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Advances on Data Management and Information Systems

• Published: 02 March 2022
• Volume 24 , pages 1–10, ( 2022 )

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• Jérôme Darmont 1 ,
• Boris Novikov 2 ,
• Robert Wrembel 3 &
• Ladjel Bellatreche 4  

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1 Introduction

The research and technological area of data management encompasses various concepts, techniques, algorithms and technologies, including data modeling, data integration and ingestion, transactional data management, query languages, query optimization, physical data storage, data structures, analytical techniques (including On-Line Analytical Processing – OLAP), as well as service creation and orchestration (Garcia-Molina et al., 2009 ). Data management technologies are core components of every information system, either centralized or distributed, deployed in an on-premise hardware architecture or in a cloud ecosystem. Data management technologies have been used in commercial, mature products for decades. They were originally developed for managing structured data (mainly expressed in the relational data model).

Yet, the ubiquitous big data (Azzini et al., 2021 ) require development of new data management techniques, suitable for the variety of data formats (from structured, through semi-structured, to unstructured), overwhelming data volumes and velocity of big data generation. These new techniques draw upon the concepts applied to managing relational data.

One of the most frequently used data format for big data is based on graphs, which are a natural way of representing relationships between entities, e.g., knowledge, social connections and components of a complex system. Such data not only need to be efficiently stored but also efficiently analyzed. Therefore, some OLAP-like analysis approaches from graph data have been recently proposed, e.g., Chen et al. ( 2020 ), Ghrab et al. ( 2018 ), Ghrab et al. ( 2021 ), and Schuetz et al. ( 2021 ). Thus, combining graph and OLAP technologies offers ways of analyzing graphs in a manner already well accepted by the industry (Richardson et al., 2021 ).

The complexity of ecosystems for managing big data results in challenges for orchestrating these components and in optimizing their performance, as there are too many parameters in each system to be manually tuned by a human administrator. Thus, more and more frequently, machine learning techniques are applied to performance optimization, e.g., Hernández et al. ( 2018 ) and Witt et al. ( 2019 ). Conversely, data management techniques are used to solve challenges in machine learning, such as building end-to-end data processing pipelines (Romero and Wrembel, 2020 ).

In this editorial to the special section of Information Systems Frontiers, we outline research problems in graph processing, OLAP and machine learning. These problems are addressed by the papers in this special section.

2 Selected Research Problems in Data Management and Information Systems

2.1 graph processing.

Graph processing algorithms have been attracting attention of researchers since the 1950’s. Several knowledge representation techniques (such as semantic networks) studied in the 1970’s utilize graph structures, including applications to rule-based systems (Griffith, 1982 ), data structures for efficient processing (Moldovan, 1984 )), and several other aspects.

The concepts of semantic networks served as a base for the Semantic Web and evolved into knowledge graphs, also known as knowledge bases. Processing of large distributed RDF knowledge bases with the SPARQL language is addressed in Peng et al. ( 2016 ).

Graph-based models become a natural choice for a representation of semi-structured data (McHugh et al., 1997 ). Graph representations proved their usefulness for modeling hypertexts, including the World Wide Web (Meusel et al., 2014 ). Documents (e.g., Web pages) are mapped to vertices, while directed edges represent links. The graph representations of the WWW provided several features (such as page rank and simrank) for deep analysis of its structure and definition. Sources of large graphs include social networks, bioinformatics, road networks, and other application domains.

The need to store and process large graphs supports growing interest to graph databases. An overview of several aspects of graph databases can be found in (Deutsch and Papakonstantinou, 2018 ). Typically, such databases can store graph vertices and nodes, labeled with sets of attributes. A widespread opinion states that graph databases provide more powerful modeling features than the relational model used in traditional relational databases. This is doubtful, as a relational database schema (represented for example as an ER model) is also a graph (Pokorný, 2016 ). Actually, the advantage of graph databases is that the expensive and time-consuming modeling can be pushed forward to later phases of the information system lifecycle, providing more options for rapid prototyping and similar application development methodologies (Brdjanin et al., 2018 ).

A need of highly expressive tools for graph processing specification triggered a number of efforts in declarative query languages design. A step toward the standardization of graph query languages (Angles et al., 2018 ) is focused on providing a balance between high expressiveness and computational performance, avoiding constructs that may result in unacceptable computation complexity. The GSQL graph query language (Deutsch et al., 2020 ) supports the specification of complex analytical queries over graphs, including pattern matching and aggregations. A comparison of different graph processing techniques, available in the Neo4j graph database management system, can be found in Holzschuher and Peinl ( 2013 ).

Several similarities can be found between relational and graph declarative query languages: as soon as sets of labeled nodes or edges are produced as intermediate results, the remaining processing is typically expressed in terms of relational operations. The most significant differences between graph and relational database query languages follow.

Graph traversal (implicit and rarely used in relational languages) requires the intensive use of recursion and an efficient implementation in graph databases.

Graph query languages provide support for computationally complex processing, such as weighted shortest path search, potentially with additional constraints.

Locality of data placement is essential for high performance of relational systems. Data placement in graph databases is much more complex and often results in poor performance when the size of the database exceeds the available main memory of a single server.

The items listed above are inter-related: the performance of graph processing depends on locality needed for efficient traversing of a graph. However, traversing depends on the problem being solved. A generic approach is to rely on certain graph properties to optimize the storage of graph nodes and edges, that is, graph partitioning.

2.2 On-Line Analytical Processing

The term “On-Line Analytical Processing” (OLAP) was coined by Edgar F. Codd in 1993 (Codd et al., 1993 ). OLAP is defined in contrast to operational database systems that run On-Line Transactional Processing (OLTP). In OLTP, data representing the current state of information may be frequently modified and are interrogated through relatively simple queries. OLAP’s data are typically sourced from one or several OLTP databases, consolidated and historicized for decision-support purposes. They are seldom modified and are queried by complex, analytical queries that run over large data volumes.

Conceptually, OLAP rests on a metaphor that is easy to grasp by business users: the (hyper)cube. Facts constituted of numerical Key Performance Indicators (KPIs), e.g., product sales, are analysis subjects. They are viewed as points in a multidimensional space whose dimensions are analysis axes, e.g., time, store, salesperson, etc. Dimensions may also have hierarchies, e.g., \(store \rightarrow city \rightarrow state\) . Thus, dimensions represent the coordinates of facts in the multidimensional space.

In the 1990’s, OLAP research mainly focused on designing efficient logical and physical models, synthetically surveyed by Vassiliadis and Sellis ( 1999 ). Relational OLAP (ROLAP) relies on storing data in time-tested relational Database Management Systems (DBMSs), complemented with new, OLAP-specific operators and queries available in SQL99. ROLAP is cheap and easy to implement, can handle large data volumes, and schema evolution is relatively easy. However, ROLAP induces numerous, costly joints that hinder query performance, and analysis results are not suitable to end-users, i.e., business users, and thus must be reformatted.

In contrast, Multidimensional OLAP (MOLAP) sticks to the cube metaphor. Hypercubes are natively stored in multidimensional tables, allowing quick aggregate computations. However, it turned out that MOLAP systems and languages (e.g., MDX) were in majority proprietary and difficult to implement. Moreover, data volume is limited to the RAM size and a cube can be quite sparse, wasting memory. Eventually, refreshing the system is limited, inducing full and costly periodical reconstructions.

Eventually, Hybrid OLAP (HOLAP) was proposed as the best of both worlds (Salka, 1998 ), by storing atomic data in a relational DBMS and aggregated data in MOLAP cubes, thus achieving a good cost/performance tradeoff on large data volumes. However, HOLAP is difficult to implement and neither as fast as MOLAP nor as scalable as ROLAP. Later on, in 2014, Gartner introduced the Hybrid Transaction/Analytical Processing (HTAP Footnote 1 ), where an in-memory DBMS helps process OLTP and OLAP simultaneously, which allows transactional data to be quickly available for analytics and induces fast, distributed query computation while avoiding data redundancy. However, this is a complex and drastic change in decision-support architectures.

After OLAP pioneers, many lines of research went on for more than fifteen years, which can be classified in two trends. In the first trend, OLAP is adapted to particular data formats. One of the most prominent of such adaptations is probably Spatial OLAP (SOLAP Han, 2017 ), where OLAP is applied on spatial (and even spatio-temporal) data, allowing for example to zoom and dezoom (i.e., drill-down and roll-up in terms of OLAP operations) spatial representations such as maps. Another well-researched adaptation was XML-OLAP (also called XOLAP), which allows OLAP on semi-structured data. Related approaches are surveyed in Mahboubi et al. ( 2009 ). Other examples include OLAP on trajectory data (Marketos and Theodoridis, 2010 ) and mobile OLAP (Maniatis, 2004 ).

In the second trend, OLAP is hybridized with other techniques for specific purposes. Quite quickly, OLAP was associated with data mining, with OLAP providing data navigation and identifying a subset of a cube; and data mining featuring association, classification, prediction, clustering, and sequencing on this data subset (Han, 1997 ).

With the Web becoming an important source of data, OLAP systems could not rely only on internal data any more and had to discover external, Web data, as well as their semantics. This issue was addressed with the help of Semantic Web (SW) technologies that support inference and reasoning on data. An extensive survey covers this research trend (Abelló et al., 2015 ). OLAP was also combined with information networks akin to social media, in the sense that they can be represented by very similar graphs. A comprehensive survey of the so-called Graph OLAP, with a focus on bibliographic data analysis, is provided in Loudcher et al. ( 2015 ).

Eventually, the Big Data era made OLAP meet new challenges such as: (1) design methods that handle a high complexity that tends to make the number of dimensions explosive; (2) computing methodologies that leverage the cloud computing paradigm for scaling and performance; and (3) query languages that can manage data variety (Cuzzocrea, 2015 ).

Big Data also pushed forward the exploitation of textual documents, which are acknowledged to represent the majority of the information stored worldwide. In the context of OLAP, i.e., Text or Textual OLAP, the key issue is to find ways of aggregating textual documents instead of numerical KPIs. Two trends emerge, based on the hypercube structure and text mining, respectively. They are thoroughly surveyed and discussed in Bouakkaz et al. ( 2017 ).

Finally, with the emergence of Data Lakes (DLs) in the 2010’s, the concept of Data Warehouse (DW), on which OLAP typically rests, is challenged in terms of data integration complexity, data siloing, data variety management and even scaling. However, DLs and DWs are actually synergistic. A DL can indeed be the source of a DW, and DWs can be components, among others, of DLs. Thence, OLAP remains very useful as an analytical tool in both cases. Two recent and complimentary surveys cover DL, DW and OLAP-related issues (Sawadogo and Darmont, 2021 ; Hai et al., 2021 ).

