community.

The government of Zanzibar, in line with its stated mission 45 of addressing ‘harmful social and cultural norms and practices’, plays a key role in enabling socially innovative practices and transforming gendered norms, also providing trust and credibility for the programme.

When Mamas agree to join the training for five months, their husbands must sign an agreement saying they are letting their wives go for training. So, when there is a sudden change of mind, with the help of the village leader, we have dialogues with the husband or other men in the family and try to educate them about the benefits of the programme for the family. Interview with recruitment team member from Zanzibar government

Despite resistance, the political support and clearly positive benefits resulted in a slow transformation of attitudes and generated support from male partners, leading to programme expansion and sustainability. The credibility attained by the women as ‘knowers’ is an outcome of addressing the devaluation they experienced as caregivers at the household level.

I am more respected now—they see me as a solar engineer. Interview with Solar Mama.

This includes the credibility the Solar Mamas have gained in the society and their voices being heard—addressing testimonial injustice—and a shift in the community’s perception around women’s capabilities, which improves the collective interpretive resources of the community—addressing hermeneutical injustice. It also shows slow shift in attitudes of the male partners with the women’s voices being heard and valued more.

My husband has changed. My power in decision-making regarding our family has increased—we communicate well, and we discuss issues together, and when I tell him of something, he doesn’t disagree much. Interview with Solar Mama.

The community, especially men, are amazed at how a job that was predominantly done by men is done by us! Interview with Solar Mama

The training modules and increased economic independence improved the level of confidence and sense of agency for Solar Mamas, who became more assertive and demanded a place at various decision-making arenas. The communities in turn increasingly recognized them as solar engineers and people supporting the community.

The care–epistemic justice interface

The synergistic lens of care and epistemic injustice aids in articulating a feminist vision of energy transitions. Figure 2 illustrates the link between the concept of care and epistemic injustice in the Solar Mamas case study. The case shows the effectiveness of addressing the norm of men as superior knowers of technological matters through an approach that involves not just training of women to possess new skills but also collective community conscientization around the value of women’s pre-existing knowledge and capabilities.

Illustration of the combined lens of care and epistemic injustice in the Solar Mamas programme of Barefoot College Zanzibar.

For the Solar Mamas, the economic and social shift in status changed their relations with the community. Their ability to care for the technology and their communities, brought a sense of pride, joy and power. By leveraging the concept of ‘in-between spaces and in-between times’, as proposed by Arora-Jonsson, we contend that the work undertaken by the Solar Mamas in their communities erases the boundary between public (work) and private (care), between paid or unpaid work 38 .

The existence of an innovative partnership between the college and the government of Zanzibar plays a key role in the operationalization and success of the programme. Supported by both the college and the government of Zanzibar, the Solar Mamas dedicate their lives to electrifying villages and maintaining technology. In doing so, they mobilize valuable knowledge and skills from their lived experiences and technical training, offering insights essential for the care and maintenance required in decentralized energy transitions 44 . The case study also emphasizes the crucial role played by cross-scalar support networks involving, for instance, the collaboration between the state and development organizations/non-governmental organisations in facilitating gender-just energy futures.

The emphasis on techno-economic aspects of energy transitions neglects strategies for fostering positive change through social relations, local resources, innovative organizational methods and political mobilization 46 , 47 . The Solar Mamas programme serves as an illustrative example of a comprehensively designed Solar Engineer Training Program, which is embedded within the communities they come from cognizant of the gendered norms and power relations that exist. The programme addresses contextual barriers in the shape of attitudes and norms underestimating illiterate women’s capacities in addition to the technical training provided. This involves the unlearning of gendered social norms and unjust practices by the women themselves, their male partners and the wider community. In the process, the knowledge that these women already possess as caregivers, including as providers of household-level energy, also becomes valued. The transformative impacts are achieved by primarily targeting the society’s devaluation of women’s domestic care work and making visible the care for community that was already practiced but now extends to a new shape involving a desired technology 32 .

The Solar Mamas programme reflects a nuanced understanding of gendered energy care work, addressing the devaluation experienced by participants and overcoming epistemic injustice by fostering confidence, agency and a transformative shift in gender norms within their communities. The evidence suggests that interventions that centre care and knowledge in the transition from centralized electricity supply towards decentralized and locally managed provision holds potential to disrupt established gender relations and develop a new functional energy order 27 . The case illustrates the cross-scale networks and importance of alliances that help overcome or diminish resistance, and the ongoing adjustments that are necessary for viable initiatives 48 , 49 . The findings from the case study also underscore the need for further work in energy studies, which is cognizant of the care practices, gendered norms, lived realities and diverse knowledge of the communities in the Global South. This exploration is vital for comprehending the everyday experiences of individuals experiencing energy transitions.

The study of the Solar Mamas programme of Barefoot College Zanzibar was conducted in Zanzibar during October 2022. The study was informed by previous studies conducted at the Barefoot College campus in Tilonia, Rajasthan, India 40 , 41 . The case selection was preceded by online interviews with the communication team of Barefoot College International and the former chief executive officer of Barefoot College International. These interviews aided in the process of case selection. In addition, our research has also been informed by previous studies on decentralized energy systems in East Africa, which have particularly examined the gender–energy/electricity nexus 36 , 43 , 44 , 50 , 51 .

Zanzibar, a semi-autonomous province that consists of an archipelago with two main islands, has been part of the United Republic of Tanzania since 1964. It is a cultural meeting place shaped by trade and colonization, with Swahili culture emerging from African Indian Ocean encounters, and later western colonization of the African East Coast. Tourism and spice production dominate the economy. Culturally, Zanzibar distinguishes itself from the Tanzania mainland with Sunni Islam—not Christianity—as the dominant religion. Traditional gender roles as understood in Sunni Islam define women as care providers and men as breadwinners. Winther’s 36 , 51 anthropological account of Zanzibar’s energy sector shows the gendered division of energy work, with firewood, charcoal and kerosene as dominant domestic energy sources, the provision of which is largely women’s responsibility. Despite this provisioning role, Tanzanian energy policy portrays women as ‘users’, and the supply chains and infrastructure for electricity, gas and diesel are male dominated. Winther articulates that the Islam religion as practiced in Zanzibar is of a pragmatic nature despite conforming to traditional gendered roles. She argues that despite women’s roles being defined as homemakers and care providers, the precarious living conditions in rural Zanzibar often led to reliance on women’s labour outside the home for survival. Islamic rules apply to marriage and inheritance, allowing women very limited rights of ownership, and women rarely own land or houses.

In 2015, the Barefoot College Zanzibar was established through a memorandum of understanding between the Zanzibar’s’ Ministry of Empowerment, Social Welfare, Youth, Women and Children’ and ‘Barefoot College International’. The collaboration began in 2011, when 13 rural women from Zanzibari villages were trained as ‘solar engineers’ at Barefoot College, Tilonia, in India 40 , 41 . These women returned to Zanzibar and electrified more than 200 homes in rural Zanzibar. The former president of Zanzibar, Ali Mohamed Shein, visited Barefoot College in India in 2014. Impressed with the transformation happening there, he aimed to replicate the Indian model in Zanzibar and supported the establishment of Barefoot College Zanzibar in 2015. He appointed the 13 Zanzibari Solar Mamas trained in India as Solar Master Trainers in Zanzibar, who played a key role in designing the training. Set within the socio-cultural context of Zanzibar, the case is an example of how gender relations can be centred as a leverage point in the energy transitions process within traditional patriarchal orders and technology-oriented programmes that are typically ‘masculine spaces’.

The data for the case study were collected through semi-structured interviews. A total of 16 interviews were conducted, including interviews with four solar master trainers, three government representatives, three Barefoot College staff members and six Solar Mamas with the help of an interpreter. While acknowledging the limited sample size in this qualitative study, it is important to emphasize that the focus on depth over breadth enabled a thorough exploration of participants’ experiences, contributing to the richness of the data collected. Furthermore, it’s important to highlight that the inclusion of diverse stakeholders in the interviews aimed to capture a diverse range of perspectives, enriching the study despite the inherent limitation of a small sample size. Our engagements with stakeholders are guided by internationally recognized ethical principles. We approach communities transparently, clearly articulating our intentions, expectations and data-handling procedures. Before interviews or questionnaire administration, participants are briefed on data usage, storage practices and their rights under General Data Protection Regulation regulations. Consent agreements are obtained, affirming participants’ understanding and agreement to data-use protocols. Personal data collection is minimized. Data management adheres to university guidelines on data security and privacy. Additionally, we uphold responsibility by sharing preliminary findings with key informants, seeking input, feedback and clarifications to mitigate misunderstandings or misinterpretations. This iterative process ensures ethical conduct throughout the research endeavour.

As per the Swedish Ethical Review Authority and the provisions of the Act Concerning the Ethical Review of Research Involving Humans, this research falls outside the scope of projects requiring formal ethical approval. The project team at Chalmers for the User Centred Energy Systems (UsersTCP) Gender and Energy Task comprises researchers with a wealth of experience in ethical research practices. They meticulously reviewed the research design, methodology and interview questionnaires, granting approval for the study to proceed. Furthermore, ethical approval was obtained from a team at Barefoot College Zanzibar, including its director and team members, who played a pivotal role in facilitating interviews with diverse stakeholders. Supplementary Table 1 provides general information about the interviewees.

Building on previous literature and feminist theory, an interview guide was prepared, and the scope included the programme history, the set-up (pre-training period, the training process) and the programme impacts. Audio recordings from the interviews have been transcribed and translated with the help of the interview assistant. The interview material was coded with the qualitative analysis software Nvivo. We adhered to an abductive content analysis approach that iterated between pre-defined themes and concepts (deductive codes) and those emerging from the interview data (inductive codes) 52 .The analytical framework that combines the lens of care and epistemic justice emerged through this abductive process where epistemic injustice and care work were among the themes identified ahead of the study, but their intersection and interplay was informed by the analysis and empirical findings. Beyond the application of pre-defined deductive codes, an additional layer of analysis involved a comprehensive examination of the data to formulate inductive codes. For instance, the deductive codes under the theme care captured relationships between care and power, care and knowledge and energy care work. However, the analysis revealed emergent inductive codes related to care as social infrastructure and the concept of community/collaborative care. This was identified in interviews with the Solar Mamas, who highlighted the community’s perception of the services they rendered as a crucial support infrastructure. This support, in turn, facilitated extended business hours and supported school-going children in their education. Supplementary Table 2 enumerates key themes and associated codes relevant to the analysis undertaken in this study, following the structure adapted from Johnson et al. 53 .

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this study are included in the article.

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Acknowledgements

This study has been funded by the Swedish Energy Agency and is conducted under a larger project ‘Empowering all: gender in policy and implementation for achieving transitions to sustainable energy’ under the User-Centred Energy Systems (UsersTCP), part of the IEA Technology Collaboration Programme, task on Gender and Energy (project number Energimyndigheten 2020–22623). We acknowledge and thank T. Morosso for her assistance in conducting interviews in Swahili and help in the translation process. We express our gratitude to B. Geofrey, the staff of Barefoot College and all our interviewees. We would also like to thank O. Osunmuyiwa, S. Arora-Jonsson, M. Hultman and A. Åberg for their comments and inputs.

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K.M. conceived the idea for the manuscript, designed the study and formulated the overarching research goals and aims with input from H.A. K.M. conducted fieldwork in Zanzibar, collected and analysed the data and developed the theoretical framework, which H.A. helped refine. H.A. provided inputs and revisions throughout the process. K.M. wrote the first draft of the paper, which H.A. commented on and revised. Both K.M. and H.A. discussed the results and implications and collaborated on rewriting, editing and revising subsequent drafts of the paper.

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Michael, K., Ahlborg, H. A conceptual analysis of gendered energy care work and epistemic injustice through a case study of Zanzibar’s Solar Mamas. Nat Energy (2024). https://doi.org/10.1038/s41560-024-01539-1

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Maintaining an active hospital microbiology laboratory allows definitive antibiotic treatment for bacterial infections to be given in a timely manner. This would be expected to improve patient outcomes and shorten length of hospital stay. However, many hospitals in low- and middle-income countries lack access to microbiology services, and the cost-effectiveness of an active microbiology service is unknown. We constructed a decision tree and performed a cost-effectiveness model analysis to determine whether maintaining an active microbiology laboratory service would be cost-effective in Timor-Leste, a lower middle-income country. The analysis was informed by local microbiology data, local patient treatment costs, results of an expert elicitation exercise and data from literature reviews. The results indicate that there is a high probability that maintaining an active microbiology laboratory is a cost-effective intervention that would both improve patient outcomes and reduce net costs (due to reduced intensive care admissions and potential costs of resistant infections) compared to no microbiological testing, especially for the hospitalised paediatric patients with suspected primary bacteraemia. This remained true under various one-way sensitivity analyses, including when accuracy of microbiological testing is low, prevalence of bacterial infection among patients with suspected bloodstream infection was high, and prevalence of antibiotic resistance was high.