2.3 Machine Learning

Artificial intelligence (AI) has been a hot research and technological topic for a few years. AI refers to the computing techniques that allow stimulation of human-like intelligence in machines. AI is a broad area of research and technology that includes a sub-area - Machine Learning (ML), which enables a computer system to learn models from data.

The most frequent ML techniques include regression, clustering, and classification (they are supported by multiple software tools (Krensky & Idoine, 2021 )). Regression aims at building statistical models to predict continuous values (e.g., electrical or thermal energy usage in a given point in time or time period). Clustering aims at dividing data items into a non-predefined number of groups, such that the instances in the same group have similar values of some features (e.g., grouping customers by their purchase behaviour). Classification aims at predicting a predefined class to which belongs a given data item (e.g., classifying patients into a class of high blood pressure risk or a class of no-risk).

ML in turn includes a sub-area - Artificial Neural Networks (ANNs). ANNs are based on a statistical model that reflects the way a human brain is build, thus it mathematically models how the brain works. ANNs are the foundation of Deep Learning (DL) (Bengio et al., 2021 ). DL applies algorithms that allow a machine to train itself from large volumes of data, in order to learn new models based on new input (data). DL turned out to be especially efficient in image and speech recognition.

In order to build prediction models by ML algorithms, massive amounts of pre-processed data are needed. The pre-processing includes a workflow of tasks (a.k.a. data wrangling (Bogatu et al., 2019 ), data processing/preparation pipeline (Konstantinou & Paton, 2020 ; Romero et al., 2020 ) or ETL (Ali & Wrembel, 2017 )). The workflow includes the following tasks: data integration and transformation, data cleaning and homogenization, data preparation for a particular ML algorithm. Based on pre-processed data, ML models are built (trained, validated, and tuned Quemy, 2020 ). Since the whole workflow is very complex, constructing it requires a deep knowledge from its developer in multiple areas, including software engineering, data engineering, performance optimization, and ML. Thus, multiple works focus on automating the construction of such workflows. This research area is commonly called AutoML . It turned to be a hot research area in recent years (Bilalli et al., 2019 ; Giovanelli et al., 2021 ; Koehler et al., 2021 ; Quemy, 2019 ). (Kedziora et al., 2020 ) provides an excellent state of the art of this research area.

Other major trends in ML are pointed to by the Gartner report on strategical technological trends for 2021 (Panetta, 2020 ). Among the trends AI engineering is listed. It is defined as means to “facilitate the performance, scalability, interpretability and reliability of AI models”. Interpretability and reliability is crucial, since AI systems are typically applied to support decision making by providing means of prediction models and recommendations, which by definition must be reliable. Moreover, a decision maker must be able to figure out and understand how a decision was reached by a given model.

Unfortunately, models built by ML algorithms may be difficult to understand for a user, for two main reasons. First, a model may be too complex to be understood by a user. Second, a user typically has access to an input and output of a model, i.e., internals of the model are hidden. Such models are typically referred to as black-box models . They typically include ensemble models produced by classification techniques (e.g., Random Forest, Bagging, Adaboost) and ANN models. Even a simple classification model may be difficult to understand if a decision tree is large. ANN models are by their nature non-interpretable (e.g., an ANN with a hundred of inputs and several hidden layers). As a consequence, a user is not able to fully understand how decisions are reached by such complex models (Du et al., 2019 ).

Yet, in a decision making process, it is necessary to understand how a given decision was reached by a ML model. Therefore, there is a need for developing methods for explaining how ML black-box models work internally. As the response to this need, the so-called Explainable Artificial Intelligence (EAI) (Biggio et al., 2021 ; Goebel et al., 2018 ; Liang et al., 2021 ; Langer et al., 2021 ; Miller, 2019 ) or Interpretable Machine Learning (IML) (Du et al., 2019 ) techniques are being developed. This research problem is defined as “investigating methods to produce or complement AI to make accessible and interpretable the internal logic and the outcome of the model, making such process human understandable” (Bodria et al., 2021 ). Explaining ML models turned out to be crucial in multiple business and engineering domains, such as system security (Mahbooba et al., 2021 ), health care (Danso et al., 2021 ), chemistry (Karimi et al., 2021 ), text processing (Moradi & Samwald, 2021 ), finance (Ohana et al., 2021 ), energy management (Sardianos et al., 2021 ), and IoT (García-Magariño et al., 2019 ).

A substantial growth of this research topic is observed in years 2018-2019. The DBLP service Footnote 2 includes in total 215 papers on EAI and 163 papers on IML (as of September 25, 2021). Google Trends Footnote 3 shows an increasing popularity trend of this research topic. Figure  1 shows an aggregated trend for EAI and IML.

Aggregated popularity trend of EAI and IML topics (Google Trends)

Techniques for building interpretable ML models can be divided into two categories, namely intrinsic and post-hoc. An orthogonal classification divides the techniques into global and local interpretable models (Du et al., 2019 ).

Models from the intrinsic interpretability incorporate interpretability directly to their structures, making them self-interpretable. Examples of such models include for example: decision trees, rule-based models, and linear models. Models from the post-hoc interpretability require constructing an additional model, which provides explanations to the main model.

A globally interpretable model means that a user is able to understand how a model works globally, i.e., in a generalized way. A locally interpretable model allows a user to understand how an individual prediction was made by the model.

Multiple approaches to explaining models have been proposed. They may be specific to a type of data used to build a model, i.e., there are specific approaches for table-like data, for images, and for texts.

For explaining models that use table-like data, the most popular method is based either on rules or on feature importance. A rule-based explanation uses decision rules understandable by a user, which explain reasoning that produced the final prediction (decision). A feature importance explanation assigns a value to each input feature. The value represents the importance of a given feature in the produced model.

For explaining models that work on images, the most frequently used technique is called the Sailency Map (SM). The SM is an image where a brightness and/or color of a pixel reflects how important the pixel is (it is typically visualized as a divergent color map). This way, it can be visualized whether and how strongly a given pixel in an image contributes to the given output of a model. The SM is typically modeled as a matrix, whose dimensions are the sizes of the image being analyzed.

A concept similar to SM can be used to explain models that work on text data. When the SM is applied to a text, then every word in the text is assigned a color, which reflects the importance of a given word in the final output of a model.

An excellent overview of explanation methods in ML for various types of data is available in Bodria et al. ( 2021 ).

3 Special Section Content

This editorial paper overviews research topics covered in this special section of the Information Systems Frontiers journal. The special section contains papers invited from the 24 th European Conference on Advances in Databases and Information Systems (ADBIS).

3.1 ADBIS Research Topics

The ADBIS conference has been running continuously since 1993. An overview of ADBIS past and present activities can be found in (Tsikrika and Manolopoulos, 2016 ) and at http://adbis.eu . ADBIS is considered among core European conferences on practical and theoretical aspects of databases, data engineering, data management as well as information systems development and management. In this context, the most frequent research topics addressed by researchers submitting papers to ADBIS within the last ten years include: Data streams , Data models and modeling , Data cleaning and quality , Graph processing , Reasoning and intelligent systems , On-Line Analytical Processing , Software and systems , Ontologies and RDF , Algorithms , Indexing , Spatio-temporal data processing , Data integration , Query language and processing , Machine Learning .

All research topics covered within the last 10 editions (years 2012-2021) of ADBIS are visualized in Fig.  2 . We constrained the analysis to the 10-years period in order to reflect the recent research interests. Moreover, we analyzed papers published only in the LNCS volumes, to include the highest quality papers. In Fig.  2 , the Y axis shows a total number of papers addressing a given topic (the median is equal to 7). Q1-Q4 represent the first, second, third, and fourth quartile, respectively.

Research topics within the last 10 years of ADBIS (based on papers published only in LNCS volumes)

The papers included in this special section address topics from Q3 and Q4, and thus represent frequent ADBIS topics . These papers cover: Graph processing , OLAP , and Machine Learning (marked in black in Fig.  2 ). These three topics are outlined in Section  2 , whereas the papers included in this special section are summarized in Section  3 . It is worth to note that the most frequent ADBIS topics reflect world research trends and they follow research topics of top world conferences in databases and data engineering, including SIGMOD, VLDB, and ICDE (Wrembel et al., 2019 ). This special section includes three papers covering: Graph processing , OLAP , and Machine Learning .

3.2 Papers in this Special Section

The first paper (Belayneh et al., 2022 ), Speeding Up Reachability Queries in Public Transport Networks Using Graph , authored by Bezaye Tesfaye Belayneh, Nikolaus Augsten, Mateusz Pawlik, Michael H. Böhlen, and Christian S. Jensen, addresses the challenges discussed in Section  2.1 for a special case of temporal road networks graphs and a special case of queries, namely, reachability queries over public transport network.

An evaluation of such queries involves multiple computations of shortest paths with additional temporal constraints. Specifically, the connection time calculated as a difference between a departure of the outgoing vehicle and an arrival of previous incoming vehicle is added to the length of a path. The problem is, in general, NP -hard. Therefore, an approximate algorithm is needed to solve the problem efficiently. To this end, the authors propose an algorithm based on graph partitioning: the problem is split into smaller problems. A set of boundary nodes is pre-calculated for each partition. The shortest path is found in each partition (called a cell in the paper) and a choice of a path between partitions. Pre-calculated paths inside cells constitute an index that significantly speeds up the search. In the proposed evaluation, the search is limited to startpoint and endpoint cells and search for chains of cells, as the paths inside cells and boundary nodes are pre-calculatied. The partitioning provides locality, but of course actual performance depends on the choice of partitioning algorithm. The paper contains deep performance analysis and comparison of different partitioning algorithms.

The second paper (Francia et al., 2022 ), entitled Enhancing Cubes with Models to Describe Multidimensional Data , by Matteo Francia, Patrick Marcel, Veronika Peralta, and Stefano Rizzi, presents a first step toward a proof of concept of the Intentional Analytics Model (IAM).

IAM mobilizes both Online Analytical Processing (OLAP) and various machine learning methods to allow users express the so-called analysis intentions and obtain the so-called enhanced (annotated) data cube. Analysis intentions are expressed with five operators. The paper focuses on formalizing and implementing the describe operator, which describes cube measures. Enhanced cube cells are associated with interesting components of models (e.g., clustering models) that are automatically extracted from cubes. For example, cells containing outliers can be highlighted.

Moreover, the authors propose a measure to assess the interestingness of model components in terms of novelty, peculiarity and surprise during the user’s data navigation. A dataviz is also automatically produced by a heuristic to depict enhanced cubes, by coupling text-based representations (a pivot table and a ranked component list) and graphical representations, i.e., various possible charts. Eventually, the whole approach is evaluated through experiments that target efficiency, scalability, effectiveness, and formulation complexity.