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This study was funded by the Fleming Fund Country Grant to Timor-Leste. The Fleming Fund is a UK Aid fund administered by the UK Government's Department of Health and Social Care, which supports antimicrobial resistance surveillance activities in at least 25 countries around the world.

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Published: 11 May 2024

Does a perceptual gap lead to actions against digital misinformation? A third-person effect study among medical students

Zongya Li ORCID: orcid.org/0000-0002-4479-5971 1 &
Jun Yan ORCID: orcid.org/0000-0002-9539-8466 1

BMC Public Health volume 24 , Article number: 1291 ( 2024 ) Cite this article

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We are making progress in the fight against health-related misinformation, but mass participation and active engagement are far from adequate. Focusing on pre-professional medical students with above-average medical knowledge, our study examined whether and how third-person perceptions (TPP), which hypothesize that people tend to perceive media messages as having a greater effect on others than on themselves, would motivate their actions against misinformation.

We collected the cross-sectional data through a self-administered paper-and-pencil survey of 1,500 medical students in China during April 2022.

Structural equation modeling (SEM) analysis, showed that TPP was negatively associated with medical students’ actions against digital misinformation, including rebuttal of misinformation and promotion of corrective information. However, self-efficacy and collectivism served as positive predictors of both actions. Additionally, we found professional identification failed to play a significant role in influencing TPP, while digital misinformation self-efficacy was found to broaden the third-person perceptual gap and collectivism tended to reduce the perceptual bias significantly.

Conclusions

Our study contributes both to theory and practice. It extends the third-person effect theory by moving beyond the examination of restrictive actions and toward the exploration of corrective and promotional actions in the context of misinformation., It also lends a new perspective to the current efforts to counter digital misinformation; involving pre-professionals (in this case, medical students) in the fight.

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Introduction

The widespread persistence of misinformation in the social media environment calls for effective strategies to mitigate the threat to our society [ 1 ]. Misinformation has received substantial scholarly attention in recent years [ 2 ], and solution-oriented explorations have long been a focus but the subject remains underexplored [ 3 ].

Health professionals, particularly physicians and nurses, are highly expected to play a role in the fight against misinformation as they serve as the most trusted information sources regarding medical topics [ 4 ]. However, some barriers, such as limitations regarding time and digital skills, greatly hinder their efforts to tackle misinformation on social media [ 5 ].

Medical students (i.e., college students majoring in health/medical science), in contrast to medical faculty, have a greater potential to become the major force in dealing with digital misinformation as they are not only equipped with basic medical knowledge but generally possess greater social media skills than the former generation [ 6 ]. Few studies, to our knowledge, have tried to explore the potential of these pre-professionals in tackling misinformation. Our research thus fills the gap by specifically exploring how these pre-professionals can be motivated to fight against digital health-related misinformation.

The third-person perception (TPP), which states that people tend to perceive media messages as having a greater effect on others than on themselves [ 7 ], has been found to play an important role in influencing individuals’ coping strategies related to misinformation. But empirical exploration from this line of studies has yielded contradictory results. Some studies revealed that individuals who perceived a greater negative influence of misinformation on others than on themselves were more likely to take corrective actions to debunk misinformation [ 8 ]. In contrast, some research found that stronger TPP reduced individuals’ willingness to engage in misinformation correction [ 9 , 10 ]. Such conflicting findings impel us to examine the association between the third-person perception and medical students’ corrective actions in response to misinformation, thus attempting to unveil the underlying mechanisms that promote or inhibit these pre-professionals’ engagement with misinformation.

Researchers have also identified several perceptual factors that motivate individuals’ actions against misinformation, especially efficacy-related concepts (e.g., self-efficacy and health literacy) and normative variables (e.g., subjective norms and perceived responsibility) [ 3 , 8 , 9 ]. However, most studies devote attention to the general population; little is known about whether and how these factors affect medical students’ intentions to deal with misinformation. We recruited Chinese medical students in order to study a social group that is mutually influenced by cultural norms (collectivism in Chinese society) and professional norms. Meanwhile, systematic education and training equip medical students with abundant clinical knowledge and good levels of eHealth literacy [ 5 ], which enable them to have potential efficacy in tackling misinformation. Our study thus aims to examine how medical students’ self-efficacy, cultural norms (i.e., collectivism) and professional norms (i.e., professional identification) impact their actions against misinformation.

Previous research has found self-efficacy to be a reliable moderator of optimistic bias, the tendency for individuals to consider themselves as less likely to experience negative events but more likely to experience positive events as compared to others [ 11 , 12 , 13 ]. As TPP is thought to be a product of optimistic bias, accordingly, self-efficacy should have the potential to influence the magnitude of third-person perception [ 14 , 15 ]. Meanwhile, scholars also suggest that the magnitude of TPP is influenced by social distance corollary [ 16 , 17 ]. Simply put, individuals tend to perceive those who are more socially distant from them to be more susceptible to the influence of undesirable media than those who are socially proximal [ 18 , 19 , 20 ]. From a social identity perspective, collectivism and professional identification might moderate the relative distance between oneself and others while the directions of such effects differ [ 21 , 22 ]. For example, collectivists tend to perceive a smaller social distance between self and others as “they are less likely to view themselves as distinct or unique from others” [ 23 ]. In contrast, individuals who are highly identified with their professional community (i.e., medical community) are more likely to perceive a larger social distance between in-group members (including themselves) and out-group members [ 24 ]. In this way, collectivism and professional identification might exert different effects on TPP. On this basis, this study aims to examine whether and how medical students’ perceptions of professional identity, self-efficacy and collectivism influence the magnitude of TPP and in turn influence their actions against misinformation.

Our study builds a model that reflects the theoretical linkages among self-efficacy, collectivism, professional identity, TPP, and actions against misinformation. The model, which clarifies the key antecedents of TPP and examines the mediating role of TPP, contribute to the third-person effect literature and offer practical contributions to countering digital misinformation.

Context of the study

As pre-professionals equipped with specialized knowledge and skills, medical students have been involved in efforts in health communication and promotion during the pandemic. For instance, thousands of medical students have participated in various volunteering activities in the fight against COVID-19, such as case data visualization [ 25 ], psychological counseling [ 26 ], and providing online consultations [ 27 ]. Due to the shortage of medical personnel and the burden of work, some medical schools also encouraged their students to participate in health care assistance in hospitals during the pandemic [ 28 , 29 ].

The flood of COVID-19 related misinformation has posed an additional threat to and burden on public health. We have an opportunity to address this issue and respond to the general public’s call for guidance from the medical community about COVID-19 by engaging medical students as a main force in the fight against coronavirus related misinformation.

Literature review

The third-person effect in the misinformation context.

Originally proposed by Davison [ 7 ], the third-person effect hypothesizes that people tend to perceive a greater effect of mass media on others than on themselves. Specifically, the TPE consists of two key components: the perceptual and the behavioral [ 16 ]. The perceptual component centers on the perceptual gap where individuals tend to perceive that others are more influenced by media messages than themselves. The behavioral component refers to the behavioral outcomes of the self-other perceptual gap in which people act in accordance with such perceptual asymmetry.

According to Perloff [ 30 ], the TPE is contingent upon situations. For instance, one general finding suggests that when media messages are considered socially undesirable, nonbeneficial, or involving risks, the TPE will get amplified [ 16 ]. Misinformation characterized as inaccurate, misleading, and even false, is regarded as undesirable in nature [ 31 ]. Based on this line of reasoning, we anticipate that people will tend to perceive that others would be more influenced by misinformation than themselves.

Recent studies also provide empirical evidence of the TPE in the context of misinformation [ 32 ]. For instance, an online survey of 511 Chinese respondents conducted by Liu and Huang [ 33 ] revealed that individuals would perceive others to be more vulnerable to the negative influence of COVID-19 digital disinformation. An examination of the TPE within a pre-professional group – the medical students–will allow our study to examine the TPE scholarship in a particular population in the context of tackling misinformation.

Why TPE occurs among medical students: a social identity perspective

Of the works that have provided explanations for the TPE, the well-known ones include self-enhancement [ 34 ], attributional bias [ 35 ], self-categorization theory [ 36 ], and the exposure hypothesis [ 19 ]. In this study, we argue for a social identity perspective as being an important explanation for third-person effects of misinformation among medical students [ 36 , 37 ].

The social identity explanation suggests that people define themselves in terms of their group memberships and seek to maintain a positive self-image through favoring the members of their own groups over members of an outgroup, which is also known as downward comparison [ 38 , 39 ]. In intergroup settings, the tendency to evaluate their ingroups more positively than the outgroups will lead to an ingroup bias [ 40 ]. Such an ingroup bias is typically described as a trigger for the third-person effect as individuals consider themselves and their group members superior and less vulnerable to undesirable media messages than are others and outgroup members [ 20 ].

In the context of our study, medical students highly identified with the medical community tend to maintain a positive social identity through an intergroup comparison that favors the ingroup and derogates the outgroup (i.e., the general public). It is likely that medical students consider themselves belonging to the medical community and thus are more knowledgeable and smarter than the general public in health-related topics, leading them to perceive the general public as more vulnerable to health-related misinformation than themselves. Accordingly, we propose the following hypothesis:

H1: As medical students’ identification with the medical community increases, the TPP concerning digital misinformation will become larger.

What influences the magnitude of TPP

Previous studies have demonstrated that the magnitude of the third-person perception is influenced by a host of factors including efficacy beliefs [ 3 ] and cultural differences in self-construal [ 22 , 23 ]. Self-construal is defined as “a constellation of thoughts, feelings, and actions concerning the relationship of the self to others, and the self as distinct from others” [ 41 ]. Markus and Kitayama (1991) identified two dimensions of self-construal: Independent and interdependent. Generally, collectivists hold an interdependent view of the self that emphasizes harmony, relatedness, and places importance on belonging, whereas individualists tend to have an independent view of the self and thus view themselves as distinct and unique from others [ 42 ]. Accordingly, cultural values such as collectivism-individualism should also play a role in shaping third-person perception due to the adjustment that people make of the self-other social identity distance [ 22 ].

Set in a Chinese context aiming to explore the potential of individual-level approaches to deal with misinformation, this study examines whether collectivism (the prevailing cultural value in China) and self-efficacy (an important determinant of ones’ behavioral intentions) would affect the magnitude of TPP concerning misinformation and how such impact in turn would influence their actions against misinformation.

The impact of self-efficacy on TPP

Bandura [ 43 ] refers to self-efficacy as one’s perceived capability to perform a desired action required to overcome barriers or manage challenging situations. He also suggests understanding self-efficacy as “a differentiated set of self-beliefs linked to distinct realms of functioning” [ 44 ]. That is to say, self-efficacy should be specifically conceptualized and operationalized in accordance with specific contexts, activities, and tasks [ 45 ]. In the context of digital misinformation, this study defines self-efficacy as one’s belief in his/her abilities to identify and verify misinformation within an affordance-bounded social media environment [ 3 ].

Previous studies have found self-efficacy to be a reliable moderator of biased optimism, which indicates that the more efficacious individuals consider themselves, the greater biased optimism will be invoked [ 12 , 23 , 46 ]. Even if self-efficacy deals only with one’s assessment of self in performing a task, it can still create the other-self perceptual gap; individuals who perceive a higher self-efficacy tend to believe that they are more capable of controlling a stressful or challenging situation [ 12 , 14 ]. As such, they are likely to consider themselves less vulnerable to negative events than are others [ 23 ]. That is, individuals with higher levels of self-efficacy tend to underestimate the impact of harmful messages on themselves, thereby widening the other-self perceptual gap.

In the context of fake news, which is closely related to misinformation, scholars have confirmed that fake news efficacy (i.e., a belief in one’s capability to evaluate fake news [ 3 ]) may lead to a larger third-person perception. Based upon previous research evidence, we thus propose the following hypothesis:

H2: As medical students’ digital misinformation self-efficacy increases, the TPP concerning digital misinformation will become larger.