The third paper (Ferrettini et al., 2022 ), entitled Coalitional Strategies for Efficient Individual Prediction Explanation , by Gabriel Ferrettini, Elodie Escriva, Julien Aligon, Jean-Baptiste Excoffier, and Chantal Soulé-Dupuy, addresses the problem of explaining machine learning models. The goal of this work was to develop a general method for facilitating the understanding of how a machine learning model works, with a particular focus on identifying groups of attributes that affect a ML model, i.e., a quality of prediction provided by the model.

A starting point of the investigation is an observation that attributes cannot be considered as independent of each other, therefore it was required to verify the influence of all possible attributes combinations on the model quality. The influence of an attribute is measured according to its importance in each group an attribute can belong to. A complete influence of an attribute now takes into consideration its importance among all the possible attribute combinations. Computing the complete influence is of exponential complexity. For this reason, efficient methods for finding influential groups are needed.

In this context, the paper describes a method for identifying groups of attributes that are crucial for a quality of a ML model. To this end, the authors proposed the so-called coalitions . A coalition includes these attributes that influence a ML model. In order to identify coalitions, the authors proposed to use the following techniques:

Model-based coalition , where interactions between attributes used in a model are detected by analyzing the usage of the attributes by the model. To this end, the values of attributes in an input data set are modified and it is observed how the model predictions vary.

PCA-based coalition , where the PCA method is applied to create a set of combined attributes, represented by a new attribute obtained from the PCA. This set is considered as an influential group of attributes.

Variance inflation factor-based coalitions , where the standard variance inflation factor (VIF) is an estimation of the multicollinearity of the attributes in a dataset, w.r.t. a given target attribute. VIF is based on the R coefficient of determination of the linear regression. Since the value of VIF is computed by means of a linear regression, this method is suitable for coalitions where linear correlation between attributes exist.

Spearman correlation coefficient-based coalition , which takes into account non-linear correlations between attributes. The correlations are computed between all pairs of attributes and their correlations are represented by the Spearman coefficient.

These methods were evaluated by excessive experiments on multiple data sets provided by openml.org, for two classification algorithms, namely Random Forest and Support Vector Machine. As the baseline, the so-called complete method was selected. The obtained results, show that the proposed methods provided promising performance characteristics in terms of computation time and model accuracy.

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Acknowledgements

The Guest Editors thank all friends and colleagues who contributed to the success of this special section. We appreciate the effort of all the authors who were attracted by the topics of the ADBIS conference and submitted scientific contributions.

Special thanks go to the Editors-In-Chief Prof. Ram Ramesh and Prof. H. Raghav Rao, for offering this special section to the ADBIS conference and to the Springer staff, namely to Kristine Kay Canaleja and Aila O. Asejo-Nuique, for efficient cooperation.

We appreciated the work done by the reviewers who offered their expertise in assessing the quality of the submitted papers by providing constructive comments to the authors. The list of reviewers includes:

–Andras Benczur (Eötvös Loránd University, Hungary)

–Paweł Boiński (Poznan University of Technology, Poland)

–Omar Boussaid (Univeristé Lyon 2, France)

–Theo Härder (Technical University Kaiserslautern, Germany)

–Petar Jovanovic (Universitat Politècnica de Catalunya, Spain)

–Sebastian Link (University of Auckland, New Zealand)

–Angelo Montanari (University of Udine, Italy)

–Kestutis Normantas (Vilnius Gediminas Technical University, Lithuania)

–Carlos Ordonez (University of Houston, USA)

–Szymon Wilk (Poznan University of Technology, Poland)

–Vladimir Zadorozhny (University of Pittsburgh, USA)

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Darmont, J., Novikov, B., Wrembel, R. et al. Advances on Data Management and Information Systems. Inf Syst Front 24 , 1–10 (2022). https://doi.org/10.1007/s10796-021-10235-4

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95 Information Management Topics for Essays

🏆 best information management topics for essays, ✍️ information management essay topics for college, 👍 good information management research topics & essay examples, 🎓 great information management topics for research papers, ✒️ excellent information system thesis topics, 💡 most interesting topics related to information systems.

• Amazon’s Information Management System: Description and Analysis
• Types of Health Information Management Systems
• The Healthcare Manager’s Role in Information Technology Management
• Infosys Firm’s Information Systems Management
• Walmart: Information Management System
• Lenovo Group Ltd.’s Operations and Information Management
• Planning at McDonald’s: Information Resources Management
• Disaster Planning and Health Information Management This paper discusses promising measures and practices to help the organization to avoid situations with loosing all health information in case of future disastrous events.
• Strategic Information Management: 99 Cents Only Store Strategic information management is defined as the handling of information in an organization to provide value, maintain competitive advantage, and streamline core functions.
• Health Information Technology Service Management One of the aspects of using new technology in medicine is HIT service management, which is a process of establishing a framework for IT-related systems and activities.
• Information Systems Management This paper will explain the SI background, communication software, database, electronic health product choice, enterprise resource planning, business intelligence, etc.
• United Parcel Service: Information Technology Management Plan The operations of UPS are also designed to make the enterprise financially sound by improving employee buy-in through a broad employee ownership formula.
• Information Management and Related Issues in Business Business organizations should embrace the use of technology. It is virtually impossible for any organization to operate without technology.
• Choice of Career: Health Information Management The present-day world dictates conditions to specialists, and its primary orientation is technological development as well as its practical implementation within healthcare facilities.
• Amazon.com: The State of Information Technology and Management At Amazon.com, the state of information system reveals that the company has gained a competitive advantage as the largest online retailer by using an e-commerce model of business.
• Integration of Health Information Management System (HIMS) into Healthcare This paper aims to research the integration of the Health Information Management System (HIMS) into healthcare. The HIMS chosen for this project are the EHR and EMR.
• System Implementation: The Health Information Management Systems The Health Management Information System is a fundamental domain in strengthening the health systems, it helps plan management, analysis, making policies, and making decisions.
• Information Technology Project and Risk Management One technological risk is the shortage of indigenous information technology companies in the area of interest. This shortage leads to a deficiency of technically skilled staff.
• Health Information and Office Management The health information management system is essential in the healthcare industry. It helps make better decisions, organize data efficiently, and solve health-related issues.
• System Analysis of Health Information Management The purpose of this paper is to examine the current health information management system utilized by the ABC Clinic and to propose a new approach in this regard.
• Information Technology Project Portfolio Management Project portfolio management (PPM) is beneficial to IT professionals in several ways apart from an overall increase in efficiency of monitoring and evaluation of ongoing projects.
• Issues of Information Systems Management and Accounting This paper outlines the issues in accounting and ratio analysis, business terms and budget, information systems, computer-based IS threats, and e-business and e-commerce.
• E-Government and Information Age Management The gradual development of technological science in the contemporary world has remained the most anticipated issue in any developed and developing economy
• Information Sharing in Supply Chain Management This paper will describe two articles on information sharing in a supply chain, explain how they relate to Porter’s five competitive forces model.
• Marking Information System for Customer Management Marking Information System will ease the managers’ work with different groups of customers, allowing them to use an individual approach to clients.
• Managing Project Teams: Leadership and Information Systems Development Each participant in the project solves individual tasks set by the project manager, but some tasks can be solved exclusively through collective interaction.
• Universal Information Security Management Measures The cloud computing approach has become the dominant technology paradigm across the entire enterprise IT infrastructure.
• Information Technologies & Risk Management in Business The key point of risk management is their constant monitoring and prevention, ideally coordinated with the duration of project development cycles.
• Information Management Improving Patient Care Technology Information management allows each patient’s data to be recorded, stored, and easily retrieved by health professionals when needed.
• Hospital Information Management Systems Hospital information management systems (HIMSs) are vital for improving healthcare quality, and they let healthcare professionals make data-driven decisions.
• Business Process & Information Technology: Supply Chain Management IT based Grid technology powered supply chain relies on network boosted through World Wide Web, internet and intranet.
• FansivOps International: Information Security and Risk Management FansivOps needs to formulate a proper and effective information security and risk management policy regarding opening a new branch office in the southern region of Sudan, Africa.
• Managing Information Systems Strategy and Security Risks in EasyShopping Ltd The goal of this paper is to examine the EasyShoping Ltd strategy to employ information systems through online shopping sites, to enhance its competitive advantages.
• Information Systems and Project Management Performance The authors assert that requirements instability and requirements diversity are related to stakeholder perception gaps and this can be tied in with project performance.
• Operations and Information Management of CC Music The given report outlines the main features of CC Music, a company founded by Chris and Clive. It focuses on the provision of specific services to bands that include social media marketing.
• Managing Information of Sakhr Software Co This paper would consider the concepts of managing information of Sakhr Software, which is a popular language software company.
• Information Management in Different Organizations The paper examines the information management systems in the Military of the United Arab Emirates and their advantages and disadvantages.
• Real Estate Company’s Information Management The report addresses the concerns and constraints that real estate firms have to face during the implementation of their key objectives and management of customers’ needs.
• Emeco Company’s Information Management Information management plays a critical role in modern business. The presented paper is devoted to the investigation of the furniture industry and the problem Emeco experiences.
• Information Technology-Based Data Management in Retail The following paper discusses the specificities of data management and identifies the most apparent ethical considerations using retail as an example.
• Healthcare Information Project Management The development of a healthcare IT project is likely to require the collaboration of informatics professionals (IP) and the clinical staff to ensure its success.
• Information Management Systems: E-Business of Transport for London This paper explores the benefits accrued by organizations or institutions by embracing the modern methods of managing information.
• Azzaz Shop’s Operations and Information Management Azzaz is a mobile phone and accessories retailer based in England, owned by Lewis. The business started with a single shop, which Lewis has grown into a chain of eleven.
• Global Information and Technical Management Information technology has the potential to coordinate a single customer service center in the company, which will take up the tasks that multiple customer service centers.
• Applying Information Management Solutions Improvement of healthcare quality and provision of patient safety are the primary concerns of healthcare providers.
• The Current Information Management System Improvement The tools used for data analysis and the output is delivered by the staff members when adopting each of these devices can be considered the primary variables to be analyzed.
• Information Systems and Their Management The existence of numerous information systems, including closed corporate ones, allows for transferring the required information to the end user within moments.
• Information Management System Practical Solutions An effective information management system involves planning, proper alignment, and efficient management. Its periodical review ensures it suites the technological changes.
• Accounting Information Management and Performance of Small Business Enterprises
• Information Management Model for Competencies and Learning Outcomes in an Educational Context
• Soft Information Management Effects on Lending Credit Terms in Japan
• How Technology Can Assist or Hinder Information Management Practices in Organizations
• The Impact, Challenge, and Solution of Information Management in Virtual Organizations
• Enhancing Information Management Through Data Mining Analytics
• Air Asia Operational Information Management in Strategy and Operations
• Organizational Performance Through Information Management
• Integrating Data and Information Management for Social Protection
• The Army’s Personnel and Recruiting Information Management Systems
• Information Management: Medical Record as a Legal Document
• The Cost and Information Management Effect in SMEs
• The Essence of Using Communications and Information Management Technologies
• Future Technologies and Applications Within Information Management
• The US American Health Information Management Association on the Adoption of the Electronic Databases
• Applying Meaningful Use Principles to Information Management Systems
• Information Management Strategies and Supply Chain Performance Under Demand Disruptions
• Cloud Computing: The Future of Business Information Management
• MNEs and Information Management: Structuring and Governing IT Resources in the Global Enterprise
• Harnessing the Infrastructure and Data Involved in Information Management
• Information Management for Public Participation in Co-design Processes
• Records and Information Management: Efficient and Systematic Control of Records
• Social Networks and Their Impact on Records and Information Management
• Understanding the US Generic Advertising System and Its Role in Information Management
• The Use of Machine Learning Algorithms in Optimizing Inventory Management in Accounting Information Technology
• Designing a Smart Healthcare System Using Wearable Devices and Data Analytics for Remote Patient Monitoring
• Assessing the Role of Cloud Computing in Optimizing Resource Allocation and Scalability in Enterprise Systems
• The Role of Machine Learning Models in Predicting Stock Market Trends in Computer Science Information Systems
• Developing a Smart City Framework Integrating IoT Devices and Data Analytics for Improved Urban Planning
• The Significance of User Experience (UX) Design in Improving the Usability of Information Systems
• Evaluating the Role of Data Visualization Techniques in Understanding Complex Information System Data
• Analyzing the Adoption and Integration of the Internet of Things (IoT) in Modern Information Systems
• The Challenges and Opportunities of Cloud Computing in Information System Management
• Investigating the Role of Artificial Intelligence in Personalizing User Experiences in Information Systems
• Integration of Enterprise Resource Planning Systems in Organizational Processes
• Potential of Blockchain Technology in Ensuring Data Integrity and Security in Supply Chain Management
• Information Systems in Disaster Management and Emergency Response
• Blockchain-Based Voting System for Secure and Transparent Elections
• The Role of Information Systems in Enabling Personalized and Targeted Marketing Strategies
• Predictive Analytics in Supply Chain Management for Demand Forecasting
• Gamification Techniques to Enhance User Engagement and Motivation in Online Learning Platforms
• Information Systems in Optimizing Energy Consumption and Promoting Sustainability in Organizations
• Data Governance in Ensuring Data Quality and Compliance in Organizations
• Quantum Computing in Solving Complex Problems in Information Systems
• Artificial Intelligence for Intelligent Document Management Systems
• Data Warehousing and Business Intelligence for Data-Driven Decision Making
• Knowledge Management System to Improve Organizational Learning and Innovation