The influence of collectivism on TPP

Originally conceptualized as a societal-level construct [ 47 ], collectivism reflects a culture that highlights the importance of collective goals over individual goals, defines the self in relation to the group, and places great emphasis on conformity, harmony and interdependence [ 48 ]. Some scholars propose to also examine cultural values at the individual level as culture is embedded within every individual and could vary significantly among individuals, further exerting effects on their perceptions, attitudes, and behaviors [ 49 ]. Corresponding to the construct at the macro-cultural level, micro-psychometric collectivism which reflects personality tendencies is characterized by an interdependent view of the self, a strong sense of other-orientation, and a great concern for the public good [ 50 ].

A few prior studies have indicated that collectivism might influence the magnitude of TPP. For instance, Lee and Tamborini [ 23 ] found that collectivism had a significant negative effect on the magnitude of TPP concerning Internet pornography. Such an impact can be understood in terms of biased optimism and social distance. Collectivists tend to view themselves as an integral part of a greater social whole and consider themselves less differentiated from others [ 51 ]. Collectivism thus would mitigate the third-person perception due to a smaller perceived social distance between individuals and other social members and a lower level of comparative optimism [ 22 , 23 ]. Based on this line of reasoning, we thus propose the following hypothesis:

H3: As medical students’ collectivism increases, the TPP concerning digital misinformation will become smaller.

Behavioral consequences of TPE in the misinformation context

The behavioral consequences trigged by TPE have been classified into three categories: restrictive actions refer to support for censorship or regulation of socially undesirable content such as pornography or violence on television [ 52 ]; corrective action is a specific type of behavior where people seek to voice their own opinions and correct the perceived harmful or ambiguous messages [ 53 ]; promotional actions target at media content with desirable influence, such as advocating for public service announcements [ 24 ]. In a word, restriction, correction and promotion are potential behavioral outcomes of TPE concerning messages with varying valence of social desirability [ 16 ].

Restrictive action as an outcome of third-person perceptual bias (i.e., the perceptual component of TPE positing that people tend to perceive media messages to have a greater impact on others than on themselves) has received substantial scholarly attention in past decades; scholars thus suggest that TPE scholarship to go beyond this tradition and move toward the exploration of corrective and promotional behaviors [ 16 , 24 ]. Moreover, individual-level corrective and promotional actions deserve more investigation specifically in the context of countering misinformation, as efforts from networked citizens have been documented as an important supplement beyond institutional regulations (e.g., drafting policy initiatives to counter misinformation) and platform-based measures (e.g., improving platform algorithms for detecting misinformation) [ 8 ].

In this study, corrective action specifically refers to individuals’ reactive behaviors that seek to rectify misinformation; these include such actions as debunking online misinformation by commenting, flagging, or reporting it [ 3 , 54 ]. Promotional action involves advancing correct information online, including in response to misinformation that has already been disseminated to the public [ 55 ].

The impact of TPP on corrective and promotional actions

Either paternalism theory [ 56 ] or the protective motivation theory [ 57 ] can act as an explanatory framework for behavioral outcomes triggered by third-person perception. According to these theories, people act upon TPP as they think themselves to know better and feel obligated to protect those who are more vulnerable to negative media influence [ 58 ]. That is, corrective and promotional actions as behavioral consequences of TPP might be driven by a protective concern for others and a positive sense of themselves.

To date, several empirical studies across contexts have examined the link between TPP and corrective actions. Koo et al. [ 8 ], for instance, found TPP was not only positively related to respondents’ willingness to correct misinformation propagated by others, but also was positively associated with their self-correction. Other studies suggest that TPP motivates individuals to engage in both online and offline corrective political participation [ 59 ], give a thumbs down to a biased story [ 60 ], and implement corrective behaviors concerning “problematic” TV reality shows [ 16 ]. Based on previous research evidence, we thus propose the following hypothesis:

H4: Medical students with higher degrees of TPP will report greater intentions to correct digital misinformation.

Compared to correction, promotional behavior has received less attention in the TPE research. Promotion commonly occurs in a situation where harmful messages have already been disseminated to the public and others appear to have been influenced by these messages, and it serves as a remedial action to amplify messages with positive influence which may in turn mitigate the detrimental effects of harmful messages [ 16 ].

Within this line of studies, however, empirical studies provide mixed findings. Wei and Golan [ 24 ] found a positive association between TPP of desirable political ads and promotional social media activism such as posting or linking the ad on their social media accounts. Sun et al. [ 16 ] found a negative association between TPP regarding clarity and community-connection public service announcements (PSAs) and promotion behaviors such as advocating for airing more PSAs in TV shows.

As promotional action is still underexplored in the TPE research, and existing evidence for the link between TPP and promotion is indeed mixed, we thus propose an exploratory research question:

RQ1: What is the relationship between TPP and medical students’ intentions to promote corrective information?

The impact of self-efficacy and collectivism on actions against misinformation

According to social cognitive theory, people with higher levels of self-efficacy tend to believe they are competent and capable and are more likely to execute specific actions [ 43 ]. Within the context of digital misinformation, individuals might become more willing to engage in misinformation correction if they have enough knowledge and confidence to evaluate information, and possess sufficient skills to verify information through digital tools and services [ 61 ].

Accordingly, we assumed medical students with higher levels of digital misinformation self-efficacy would be likely to become more active in the fight against misinformation.

H5: Medical students with higher levels of digital misinformation self-efficacy will report greater intentions to (a) correct misinformation and (b) promote corrective information on social media.

Social actions of collectivists are strongly guided by prevailing social norms, collective responsibilities, and common interest, goals, and obligations [ 48 ]. Hence, highly collectivistic individuals are more likely to self-sacrifice for group interests and are more oriented toward pro-social behaviors, such as adopting pro-environmental behaviors [ 62 ], sharing knowledge [ 23 ], and providing help for people in need [ 63 ].

Fighting against misinformation is also considered to comprise altruism, especially self-engaged corrective and promotional actions, as such actions are costly to the actor (i.e., taking up time and energy) but could benefit the general public [ 61 ]. Accordingly, we assume collectivism might play a role in prompting people to engage in reactive behaviors against misinformation.

It is also noted that collectivist values are deeply rooted in Chinese society and were especially strongly advocated during the outbreak of COVID-19 with an attempt to motivate prosocial behaviors [ 63 ]. Accordingly, we expected that the more the medical students were oriented toward collectivist values, the more likely they would feel personally obliged and normatively motivated to engage in misinformation correction. However, as empirical evidence was quite limited, we proposed exploratory research questions:

RQ2: Will medical students with higher levels of collectivism report greater intentions to (a) correct misinformation and (b) promote corrective information on social media?

The theoretical model

To integrate both the antecedents and consequences of TPP, we proposed a theoretical model (as shown in Fig. 1 ) to examine how professional identification, self-efficacy and collectivism would influence the magnitude of TPP, and how such impact would in turn influence medical students’ intentions to correct digital misinformation and promote corrective information. Thus, RQ3 was proposed:

RQ3: Will the TPP mediate the impact of self-efficacy and collectivism on medical students’ intentions to (a) correct misinformation, and (b) promote corrective information on social media? Fig. 1 The proposed theoretical model. DMSE = Digital Misinformation Self-efficacy; PIMC = Professional Identification with Medical Community; ICDM = Intention to Correct Digital Misinformation; IPCI = Intention to Promote Corrective Information Full size image

To examine the proposed hypotheses, this study utilized cross-sectional survey data from medical students in Tongji Medical College (TJMC) of China. TJMC is one of the birthplaces of Chinese modern medical education and among the first universities and colleges that offer eight-year curricula on clinical medicine. Further, TJMC is located in Wuhan, the epicenter of the initial COVID-19 outbreaks, thus its students might find the pandemic especially relevant – and threatening – to them.

The survey instrument was pilot tested using a convenience sample of 58 respondents, leading to minor refinements to a few items. Upon approval from the university’s Institutional Research Board (IRB), the formal investigation was launched in TJMC during April 2022. Given the challenges of reaching the whole target population and acquiring an appropriate sampling frame, this study employed purposive and convenience sampling.

We first contacted four school counselors as survey administrators through email with a letter explaining the objective of the study and requesting cooperation. All survey administrators were trained by the principal investigator to help with the data collection in four majors (i.e., basic medicine, clinical medicine, nursing, and public health). Paper-and-pencil questionnaires were distributed to students on regular weekly departmental meetings of each major as students in all grades (including undergraduates, master students, and doctoral students) were required to attend the meeting. The projected time of completion of the survey was approximately 10–15 min. The survey administrators indicated to students that participation was voluntary, their responses would remain confidential and secure, and the data would be used only for academic purposes. Though a total of 1,500 participants took the survey, 17 responses were excluded from the analysis as they failed the attention filters. Ultimately, a total of 1,483 surveys were deemed valid for analysis.

Of the 1,483 respondents, 624 (42.10%) were men and 855 (57.70%) were women, and four did not identify gender. The average age of the sample was 22.00 ( SD = 2.54, ranging from 17 to 40). Regarding the distribution of respondents’ majors, 387 (26.10%) were in basic medicine, 390 (26.30%) in clinical medicine, 307 (20.70%) in nursing, and 399 (26.90%) in public health. In terms of university class, 1,041 (70.40%) were undergraduates, 291 (19.70%) were working on their master degrees, 146 (9.90%) were doctoral students, and five did not identify their class data.

Measurement of key variables

Perceived effects of digital misinformation on oneself and on others.

Three modified items adapted from previous research [ 33 , 64 ] were employed to measure perceived effects of digital misinformation on oneself. Respondents were asked to indicate to what extent they agreed with the following: (1) I am frequently concerned that the information about COVID-19 I read on social media might be false; (2) Misinformation on social media might misguide my understanding of the coronavirus; (3) Misinformation on social media might influence my decisions regarding COVID-19. The response categories used a 7-point scale, where 1 meant “strongly disagree” and 7 meant “strongly agree.” The measure of perceived effects of digital misinformation on others consisted of four parallel items with the same statement except replacing “I” and “my” with “the general others” and “their”. The three “self” items were averaged to create a measure of “perceived effects on oneself” ( M = 3.98, SD = 1.49, α = 0.87). The three “others” items were also added and averaged to form an index of “perceived effects on others” ( M = 4.62, SD = 1.32, α = 0.87).

The perceived self-other disparity (TPP)

TPP was derived by subtracting perceived effects on oneself from perceived effects on others.

Professional identification with medical community

Professional identification was measured using a three item, 7-point Likert-type scale (1 = strongly disagree , 7 = strongly agree ) adapted from previous studies [ 65 , 66 ] by asking respondents to indicate to what extent they agreed with the following statements: (1) I would be proud to be a medical staff member in the future; (2) I am committed to my major; and (3) I will be in an occupation that matches my current major. The three items were thus averaged to create a composite measure of professional identification ( M = 5.34, SD = 1.37, α = 0.88).

Digital misinformation self-efficacy

Modified from previous studies [ 3 ], self-efficacy was measured with three items. Respondents were asked to indicate on a 7-point Linkert scale from 1 (strongly disagree) to 7 (strongly agree) their agreement with the following: (1) I think I can identify misinformation relating to COVID-19 on social media by myself; (2) I know how to verify misinformation regarding COVID-19 by using digital tools such as Tencent Jiaozhen Footnote 1 and Piyao.org.cn Footnote 2 ; (3) I am confident in my ability to identify digital misinformation relating to COVID-19. A composite measure of self-efficacy was constructed by averaging the three items ( M = 4.38, SD = 1.14, α = 0.77).

Collectivism

Collectivism was measured using four items adapted from previous research [ 67 ], in which respondents were asked to indicate their agreement with the following statements on a 7-point scale, from 1 (strongly disagree) to 7 (strongly agree): (1) Individuals should sacrifice self-interest for the group; (2) Group welfare is more important than individual rewards; (3) Group success is more important than individual success; and (4) Group loyalty should be encouraged even if individual goals suffer. Therefore, the average of the four items was used to create a composite index of collectivism ( M = 4.47, SD = 1.30, α = 0.89).

Intention to correct digital misinformation

We used three items adapted from past research [ 68 ] to measure respondents’ intention to correct misinformation on social media. All items were scored on a 7-point scale from 1 (very unlikely) to 7 (very likely): (1) I will post a comment saying that the information is wrong; (2) I will message the person who posts the misinformation to tell him/her the post is wrong; (3) I will track the progress of social media platforms in dealing with the wrong post (i.e., whether it’s deleted or corrected). A composite measure of “intention to correct digital misinformation” was constructed by adding the three items and dividing by three ( M = 3.39, SD = 1.43, α = 0.81).