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These essay examples and topics on Information Management were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

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Ph.D. in Information Technology Management Research Topics

Current Ph.D. faculty research programs span a wide range of technology management topics. Examples include:

• Analytics in organizations and social media
• Organizational diffusion, adoption and use of information technologies
• Role of routines and organizational work processes in IT-mediated transformations
• The roles of trust in the adoption of new technology and sharing economy services
• Organizational capabilities, structures and skills for leveraging IT value
• Impacts of IT investments on customer relationship, supply chain and knowledge management
• IT capabilities and the dynamics of competitive action

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Our doctoral students work with faculty members on many intriguing topics. Here are some research projects involving faculty and either current Ph.D. students or graduates:

“Understanding User Participation in Crowdsourced Mobile Apps: A Geo-Spatial Analysis” ( Tae Hun Kim, graduated 2018 ) “Dynamics of Online Word of Mouth Spillover Effects” ( Yen-Yao Wang, graduated 2017 ) “The Effect of Mergers and Acquisitions on Firm Performance: Evidence from Digital Industries” ( Kangkang Qi, graduated 2016 ) “Community Engagement and Collective Evaluation in Crowdfunding” ( Eun Ju Jung, graduated 2015 ) “A Process Theory of Technology Trust Change” ( Peng Liu, graduated 2013 ) “Technology, Humanness and Trust: Rethinking Trust in Technology” ( John Tripp, graduated 2012 ) “The (N)Ever-Changing World: Stability and Change in Organizational Routines” ( Derek Hillison, graduated 2009 ) “How Peripheral Developers Contribute to Open-Source Software Development” ( Pankaj Setia, graduated 2008 ) “Team Documentation Influences Clinic Complexity and Patient Satisfaction” ( Inkyu Kim and Dr. Brian Pentland )

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Management Information Systems Research Guide

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How to Use This Guide

This guide introduces:

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Start at the beginning and work through each page. When you've reached the bottom, select:

The Research Process

Beginning a research paper or a project can be overwhelming, but we're here to help. This research guide will guide you through the research process from start to finish. Your experience with the steps outlined here may differ in order and might even repeat as you complete research, papers, and projects.

Understanding the research process helps you set yourself up for success. Watch this video to learn more about the steps of a successful research process.

Getting Started

Before you choose a topic, make sure you understand your assignment.

• Pay attention to the project length or word count. Shorter assignments require a more focused idea.
• What are the due dates? Give yourself ample time to explore your topic.
• Note the source requirements. Sometimes the information you will need will be found in a library database.
• Keep in mind that scholarly sources take longer to publish, so scholarly information on topics focused on specific recent events may be limited.

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100s of Free Management Dissertation Topics and Titles

Published by Grace Graffin at January 6th, 2023 , Revised On April 16, 2024

Introduction

The subject of management involves an in-depth understanding of the various aspects of business management, such as employee management, risk management, organisational behaviour, and many more.

When choosing a topic for your management dissertation, make sure to consider diverse topics that explore both the theoretical and practical aspects of management.

We understand that getting a dissertation topic approved can be extremely challenging as academic supervisors require students to research a unique case.

This is where our team of writers comes into play. Our writers can up with exciting and manageable management dissertation topics to help get the juices flowing in your head so you can write your dissertation on a unique and engaging topic.

You may also want to start your dissertation by requesting  a brief research proposal  from our writers on any of these topics, which includes an  introduction  to the topic,  research question ,  aim and objectives ,  literature review  along with the proposed  methodology  of research to be conducted.  Let us know  if you need any help in getting started.

Check our  dissertation examples  to get an idea of  how to structure your dissertation .

Review the full list of  dissertation topics for here.

How to Select the Best Management Dissertation Topic?

A dissertation topic must be selected based on research interests, availability of data, time limitations, and the research’s scope and significance. The following management dissertation topics are carefully shortlisted while considering all these parameters. Please review these topics and let us know if you have any queries.

Also Read: Operations Management Dissertation Topics

• International Development Dissertation Topics
• Cooperate Governance Dissertation Topics
• Business Intelligence Dissertation Topics
• Business Information Technology Dissertation Topics
• International Business Dissertation Topics
• Business Management Dissertation Topics
• Business Psychology Dissertation Topics
• Business Law Dissertation Topics
• Project Management Dissertation Topics
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• Operations Management Dissertation Topics

2024 Management Research Topics

Topic 1: an evaluation of organizational change management- why do people tend to oppose change.

Research Aim: The research will aim to assess the structure of organizational change management and to find the reasons why people resist or oppose the changes in an organization. There are many reasons through which change in organization’s management becomes important but some employees’ does not accept that changes. There are many reasons why people resist changes on organization. In certain circumstances, resistance to change might be beneficial. Resistance to change is, in fact, a crucial feedback mechanism that must not be neglected.

Topic 2: Investigating the effectiveness of customer relationship management in airlines

Research Aim: The research will aim to study the efficiency of CRM in airlines. Customer relationship management has evolved into a critical technique used by every corporation to better its operations and obtain a competitive advantage over competitors. Customer relationship management has evolved into a key priority for airline firms and an integral part of airline businesses’ corporate strategy to distinguish themselves from rivals in the eyes of the consumer. The goal of facility organisations, such as airlines, is to provide services that attract and maintain satisfied, loyal customers who promote the airline.

Topic 3: How does leadership affect employees’ productivity? A case of IT firms

Research Aim: This research will focus on leadership positions in IT organisations with the goal of increasing staff productivity and performance. Leadership is essential for increasing employee retention, career drive, and efficiency. Most companies’ progress is accelerated by effective leadership. As a result, it is critical to organisational success. Employee performance, on the other hand, is a critical pillar of every firm, and companies must examine the variables that contribute to great performance. Leadership is based on confidence, which is based on skill, sincerity, ethics, transparency, reactivity, empathy, and kindness.

Topic 4: The effect of organisation advancement tools on business performance

Research Aim: The research will aim to find the effect of organization advancement on business performance. Organizational tools are objects that assist you in organising your workspace, resources, and tasks in order to make your workday more effective. Physical instruments, planners, and software platforms are examples of what they can be. Organization advancement tools are a great source to improve your business performance as they help you in managing your daily tasks and workforce.

Topic 5: The importance of leadership and social skills in new entrepreneurs: An investigative study

Research Aim: The research will aim to investigate the importance of leadership and social skills in new entrepreneurs. Developing talent, introducing innovative goods and services, delivering efficiency, and gaining market share all benefit from improved leadership qualities. If you wish to stay small, you might be able to get away with not growing your leaders. Otherwise, it will restrict your progress. Social skills enable entrepreneurs to interact with customers more effectively, resulting in more agreements and more profitability.

Covid-19 Management Research Topics

Crisis management during covd-19.

Research Aim: This study will identify crisis management aspects during COVD-19, including its challenges and solutions.

Business management during COVID-19

Research Aim: This study will review business executives’ challenges in various scale industries and how they are recovering from the loss. How far did they succeed?

Hospital and medicine management during COVID-19

Research Aim: This study will highlight the role of hospital management during COVID-19, the challenges they came across, and the ways to overcome those challenges.

Educational management during COVID-19

Research Aim: This study will address the issues faced by students and educational institutes. How are they trying to overcome the challenges of imparting education during the coronavirus pandemics?

Maternal health care management during COVID-19

Research Aim: The lockdown situation has been an issue of concern for the patients, including pregnant women. This study will address the role of Maternal health care management during COVID-19.

Management Dissertation Topics for 2023

Topic 1: analyzing the traditions and trends in public administration and management in post-wwii europe.

Research Aim: The purpose of the research will be to analyze the characteristics of cultural and national communities that have influenced public administration and management in the 1970s and 1980s in Europe. The study will be carried out using a systematic literature review.

Topic 2: The Impact of Gender-inclusive Gatekeeping and Predecessors Influence on the Success of Female CEOs

Research Aim: The purpose of the research will explore how local organisational agents and contexts can help women leaders overcome barriers and achieve success at higher levels in corporate firms. The study will focus on CEO succession events and predecessor CEOS factors and their influence on women post-succession. The research design will be developed qualitatively.

Topic 3: Analysing the Impact of Daily Psychological Power on Organisational Leaders

Research Aim: The research will use quantitative techniques to analyze power-holders relational and interdependent work contexts. The study will examine the effect of daily psychological power using the factors of abusive behaviour and perceived incivility.

Topic 4: Examining the Impact of Cultural Diversity on Interaction Process and Performance

Research Aim: Using quantitative techniques, the research will analyse the interaction process and performance factors in two groups of employees in the services industry – homogenous and culturally diverse. The effectiveness in operation and arrangements will be examined.

Topic 5: Analyzing the Impact of ‘Voice’ and ‘Silence’ on Destructive Leadership

Research Aim: The research will examine the limited and biased view of silence in management literature. The study will also analyse the impact of silence in an organisation in terms of a functional value using quantitative research techniques. Furthermore, how silence in organisations can be used as a strategic response will be discussed.

Topic 6: Examining the Relationship between Productivity, Management Practices, and Employee Ability in the UK Setting

Research Aim: Using quantitative techniques, the study will analyse a relationship between productivity, management practices, and employee ability using data from management practices surveys and employees’ longitudinal earnings records.

Topic 7: Analysing the Factors that Impact International Differences in Gender Pay Gap

Research Aim: The research will use quantitative techniques to analyse microdata from various countries between 1980 and 2010. The study will use the factors of wage structures, net supply, wage compression, collective bargaining coverage, and unionised wage setting to identify the lower gender pay gap internationally.

Topic 8: The Impact of Psychosocial Hazards on Workplace Risk Management

Research Aim: The study will investigate workplace risk management practices in industry sectors with a high risk of musculoskeletal disorders (MSDs) and mental health disorders (MHDs) and the extent to which they may rise from psychosocial hazards. The research will be conducted using qualitative research techniques.

Strategic Management and Organisational Behavior Dissertation Topics

Strategic management and organisational behaviour can be described as the actions a firm takes to achieve its business objectives primarily derived from competitive markets’ dynamic behaviour. Following are some interesting dissertation topics under this field of study;

Topic 1: The Impact of Organisational Goals on Organisation Behavior

Research Aim: The primary focus of this research will be to combine factors from the theory of action, phases and self-determination theory to develop a motivational model that will explain the relationship between organisational goals setting process that lead to organisational behaviour. The research will be conducted using mixed methods of research techniques.

Topic 2: Integrating the Principles of Strategic Human Capital and Strategic Human Resource Management to Improve Organisational Performance

Topic 3: comparing the impact of family and non-family firm goals on strategy, family and organisational behavior.

Research Aim: This research will analyse the differences between family and non-family business goals and their impact on how businesses develop strategies. Also, the research will assess how these developed strategies would affect family and organisational behaviour. This research will use quantitative research techniques.

Topic 4: Analyzing the Effect of Strategy, Innovation, Networks and Complexity on Organisational Adaptability – The Mediating Effect of Leadership

Research Aim: The current study will use empirical analysis to examine the effects of strategy, innovation, networks, and complexity of organisational adaptability using leadership as a mediation factor.

Topic 5: Examining the Effect of Appointment of a Racial Minority Female CEO on White Male Top Manager Intrapsychic and Behavioral Responses

Research Aim: This research will examine white male managers’ behavioural responses to a female racial minority CEO’s appointment. The behaviour that the research will analyse is the amount of help that the white male top manager provides to their fellow executives. The research will be conducted using quantitative techniques.

Topic 6: Analysis of the Effectiveness of an Affect-Based Model to Portray Recipients Responses to Organisational Change Events

Research Aim: The study will use the Affect-Based Model developed by Oreg et al. (2016) to analyse if it is useful in documenting and portraying the recipient responses to organisational change events. The research will use factors of valence and activation to assess the effectiveness of the model. The study will be conducted using quantitative techniques.

Topic 7: Evaluating the Relationship between the personality of a CEO and Employee Motivation

Research Aim: This research will investigate the relationship between a CEO’s personality and employee motivation. The core of this study will be to assess whether a CEO’s character possesses the power to influence employee motivation or not. Case studies from various companies will be used in this study.

Topic 8: Assessing the Role of Managers in Bringing and Implementing Technological Change in an Organisation

Research Aim: This research will focus on how managers implement technological change in their organisations. Change management is challenging as not all employees are open to accepting change. This research will focus on various ways through which managers successfully implement technological change in their companies.

Topic 9: An Analysis of Organisational Change Management: Why Employees Resist Change?

Research Aim: This research will focus on why employees resist change in organisations, i.e., why employees dislike change. Different causes and factors will be discussed in this study, and the research will conclude why employees do not wholeheartedly accept the change.

Knowledge Management Dissertation Topics

The importance of knowledge management for organisations can’t be understated because this aspect of management enhances the workforce’s capabilities and overall productivity. It leads to a competitive advantage and provides the basis for differentiating an organisation from its competitors. Some interesting dissertation topics under this field are;

Topic 1: Examining the Impact of Enterprise Social Networking Systems (ESNS) on Knowledge Management and Organisational Learning

Research Aim: The research will investigate the effect of ESNS on knowledge management processes and organisational learning. The research will use knowledge creation and sharing to play the mediating role in analysing the proposed relationship. The proposed study will use empirical research methods.

Topic 2: A Review of Knowledge Management Research

Research Aim: The research paper will use a systematic literature review technique for the proposed study. The research will review the last twenty years of knowledge management literature to assess the presence of bias in explaining knowledge integration over research by exploring knowledge differentiation processes.

Topic 3: The Impact of the Internet of Things (IoT) on Innovation and Knowledge Management Capacity

Research Aim: The purpose of this research will be to investigate the plausible relationship between knowledge management systems, open innovation, knowledge management capacity, and innovation capacity in firms. The research will be conducted using empirical techniques to draw reliable conclusions.

Topic 4: The Impact of Strategic Knowledge Management on MNC and their Subsidiaries Performance

Research Aim: The research will develop a model to test the possibility of a relationship between strategic knowledge management (SKM) processes and organisation performance compared between multinational companies and their subsidiaries. The research will also analyse the impact of relational context on knowledge creation and transfer.

Topic 5: Analyzing the Relationship between Knowledge Management Practices and Knowledge Worker Performance - The Mediating Role of Organisational Commitment

Research Aim: The study will analyse the role of knowledge management practices to address the issues of insufficient organisational commitment and knowledge workers’ performance in the UK’s public sectors. The proposed study will use quantitative research techniques to fulfil its aim and objectives.

Topic 6: The Relationship between Knowledge Management Processes and Sustainable Competitive Advantage in Private Business Schools in the UK

Research Aim: The proposed research will explore the impact of knowledge management processes on sustainable completive advantages by using knowledge-based view (KBV) and resource-based view (RBV) as mediators in the relationship. The research will be conducted using quantitative techniques of data collection (i.e. questionnaire) and analysis (i.e. structural equation modelling).

Topic 7: The Impact of Strategic Knowledge Management on Manufacturing Firm’s Performance

Research Aim: The purpose of the study will be to empirically investigate the relationship between the availability and use of IT solutions for strategic knowledge management and a manufacturing firm’s performance, which will be measured in unit production. The research will use the resource-based view and the knowledge-based theory to develop a conceptual framework to analyze this relationship’s effect.

Topic 8: Evaluating how Knowledge Management Impacts Company Performance: A Case Study of Sainsbury

Research Aim: This research will discuss the basic concepts of knowledge management. The study will also discuss the impact knowledge management has on a company’s performance, i.e. how it helps companies achieve their goals. The main focus of this research work will be on Sainsbury’s knowledge management framework.

Topic 9: Knowledge Management as a Core Competency? Evaluating the Application and Benefits of Knowledge Management

Research Aim: This research will uncover how companies utilise knowledge management as their core competency and how it benefits their business operations. This study’s main focus will be on applying the various concepts of knowledge management and their implication for businesses.

Topic 10: Exploring the Managerial Concerns and Issues in Knowledge Management and Their Impact on Organisations

Research Aim: This research will explore the managerial concerns and issues related to knowledge management. The study will also focus on assessing the impact of these issues on businesses and how they can influence day-to-day operations. This will be an evidence-based study where evidence from different companies and various situations will be evaluated.

Leadership and Management Information System Dissertation Topics

Leadership drives the organisational agenda and is regarded as one of the most influential factors in streamlining organisations’ processes. Good leadership results in better performance of any organisation because it gives direction to the business activities under the market conditions and requirements.

Similarly, management information systems are pivotal to any organisation’s success and successfully implementing them can benefit the organisation in many ways. Following are some dissertation topics under the subject of leadership and management information systems;

Topic 1: The Role of Information Systems (IS) in Enterprise Architecture and its Impact on Business Performance

Research Aim: This study will examine the relationship between IS Enterprise Architecture and business performance using technical alignment and IS capabilities mediators. This research will be conducted using quantitative techniques to fulfil its aim.

Topic 2: Exploring The Relationship between Ethical Leadership and Employee Knowledge Sharing

Research Aim: This research will use social learning theories and self-determination to investigate the relationship between ethical learning and employee knowledge sharing. The study will be conducted using empirical research techniques.

Topic 3: Analysing the Impact of Relationship Leadership and Social Alignment on Information Security Systems Effectiveness in Private Organisations

Research Aim: This research will use social capital theory as its theoretical foundation to explore the impact of relational leadership on social alignment between business and IT executives. The relational model will study the factors of integrated knowledge, information security system effectiveness, and organisational performance. This research will use empirical techniques.

Topic 4: Examining the Relationship between Operating Room (OR) Leadership and Operating Staff Performance

Research Aim: This research will analyse the relationship between Operating Room leadership and operating staff performance. This will be done using emotional intelligence and collaboration variables to assess staff performance, using recovery numbers. The relationship will also be examined through the mediating role of leadership principles. The data will be collected and assessed using quantitative research techniques.

Topic 5: The Role of Transformational Leadership as a Mediating Variable in the DeLone and McLean Information Success Model.

Research Aim: The research will use the DeLone and McLean Information Success Model to analyse if productivity software implemented in an organisation can improve its performance. However, the research will also evaluate the model and propose modifications to include transformational leadership as a mediating factor in the information success model. The research will be quantitative in nature.

Topic 6: Assessing the Role of Leadership in an Organisation to Help Adopt Advanced Technological Systems

Research Aim: This research will assess the role of leadership in an organisation to help companies realise the importance of innovative, technologically advanced systems. Many companies today are still naive to the ever more important role of technology. Thus this research will aim to help companies adopt innovative technological systems through leadership. The research will be evidence-based in nature.

Topic 7: Evaluating How Changing Business Leadership Impacts Technological Organisational Performance

Research Aim: Changing leadership in organisations can prove a disaster if not handled properly. The transition process is extremely challenging, and companies should have the capability to handle this phase. This research will explore how their decision to change leadership impacts technological and organisational performance and how to optimise the process. This research will be quantitative in nature.

Topic 8: Can Information Systems in Organisations Be Considered a Competitive Advantage?