Intention to promote corrective information

On a 7-point scale ranging from 1 (very unlikely) to 7 (very likely), respondents were asked to indicate their intentions to (1) Retweet the corrective information about coronavirus on my social media account; (2) Share the corrective information about coronavirus with others through Social Networking Services. The two items were averaged to create a composite measure of “intention to promote corrective information” ( M = 4.60, SD = 1.68, r = 0.77).

Control variables

We included gender, age, class (1 = undergraduate degree; 2 = master degree; 3 = doctoral degree), and clinical internship (0 = none; 1 = less than 0.5 year; 2 = 0.5 to 1.5 years; 3 = 1.5 to 3 years; 4 = more than 3 years) as control variables in the analyses. Additionally, coronavirus-related information exposure (i.e., how frequently they were exposed to information about COVID-19 on Weibo, WeChat, and QQ) and misinformation exposure on social media (i.e., how frequently they were exposed to misinformation about COVID-19 on Weibo, WeChat, and QQ) were also assessed as control variables because previous studies [ 69 , 70 ] had found them relevant to misinformation-related behaviors. Descriptive statistics and bivariate correlations between main variables were shown in Table 1 .

Statistical analysis

We ran confirmatory factor analysis (CFA) in Mplus (version 7.4, Muthén & Muthén, 1998) to ensure the construct validity of the scales. To examine the associations between variables and tested our hypotheses, we performed structural equation modeling (SEM). Mplus was chosen over other SEM statistical package mainly because the current data set included some missing data, and the Mplus has its strength in handling missing data using full-information maximum likelihood imputation, which enabled us to include all available data [ 71 , 72 ]. Meanwhile, Mplus also shows great flexibility in modelling when simultaneously handling continuous, categorical, observed, and latent variables in a variety of models. Further, Mplus provides a variety of useful information in a concise manner [ 73 ].

Table 2 shows the model fit information for the measurement and structural models. Five latent variables were specified in the measurement model. To test the measurement model, we examined the values of Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE) (Table 1 ). Cronbach’s alpha values ranged from 0.77 to 0.89. The CRs, which ranged from 0.78 to 0.91, exceeded the level of 0.70 recommended by Fornell (1982) and thus confirmed the internal consistency. The AVE estimates, which ranged from 0.54 to 0.78, exceeded the 0.50 lower limit recommended by Fornell and Larcker (1981), and thus supported convergent validity. All the square roots of AVE were greater than the off-diagonal correlations in the corresponding rows and columns [ 74 ]. Therefore, discriminant validity was assured. In a word, our measurement model showed sufficient convergence and discriminant validity.

Five model fit indices–the relative chi-square ratio (χ 2 / df ), the comparative fit index (CFI), the Tucker–Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root-mean-square residual (SRMR) were used to assess the model. Specifically, the normed chi-square between 1 and 5 is acceptable [ 75 ]. TLI and CFI over 0.95 are considered acceptable, SRMR value less than 0.08 and RMSEA value less than 0.06 indicate good fit [ 76 ]. Based on these criteria, the model was found to have an acceptable fit to the data.

Figure 2 presents the results of our hypothesized model. H1 was rejected as professional identification failed to predict TPP ( β = 0.06, p > 0.05). Self-efficacy was positively associated with TPP ( β = 0.14, p < 0.001) while collectivism was negatively related to TPP ( β = -0.10, p < 0.01), lending support to H2 and H3.

Note. N = 1,483. The coefficients of relationships between latent variables are standardized beta coefficients. Significant paths are indicated by solid line; non-significant paths are indicated by dotted lines. * p < .05, ** p < .01; *** p < .001. DMSE = Digital Misinformation Self-efficacy; PIMC = Professional Identification with Medical Community; ICDM = Intention to Correct Digital Misinformation; IPCI = Intention to Promote Corrective Information

H4 posited that medical students with higher degrees of TPP would report greater intentions to correct digital misinformation. However, we found a negative association between TPP and intentions to correct misinformation ( β = -0.12, p < 0.001). H4 was thus rejected. Regarding RQ1, results revealed that TPP was negatively associated with intentions to promote corrective information ( β = -0.08, p < 0.05).

Further, our results supported H5 as we found that self-efficacy had a significant positive relationship with corrective intentions ( β = 0.18, p < 0.001) and promotional intentions ( β = 0.32, p < 0.001). Collectivism was also positively associated with intentions to correct misinformation ( β = 0.14, p < 0.001) and promote corrective information ( β = 0.20, p < 0.001), which answered RQ2.

Regarding RQ3 (see Table 3 ), TPP significantly mediated the relationship between self-efficacy and intentions to correct misinformation ( β = -0.016), as well as the relationship between self-efficacy and intentions to promote corrective information ( β = -0.011). However, TPP failed to mediate either the association between collectivism and corrective intentions ( β = 0.011, ns ) or the association between collectivism and promotional intentions ( β = 0.007, ns ).

Recent research has highlighted the role of health professionals and scientists in the fight against misinformation as they are considered knowledgeable, ethical, and reliable [ 5 , 77 ]. This study moved a step further by exploring the great potential of pre-professional medical students to tackle digital misinformation. Drawing on TPE theory, we investigated how medical students perceived the impact of digital misinformation, the influence of professional identification, self-efficacy and collectivism on these perceptions, and how these perceptions would in turn affect their actions against digital misinformation.

In line with prior studies [ 3 , 63 ], this research revealed that self-efficacy and collectivism played a significant role in influencing the magnitude of third-person perception, while professional identification had no significant impact on TPP. As shown in Table 1 , professional identification was positively associated with perceived effects of misinformation on oneself ( r = 0.14, p < 0.001) and on others ( r = 0.20, p < 0.001) simultaneously, which might result in a diminished TPP. What explains a shared or joint influence of professional identification on self and others? A potential explanation is that even medical staff had poor knowledge about the novel coronavirus during the initial outbreak [ 78 ]. Accordingly, identification with the medical community was insufficient to create an optimistic bias concerning identifying misinformation about COVID-19.