Research Aim: Information systems, if implemented successfully, benefit organisations immensely. The impact that an information system has and its results help companies stay ahead of their competitors. This research will assess how companies can turn their information systems into a competitive advantage, and most importantly, whether they or not information systems should be considered a competitive advantage.

Topic 9: Understanding the Leadership Challenges of Implementing and Managing an Advanced Information System in an Organisation

Research Aim: This research will help explain the challenges that managers and the entire leadership of an organisation face when implementing an advanced information system. Bringing a change in a company is challenging, and throw in a technology to implement, the process becomes even more challenging. This study will explore in detail all related challenges through quantitative research.

Topic 10: Do all Business Processes in an Organisation need Information System Management?

Research Aim: It is often argued that not all business processes require information systems. However, when talking about today’s world and the technological advancements taking place, it is recommended that business processes in organisations adopt the technology. This research will be a comparative analysis of whether companies are successful and profitable with information systems or without them.

Also Read: Business Dissertation Topics

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Organisational Culture and International Business Dissertation Topics

Organisational culture shapes the work ethics and helps in defining the professional image of organisations. Organisational culture plays a huge role in international business.

Organisations that adopt the country’s culture they are operating in are known to run their operations more successfully. The following topics are related to organisational culture and international business and help students choose an appropriate topic according to their interests.

Topic 1: The Impact of Organisational Culture of Collaborative Networks Influence on IT Governance Performance in Large Enterprises

Research Aim: This research will explore the influence of collaborative networks’ organisational culture on IT governance performance. The study will use a case study to analyse multinationals as they have a wide working network. The purpose of the research will be to determine whether or not organisational culture helps businesses effectively use IT in business operations. The research will be conducted using mixed methods research.

Topic 2: Analysing the Relationship between Supervisor’s Job insecurity and Subordinates’ Work Engagement

Research Aim: The purpose of this research is two-fold. The research will analyse the relationship between the supervisor’s job insecurity and subordinates’ work engagement using a mediator and a moderator. The research will first examine the mediating role of subordinate’s pro-social voice between supervisor job insecurity and subordinates’ work engagement. Next, the research will examine the moderating role of organisational culture between the supervisor’s job insecurity and sub-ordinates pro-social voice. The research will be conducted through quantitative techniques.

Topic 3: Analysing the Impact of Individual Perception of Organisational Culture on the Learning Transfer Environment

Research Aim: The research will be conducted empirically to assess the relationship between culture (as perceived by employees) and the work environment based learning factors (i.e. learning transfer environment [LTE]) in the organisation). LTE is measured using feedback and coaching factors that received resistance or openness to chance, personal outcomes, and supervisor and peer support.

Topic 4: The Role of Organisational Culture on the Development of Psychological Distress in the Workplace

Research Aim: The purpose of the study will be to analyse how organisational culture may cause the symptoms of psychological distress in the workforce. The study will use corporate culture and work organisation conditions as base factors to relate them to employees’ psychological distress. The research will be conducted using quantitative research techniques.

Topic 5: Analysing the Role of Leadership and Organisational Culture

Research Aim: The research will examine the relationship between organisational culture, leadership and employee outcomes. The paper will focus on the mediator of leadership processes and their impact on the relationship between culture and employee outcomes. The study will be conducted using quantitative research techniques.

Topic 6: The Role and Relationships among Strategic Orientations, Cultural Intelligence, International Diversification and Performance of Organisations

Research Aim: The research will aim to understand the drivers of the international expansion of globalised firms. The research will explore the relationship between strategic orientations and cultural intelligence as drivers and international diversification and firm performance. Strategic orientations used in the study include international market orientation (IMO) and entrepreneurial orientation (IEO). The study will be conducted using quantitative research techniques.

Topic 7: Dynamics of Corruption Culture Distance to Core Values

Research Aim: The research will examine how corporate bribery is impacted by cultural distance between multinational enterprises (MNEs) in their home and host countries. The research will also analyse the organisational distance to core value between MNE’s entry into the host country and its headquarters. The research will use empirical data collection and analysis techniques.

Topic 8: Examining Organisational Export Performance by International Business Competencies

Research Aim: The study aims to explore the relationship between international business competencies and export performance. The research will also analyse export performance by singular analysis or combined analysis of the competencies. The research will be conducted using empirical data.

Topic 9: Does Organisational Culture Influence the Leadership Type that a Company Should Adopt?

Research Aim: This research will argue whether companies should hire leaders concerning their culture or not. Organisational culture and leadership are interconnected. Thus companies that do not operate according to their culture struggle to grow exponentially. This research will aim to focus on the possible relationship between leadership and organisational culture. The research will be evidence-based.

Topic 10: Organisational Culture and International Business Competition: Are they Interrelated?

Research Aim: Organisational culture plays a huge role in making a company competitive internationally. When a business’s culture is motivating to all employees and identifies the right culture for its employees, there is every likelihood of rapid growth for both the company and the employees. The research will explore how the two concepts are interrelated.

Important Notes:

As a management student looking to get good grades, it is essential to develop new ideas and experiment with existing management theories – i.e., to add value and interest to your research topic.

The management field is vast and interrelated to many other academic disciplines like operations management , business , business administration , MBA , human resource management and more. That is why creating a management dissertation topic that is particular, sound, and actually solves a practical problem that may be rampant in the field is imperative.

We can’t stress how important it is to develop a logical research topic based on your entire research. There are several significant downfalls to getting your topic wrong; your supervisor may not be interested in working on it, the topic has no academic creditability, the research may not make logical sense, there is a possibility that the study is not viable.

This impacts your time and efforts in writing your dissertation , as you may end up in the cycle of rejection at the initial stage of the dissertation. That is why we recommend reviewing existing research to develop a topic, taking advice from your supervisor, and even asking for help in this particular stage of your dissertation.

Keeping our advice in mind while developing a research topic will allow you to pick one of the best management dissertation topics that fulfil your requirement of writing a research paper and adds to the body of knowledge.

Therefore, it is recommended that when finalizing your dissertation topic, you read recently published literature to identify gaps in the research that you may help fill.

Remember- dissertation topics need to be unique, solve an identified problem, be logical, and be practically implemented. Please look at some of our sample management dissertation topics to get an idea for your own dissertation.

How to Structure your Management Dissertation

A well-structured dissertation can help students to achieve a high overall academic grade.

• A Title Page
• Acknowledgements
• Declaration
• Abstract: A summary of the research completed
• Table of Contents
• Introduction : This chapter includes the project rationale, research background, key research aims and objectives, and the research problems. An outline of the structure of a dissertation can also be added to this chapter.
• Literature Review : This chapter presents relevant theories and frameworks by analysing published and unpublished literature on the chosen research topic to address research questions . The purpose is to highlight and discuss the selected research area’s relative weaknesses and strengths while identifying research gaps. Break down the topic and key terms that can positively impact your dissertation and your tutor.
• Methodology : The data collection and analysis methods and techniques employed by the researcher are presented in the Methodology chapter, which usually includes research design , research philosophy, research limitations, code of conduct, ethical consideration, data collection methods, and data analysis strategy .
• Findings and Analysis : Findings of the research are analysed in detail under the Findings and Analysis chapter. All key findings/results are outlined in this chapter without interpreting the data or drawing any conclusions. It can be useful to include graphs, charts, and tables in this chapter to identify meaningful trends and relationships.
• Discussion and Conclusion : The researcher presents his interpretation of results in this chapter and states whether the research hypothesis has been verified or not. An essential aspect of this section is establishing the link between the results and evidence from the literature. Recommendations with regards to implications of the findings and directions for the future may also be provided. Finally, a summary of the overall research, along with final judgments, opinions, and comments, must be included in the form of suggestions for improvement.
• References : Make sure to complete this by your University’s requirements
• Bibliography
• Appendices : Any additional information, diagrams, and graphs used to complete the dissertation but not part of the dissertation should be included in the Appendices chapter. Essentially, the purpose is to expand the information/data.

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86 Information Management Essay Topic Ideas & Examples

🏆 best information management topic ideas & essay examples, 💡 interesting topics to write about information management, 🔎 good research topics about information management, ✅ simple & easy information management essay titles.