Our findings indicated that TPP was negatively associated with medical students’ intentions to correct misinformation and promote corrective information, which contradicted our hypotheses but was consistent with some previous TPP research conducted in the context of perceived risk [ 10 , 79 , 80 , 81 ]. For instance, Stavrositu and Kim (2014) found that increased TPP regarding cancer risk was negatively associated with behavioral intentions to engage in further cancer information search/exchange, as well as to adopt preventive lifestyle changes. Similarly, Wei et al. (2008) found concerning avian flu news that TPP negatively predicted the likelihood of engaging in actions such as seeking relevant information and getting vaccinated. In contrast, the perceived effects of avian flu news on oneself emerged as a positive predictor of intentions to take protective behavior.

Our study shows a similar pattern as perceived effects of misinformation on oneself were positively associated with intentions to correct misinformation ( r = 0.06, p < 0.05) and promote corrective information ( r = 0.10, p < 0.001, See Table 1 ). While the reasons for the behavioral patterns are rather elusive, such findings are indicative of human nature. When people perceive misinformation-related risk to be highly personally relevant, they do not take chances. However, when they perceive others to be more vulnerable than themselves, a set of sociopsychological dynamics such as self-defense mechanism, positive illusion, optimistic bias, and social comparison provide a restraint on people’s intention to engage in corrective and promotional actions against misinformation [ 81 ].

In addition to the indirect effects via TPP, our study also revealed that self-efficacy and collectivism serve as direct and powerful drivers of corrective and promotive actions. Consistent with previous literature [ 61 , 68 ], individuals will be more willing to engage in social corrections of misinformation if they possess enough knowledge, skills, abilities, and resources to identify misinformation, as correcting misinformation is difficult and their effort would not necessarily yield positive outcomes. Collectivists are also more likely to engage in misinformation correction as they are concerned for the public good and social benefits, aiming to protect vulnerable people from being misguided by misinformation [ 82 ].

This study offers some theoretical advancements. First, our study extends the TPE theory by moving beyond the examination of restrictive actions and toward the exploration of corrective and promotional actions in the context of misinformation. This exploratory investigation suggests that self-other asymmetry biased perception concerning misinformation did influence individuals’ actions against misinformation, but in an unexpected direction. The results also suggest that using TPP alone to predict behavioral outcomes was deficient as it only “focuses on differences between ‘self’ and ‘other’ while ignoring situations in which the ‘self’ and ‘other’ are jointly influenced” [ 83 ]. Future research, therefore, could provide a more sophisticated understanding of third-person effects on behavior by comparing the difference of perceived effects on oneself, perceived effects on others, and the third-person perception in the pattern and strength of the effects on behavioral outcomes.

Moreover, institutionalized corrective solutions such as government and platform regulation are non-exhaustive [ 84 , 85 ]; it thus becomes critical to tap the great potential of the crowd to engage in the fight against misinformation [ 8 ] while so far, research on the motivations underlying users’ active countering of misinformation has been scarce. The current paper helps bridge this gap by exploring the role of self-efficacy and collectivism in predicting medical students’ intentions to correct misinformation and promote corrective information. We found a parallel impact of the self-ability-related factor and the collective-responsibility-related factor on intentions to correct misinformation and promote corrective information. That is, in a collectivist society like China, cultivating a sense of collective responsibility and obligation in tackling misinformation (i.e., a persuasive story told with an emphasis on collective interests of social corrections of misinformation), in parallel with systematic medical education and digital literacy training (particularly, handling various fact-checking tools, acquiring Internet skills for information seeking and verification) would be effective methods to encourage medical students to engage in active countering behaviors against misinformation. Moreover, such an effective means of encouraging social corrections of misinformation might also be applied to the general public.

In practical terms, this study lends new perspectives to the current efforts in dealing with digital misinformation by involving pre-professionals (in this case, medical students) into the fight against misinformation. As digital natives, medical students usually spend more time online, have developed sophisticated digital competencies and are equipped with basic medical knowledge, thus possessing great potential in tackling digital misinformation. This study further sheds light on how to motivate medical students to become active in thwarting digital misinformation, which can help guide strategies to enlist pre-professionals to reduce the spread and threat of misinformation. For example, collectivism education in parallel with digital literacy training would help increase medical students’ sense of responsibility for and confidence in tackling misinformation, thus encouraging them to engage in active countering behaviors.

This study also has its limitations. First, the cross-sectional survey study did not allow us to justify causal claims. Granted, the proposed direction of causality in this study is in line with extant theorizing, but there is still a possibility of reverse causal relationships. To establish causality, experimental research or longitudinal studies would be more appropriate. Our second limitation lies in the generalizability of our findings. With the focus set on medical students in Chinese society, one should be cautious in generalizing the findings to other populations and cultures. For example, the effects of collectivism on actions against misinformation might differ in Eastern and Western cultures. Further studies would benefit from replication in diverse contexts and with diverse populations to increase the overall generalizability of our findings.

Drawing on TPE theory, our study revealed that TPP failed to motivate medical students to correct misinformation and promote corrective information. However, self-efficacy and collectivism were found to serve as direct and powerful drivers of corrective and promotive actions. Accordingly, in a collectivist society such as China’s, cultivating a sense of collective responsibility in tackling misinformation, in parallel with efficient personal efficacy interventions, would be effective methods to encourage medical students, even the general public, to actively engage in countering behaviors against misinformation.

Availability of data and materials

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Tencent Jiaozhen Fact-Checking Platform which comprises the Tencent information verification tool allow users to check information authenticity through keyword searching. The tool is updated on a daily basis and adopts a human-machine collaboration approach to discovering, verifying, and refuting rumors and false information. For refuting rumors, Tencent Jiaozhen publishes verified content on the homepage of Tencent's rumor-refuting platform, and uses algorithms to accurately push this content to users exposed to the relevant rumors through the WeChat dispelling assistant.

Piyao.org.cn is hosted by the Internet Illegal Information Reporting Center under the Office of the Central Cyberspace Affairs Commission and operated by Xinhuanet.com. The platform is a website that collects statements from Twitter-like services, news portals and China's biggest search engine, Baidu, to refute online rumors and expose the scams of phishing websites. It has integrated over 40 local rumor-refuting platforms and uses artificial intelligence to identify rumors.

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Acknowledgements

We thank all participants and staff working for the project.

This work was supported by Humanities and Social Sciences Youth Foundation of the Ministry of Education of China (Grant No. 21YJC860012).

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Li, Z., Yan, J. Does a perceptual gap lead to actions against digital misinformation? A third-person effect study among medical students. BMC Public Health 24 , 1291 (2024). https://doi.org/10.1186/s12889-024-18763-9

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