• Healthcare Information Management Course Reflection All the topics I chose for the five model assignments were exciting, and I enjoyed working on them with the main focus being to improve my knowledge, skills, and abilities. The five topics that I […]
• Horticultural Association of Australia’s Information Management System Clearly define the scope of the project. Provide descriptions of stakeholders involved and their role in the project.
• The American Health Information Management Association Certifications The development of health information careers plays an important role in modern society, and the American Health Information Management Association is one of the professional organizations that establishes the required standards.
• Advanced Information Management in Healthcare First of all, the usability of HIS can be defined as an extent to which physicians and the staff can use the system during their work.
• Memorial Hermann Hospital’s Health Information Management The interview was enlightening and instructive, providing insight into the day-to-day operations of HIM at an extensive hospital system, the field’s difficulties and prospects, and the position of HIM in the larger healthcare environment.
• Ethical Issues in Health Information Management Considering the data, which are given in the condition of the problem, then, first of all, it is needed to pay attention to the poor financial situation of the hospital.
• Perspectives in Health Information Management The future of health information is expected to witness a few important trends that can lead to positive results. This information denotes that health information management professionals are sufficiently educated and experienced to meet the […]
• Adoption Process: Information Management There are five main stages that influence the decision, which include awareness that the need exists, searching for information, considering alternative options, making a purchase decision, and behavior after the purchase. Without awareness of the […]
• The OKI Company: Information Knowledge Management In order to promote the concept of shared knowledge among the staff members, OKI will need to deploy the principles of the Corporate Social responsibility into its framework.
• Health Information Management in Canada The main focus in this article is thus based on bringing change in the Canadian health care sector through a collaborative approach between the governing authorities, the service providers, and the recipients of health services.
• Strategic Technology Opportunities for Information Management The incorporation of important areas of technology in the strategic plan established by the University of Hertfordshire for the 2015-2020 period is essential.
• Information Management Strategy: PHS Clinic The formalities related to the staff’s affiliation to the PHS management style, the use of out-of-hospital offices by the latter, the absence of the relevant technology and skills among the local doctors, the reluctance of […]
• Hospital Information Management & Medical Records This division of Al Baraha Hospital is governed by the regulations of the UAE Ministry of Health. It is possible to provide several recommendations that can improve the work of this department.
• Physician Practices and Health Information Management The primary objective of the report ‘Physician Practices and Information Management: HIM Professionals Offer Value in Changing Practice,’ authored by Kevin Heubusch, is to demonstrate the amount of value and expertise that health information management […]
• Convergent Evolution of Health Information Management and Health Informatics The purpose of the study was to provide perspective on the changing landscape of information technology professionals due to the convergence of health information managers and health informatics professionals’ roles.
• Healthcare It Governance: Information Management in the Hospital Setting Moreover, the inclusion of healthcare IT governance will provide opportunities for nurses and other healthcare staff members to be involved in the development of policies aimed at improving public health.
• Bloomington and Monroe County Meeting, Indiana: Focus on Information System Management Agenda The meeting discussion was tailored around the current Information Management System in its application in the management of resources, with specific biases to the use of e-Libraries in Bloomington and Monroe Counties, the extent of […]
• Information Management: Photo-Sharing The purpose of this report is to outline the potential impact of the technology of photo-sharing on the sector of Non-Governmental Organizations.
• Information Lifecycle Management Definition Information Lifecycle Management is an approach used in data management that ensures that there is a sustainable balance between the cost of keeping and managing data and the value of the data to the organization.
• Script Kiddies: Strategic Information Management The programs available also allow the script kiddies to prompt the programs to conduct operations in the background where the users are not aware of the multiple operations that are taking place in the computers.
• Information Resource Management: The Process The purchasing department of Company X completes a paper purchase order and sends it to Supplier A via fax, with a copy going to the accounts payable department of X.
• Information Resource Management and CIO The CIO need to ensure the Information security to provide information assurance in the organization, for which he/ she need to keep a tab on the data on project and risk events.
• Linux System Usage in Information Management A big question is how the open-source model for software development is superior to another kind of development such as the commercial model?
• Wells Fargo & Company Managing Information In the case of bank mergers, such as that of Wells Fargo & Company’s acquisition of Crocker National Bank, or the Bank of New York’s acquisition of Irving Bank Corporation, asset preservation and keeping key […]
• Information Management: The Organizational Dimension Unpleasantness of the dissonant cognitions deep in the unconscious makes the person to eliminate the threat to the ego by ignoring dissonant cognitions and overpower our logic and leads us to act in ways that […]
• Advanced Information Management: Epic Implementation The improvement of patient outcomes, care delivery, and information exchange may be achieved by different methods, and the implementation of Epic is one of them.
• Information Systems in Business Process Management One of the core groups involved with the project is the IT team leader, who oversees and motivates the developers to contribute meaningfully to the project.
• Electronic Information Management in Healthcare Nowadays, the given systems are implemented in the majority of healthcare facilities in the USA to create an environment characterized by the improved use of data and its exchange.
• Information Technology Department and Its Management The biggest issue that is often discussed is that it is hard to determine the number of resources that should be focused on it.
• Information Management in Modern Business However, the problem that needs to be discussed that some essential data is not taken into account most of the time, and it needs to be considered because it is possible to find correlations that […]
• UAE Academy Information Management and Security Despite the threats that the reconsideration of the information management strategy in the UAE Academy involved, the introduction of innovational principles will be quite welcome within the academic setting, since it will allow for improving […]
• Efficient and Reliable Information Management in Organisations Krizan, Merrier, and Logan observe that one of the most common and easiest ways of enhancing the sending and receiving of information in the workplace is through the use of mobile phones. Another advantage of […]
• Volunteering at the Institute for Information Management The Institute for Information Management has worked tirelessly to gain relevance as one of the leading organisations that brings together users of information management systems and the providers of the enabling technologies in a forum […]
• Strategic Information and Knowledge Management Since the managerial tasks are increasingly becoming complex, the need for advanced information systems constantly arises which aims at shifting the information systems from structured routinized support to unstructured complex enquiries serving at the highest […]
• Information Management and its Application in Organizations When this information generates some value for the organization, calls it “a knowledge based asset,” which can be categorized into explicit and tacit knowledge.
• Software as a Service of Information Management In this respect, it is necessary to consider the issue of ‘software as a service’ as the center of the next wave of information management.
• Financial Information Management: J Sainsbury Plc The essence of a balance sheet is to point out the sources and uses of funds that have been invested by owners and indicate the health position of the company in terms of cash availability, […]
• Health Information Management System: Sohar Hospital Sohar Hospital change management program had the following objectives: The strategy ensured that the change to be implemented was adopted in the minimal time possible; the system ensured that members of staff would embrace the […]
• Data Warehousing as an Information Management Tool The array of data stored in the warehouse allows the management to focus on the company as a whole instead of focusing on an organization in terms of departments.
• Organizational Information Management: Data Warehousing Warehoused data is used to support decision making; the stored information is used for further analysis and identification of trends and variations in a given phenomenon.
• Loyalty Schemes Effect: Information, Finance, and HR Management The main function of the customer information management is to facilitate the operation of customer segmentation strategies by figuring out the values and preferences of customers via customer loyalty schemes.
• Employee Recruitment: Information and Recourse Management These tasks may encompass the following: selecting the panel that will carry out the interview process as well as do the short-listing; it is of necessity to train that staff with regards to the process […]
• Paul Strassman: The Politics of Information Management Paul Strassman’s book The Politics of Information Management: Policy Guidelines is used as the primary source of information about Strassman and his vision of information management in organizations.
• Strategic Information and Business Processes Management IT forms an integral part of the organization’s management; consequently, failing to involve the entire management participants in decision-making would inevitably lead to system failure and largely the business entity.
• Information and Knowledge Management: Impacts and Recommendations Using the Baldridge Criteria for Performance Excellence, this paper aims at addressing the gaps existing in the management of information, knowledge and information technology in Community Outreach, an organization charged with the responsibility of helping […]
• Analyzing the Information Management Phenomenon
• Personal Information Management Systems: A Mobile Phone with a Touch Screen
• The Future of Business Information Management
• Security Information Management Market
• Advanced Information Management: The Application of Technology
• Product Information Management System
• The World of Information Management Professionals
• Protocols Controlling Information Management
• Information Management and Performance of Small Business Enterprises
• Optimal Information Management: Organizations Versus Markets
• Social Networks Impact on Information Management
• Information Science versus Information Management: An Analytical Perspective
• Cloud Computing: The Future of Business Information Management
• Future Technologies within Information Management
• How Technology Can Assist or Hinder Information Management
• Online Tourism Information Management System
• Organizational Performance Through Information Management
• Internet Marketing Information Management
• The Corporate Information Management System
• Information Management: The Medical Record as a Legal Document
• Information Management: The Risk Assessment Process
• Best Product Information Management (PIM) Software
• Key Concepts in Information and Knowledge Management
• Data Governance Framework & Information Management
• Best Sellers in Information Management
• Information Management: Best Practices at British Petroleum
• Positioning Information Management in the Bigger Picture: DIKAR Model
• Behavioral and Organizational Theories of Information Management
• Aligning Technology and Business Strategy with Information Management
• Investing in Information: The Information Management Body of Knowledge by Andy Bytheway
• Information Management Processes: Things Can Go Wrong
• The Early Work of Galbraith about Information Management
• Master of Information Management: Growing and Unique Need for Professionals
• Information Management Body of Knowledge: Knowledge and Process Areas
• How to Create an Information Management System
• Information Management from the Uppermost Senior Levels to the Front Line Worker
• Data Management as Synonymous with Information Management
• School Information Management System Saves the Time of Teachers
• Information Management Practices at Microsoft: Spending hours on administration and email
• Laboratory Information Management System: From Simple Sample Tracking to an Enterprise Resource Planning Tool
• Chicago (A-D)
• Chicago (N-B)

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New machine learning algorithm promises advances in computing

Digital twin models may enhance future autonomous systems.

Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests.

Using machine learning tools to create a digital twin, or a virtual copy, of an electronic circuit that exhibits chaotic behavior, researchers found that they were successful at predicting how it would behave and using that information to control it.

Many everyday devices, like thermostats and cruise control, utilize linear controllers -- which use simple rules to direct a system to a desired value. Thermostats, for example, employ such rules to determine how much to heat or cool a space based on the difference between the current and desired temperatures.

Yet because of how straightforward these algorithms are, they struggle to control systems that display complex behavior, like chaos.

As a result, advanced devices like self-driving cars and aircraft often rely on machine learning-based controllers, which use intricate networks to learn the optimal control algorithm needed to best operate. However, these algorithms have significant drawbacks, the most demanding of which is that they can be extremely challenging and computationally expensive to implement.

Now, having access to an efficient digital twin is likely to have a sweeping impact on how scientists develop future autonomous technologies, said Robert Kent, lead author of the study and a graduate student in physics at The Ohio State University.

"The problem with most machine learning-based controllers is that they use a lot of energy or power and they take a long time to evaluate," said Kent. "Developing traditional controllers for them has also been difficult because chaotic systems are extremely sensitive to small changes."

These issues, he said, are critical in situations where milliseconds can make a difference between life and death, such as when self-driving vehicles must decide to brake to prevent an accident.

The study was published recently in Nature Communications.

Compact enough to fit on an inexpensive computer chip capable of balancing on your fingertip and able to run without an internet connection, the team's digital twin was built to optimize a controller's efficiency and performance, which researchers found resulted in a reduction of power consumption. It achieves this quite easily, mainly because it was trained using a type of machine learning approach called reservoir computing.

"The great thing about the machine learning architecture we used is that it's very good at learning the behavior of systems that evolve in time," Kent said. "It's inspired by how connections spark in the human brain."

Although similarly sized computer chips have been used in devices like smart fridges, according to the study, this novel computing ability makes the new model especially well-equipped to handle dynamic systems such as self-driving vehicles as well as heart monitors, which must be able to quickly adapt to a patient's heartbeat.

"Big machine learning models have to consume lots of power to crunch data and come out with the right parameters, whereas our model and training is so extremely simple that you could have systems learning on the fly," he said.

To test this theory, researchers directed their model to complete complex control tasks and compared its results to those from previous control techniques. The study revealed that their approach achieved a higher accuracy at the tasks than its linear counterpart and is significantly less computationally complex than a previous machine learning-based controller.

"The increase in accuracy was pretty significant in some cases," said Kent. Though the outcome showed that their algorithm does require more energy than a linear controller to operate, this tradeoff means that when it is powered up, the team's model lasts longer and is considerably more efficient than current machine learning-based controllers on the market.

"People will find good use out of it just based on how efficient it is," Kent said. "You can implement it on pretty much any platform and it's very simple to understand." The algorithm was recently made available to scientists.

Outside of inspiring potential advances in engineering, there's also an equally important economic and environmental incentive for creating more power-friendly algorithms, said Kent.

As society becomes more dependent on computers and AI for nearly all aspects of daily life, demand for data centers is soaring, leading many experts to worry over digital systems' enormous power appetite and what future industries will need to do to keep up with it.

And because building these data centers as well as large-scale computing experiments can generate a large carbon footprint, scientists are looking for ways to curb carbon emissions from this technology.

To advance their results, future work will likely be steered toward training the model to explore other applications like quantum information processing, Kent said. In the meantime, he expects that these new elements will reach far into the scientific community.

"Not enough people know about these types of algorithms in the industry and engineering, and one of the big goals of this project is to get more people to learn about them," said Kent. "This work is a great first step toward reaching that potential."

This study was supported by the U.S. Air Force's Office of Scientific Research. Other Ohio State co-authors include Wendson A.S. Barbosa and Daniel J. Gauthier.

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Story Source:

Materials provided by Ohio State University . Original written by Tatyana Woodall. Note: Content may be edited for style and length.

Journal Reference :

• Robert M. Kent, Wendson A. S. Barbosa, Daniel J. Gauthier. Controlling chaos using edge computing hardware . Nature Communications , 2024; 15 (1) DOI: 10.1038/s41467-024-48133-3

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COMMUNITY CASE STUDY article

This article is part of the research topic.

Urban Agriculture as Local Food Systems: Benefits, Challenges, and Ways Forward

Factors Affecting the Adoption of High Technology in Vegetable Production in Hanoi, Vietnam Provisionally Accepted

• 1 National Economics University, Vietnam

The final, formatted version of the article will be published soon.

High-tech vegetable production is becoming a priority in agricultural development in Vietnam in the context of digital economic development. This study aims at identifying factors involving the adoption of high technology in vegetable production by local farmers in Hanoi. We used the theory of planned behavior and other farmers’ personal, social and economic factors to develop empirical model and hypotheses. Primary data was collected from a survey of 450 vegetable producers in Hanoi using cluster sampling method combined with random selection. Then, binary logit model was used to analyze the impact of influencing factors. Results showed that there were 7 factors having significant influences the decision to apply technology in vegetable production of farmers including attitude on high tech production, access to information, size of farm, member of extension organization, education level, access to credit and perceived behavior control, in which attitude variable was the most influential factor. Main management implications raised included enhancing access to technological information, providing demostration visits, giving more extension services, improving social inclusion and implementing hi- tech training for farmers in vegetable production.

Keywords: Binary logit regression, high-technology agriculture, social inclusion, Vegetable production, Theory of Planned Behavior

Received: 28 Nov 2023; Accepted: 13 May 2024.

Copyright: © 2024 Bui and Dinh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Truong D. Dinh, National Economics University, Hai Bà Trưng District, Vietnam

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Some topics you might be interested in, iso/iec 27001 transition: what you should know.

ISO/IEC 27001:2022 – Information Security, Cybersecurity and Privacy Protection – Information Security Management Systems – has replaced ISO/IEC 27001:2013.

The new standard was published on October 25, 2022. After a three-year transition period, ending October 31, 2025, all ISO/IEC 27001:2013 certifications will expire or should be withdrawn. We will not conduct initial or recertification audits to the old standard after April 30, 2024.

Certificates issued or reissued against ISO/IEC 27001:2013 during the transition period (November 1, 2022, to October 31, 2025) will have October 31, 2025, as their expiration date and not the usual three-year validity. After the transition period, an organization with an expired ISO/IEC 27001:2013 certification will be treated as a new client, subject to a full initial audit.

Transition options

The transition may be conducted in one of three ways: via special audit, routine surveillance or recertification audit.

• Special audit: a separate audit for clients who would like to complete their transition as a one-off event
• Routine surveillance: a progressive approach for clients who would like to complete their transition during their scheduled ISO/IEC 27001:2013 surveillance audits
• Recertification audit: for clients who wish to complete their transition during their scheduled ISO/IEC 27001:2013 recertification audit

What are the main changes to the standard?

ISO/IEC 27001:2022 is not a fully revised edition. Its main changes include, but are not limited to:

• Annex A references the information security controls in ISO/IEC 27002:2022, which include information about control title and control
• The notes of Clause 6.1.3 c) are revised editorially, including deleting the control objectives and using “information security control” to replace “control”
• The wording of Clause 6.1.3 d) is reorganized to remove the potential ambiguity
• Throughout the new version, the document refers to itself as “this document” rather than “this standard”
• Adding a new item, 4.2 c), to determine the requirements of the interested parties addressed through information security management systems (ISMS)
• Adding a new subclause. 6.3-Planning for changes, which defines that the changes to the ISMS shall be carried out by the organization in a planned manner
• Keeping the consistency in the verb used in connection with documented information, for example, using “Documented information shall be available as evidence of XXX” in Clauses 9.1, 9.2.2, 9.3.3 and 10.2
• Using “externally provided process, products or services” to replace “outsourced processes” in Clause 8.1 and deleting the term “outsource”
• Naming and reordering the subclauses in Clause 9.2-Internal audit and 9.3-Management review
• Exchanging the order of the two subclauses in Clause 10-Improvement
• Updating the edition of the related documents listed in Bibliography, such as ISO/IEC 27002 and ISO 31000
• Some deviations in ISO/IEC 27001:2013 to the high-level structure, identical core text, common terms and core definitions of management system standards (MSS) are revised to be consistent with the harmonized structure for MSS, for example, Clause 6.2 d)

Note 1: The first two items come from ISO/IEC 27001:2013/DAmd1 and the third item from ISO/IEC 27001:2013/COR 2:2015. The other changes result from the harmonized structure for MSS.

Note 2: Compared with the old edition, the number of information security controls in ISO/IEC 27002:2022 decreases from 114 controls in 14 clauses to 93 controls in 4 clauses. For the controls in ISO/IEC 27002:2022, 11 controls are new, 24 controls are merged from the existing controls, and 58 controls are updated. Moreover, the control structure is revised, which introduces “attribute” and “purpose” for each control and no longer uses “objective” for a group of controls.

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Professor Emeritus David Lanning, nuclear engineer and key contributor to the MIT Reactor, dies at 96

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David Lanning, MIT professor emeritus of nuclear science and engineering and a key contributor to the MIT Reactor project, passed away on April 26 at the Lahey Clinic in Burlington, Massachusetts, at the age of 96.

Born in Baker, Oregon, on March 30, 1928, Lanning graduated in 1951 from the University of Oregon with a BS in physics. While taking night classes in nuclear engineering, in lieu of an available degree program at the time, he started his career path working for General Electric in Richland, Washington. There he conducted critical-mass studies for handling and designing safe plutonium-bearing systems in separation plants at the Hanford Atomic Products Operation, making him a pioneer in nuclear fuel cycle management.

Lanning was then involved in the design, construction, and startup of the Physical Constants Testing Reactor (PCTR). As one of the few people qualified to operate the experimental reactor, he trained others to safely assess and handle its highly radioactive components.

Lanning supervised experiments at the PCTR to find the critical conditions of various lattices in a safe manner and conduct reactivity measurements to determine relative flux distributions. This primed him to be an indispensable asset to the MIT Reactor (MITR), which was being constructed on the opposite side of the country.

An early authority in nuclear engineering comes to MIT

Lanning came to MIT in 1957 to join what was being called the “MIT Reactor Project” after being recruited by the MITR’s designer and first director, Theos “Tommy” J. Thompson, to serve as one of the MITR’s first operating supervisors. With only a handful of people on the operations team at the time, Lanning also completed the emergency plan and startup procedures for the MITR, which achieved criticality on July 21, 1958.

In addition to becoming a faculty member in the Department of Nuclear Engineering in 1962, Lanning’s roles at the MITR went from reactor operations superintendent in the 1950s and early 1960s, to assistant director in 1962, and then acting director in 1963, when Thompson went on sabbatical.

In his faculty position, Lanning took responsibility for supervising lab subjects and research projects at the MITR, including the Heavy Water Lattice Project. This project supported the thesis work of more than 30 students doing experimental studies of sub-critical uranium fuel rods — including Lanning’s own thesis. He received his PhD in nuclear engineering from MIT in fall 1963.

Lanning decided to leave MIT in July 1965 and return to Hanford as the manager of their Reactor Neutronics Section. Despite not having plans to return to work for MIT, Lanning agreed when Thompson requested that he renew his MITR operator’s license shortly after leaving.

“Because of his thorough familiarity with our facility, it is anticipated that Dr. Lanning may be asked to return to MIT for temporary tours of duty at our reactor. It is always possible that there may be changes in the key personnel presently operating the MIT Reactor and the possible availability of Dr. Lanning to fill in, even temporarily, could be a very important factor in maintaining a high level of competence at the reactor during its continued operation,” Theos J. Thompson wrote in a letter to the Atomic Energy Commission on Sept. 21, 1965

One modification, many changes

This was an invaluable decision to continue the MITR’s success as a nuclear research facility. In 1969 Thompson accepted a two-year term appointment as a U.S. atomic energy commissioner and requested Lanning to return to MIT to take his place during his temporary absence. Thompson initiated feasibility studies for a new MITR core design and believed Lanning was the most capable person to continue the task of seeing the MITR redesign to fruition.

Lanning returned to MIT in July 1969 with a faculty appointment to take over the subjects Thompson was teaching, in addition to being co-director of the MITR with Lincoln Clark Jr. during the redesign. Tragically, Thompson was killed in a plane accident in November 1970, just one week after Lanning and his team submitted the application for the redesign’s construction permit.

Thompson’s death meant his responsibilities were now Lanning’s on a permanent basis. Lanning continued to completion the redesign of the MITR, known today as the MITR-II. The redesign increased the neutron flux level by a factor of three without changing its operating power — expanding the reactor’s research capabilities and refreshing its status as a premier research facility.

Construction and startup tests for the MITR-II were completed in 1975 and the MITR-II went critical on Aug. 14, 1975. Management of the MITR-II was transferred the following year from the Nuclear Engineering Department to its own interdepartmental research center, the Nuclear Reactor Laboratory , where Lanning continued to use the MITR-II for research.

Beyond the redesign

In 1970, Lanning combined two reactor design courses he inherited and introduced a new course in which he had students apply their knowledge and critique the design and economic considerations of a reactor presented by a student in a prior term. He taught these courses through the late 1990s, in addition to leading new courses with other faculty for industry professionals on reactor safety.

Co-author of over 70 papers , many on the forefront of nuclear engineering, Lanning’s research included studies to improve the efficiency, cycle management, and design of nuclear fuel, as well as making reactors safer and more economical to operate.

Lanning was part of an ongoing research project team that introduced and demonstrated digital control and automation in nuclear reactor control mechanisms before any of the sort were found in reactors in the United States. Their research improved the regulatory barriers preventing commercial plants from replacing aging analog reactor control components with digital ones. The project also demonstrated that reactor operations would be more reliable, safe, and economical by introducing automation in certain reactor control systems. This led to the MITR being one of the first reactors in the United States licensed to operate using digital technology to control reactor power.

Lanning became professor emeritus in May 1989 and retired in 1994, but continued his passion for teaching through the late 1990s as a thesis advisor and reader. His legacy lives on in the still-operational MITR-II, with his former students following in his footsteps by working on fuel studies for the next version of the MITR core. 

Lanning is predeceased by his wife of 60 years, Gloria Lanning, and is survived by his two children, a brother, and his many grandchildren .

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