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Elragal, A. & Elgendy, N. (2024). A Data-Driven Decision-Making Readiness Assessment Model: The Case of a Swedish Food Manufacturer. Decision Analytics Journal, 10, Article ID 100405.
Open this publication in new window or tab >>A Data-Driven Decision-Making Readiness Assessment Model: The Case of a Swedish Food Manufacturer
2024 (English)In: Decision Analytics Journal, ISSN 2772-6622, Vol. 10, article id 100405Article in journal (Refereed) Published
Abstract [en]

This study proposes a model to assess data-driven decision-making (DDDM) readiness in organizations. We present the results from investigating the DDDM readiness of a Swedish organization in the food industry. We designed and developed a questionnaire to collect data about the organization’s decision-making and IT systems. We conducted eleven interviews at the case study organization: ten with various functional decision-makers and one with the IT Manager about IT systems. The interview data were then analyzed against known decision theories and state-of-the-art DDDM. Based on the interview outcomes, we analyze the data according to the assessment model and recommend changes to the organization’s readiness for data-driven decisions. The findings show that while the organization was assessed as ready in the decision-making process and decision-maker pillars, it was not ready in the data or analytics pillars. Accordingly, we recommend a set of actions, including considering integration and decision systems, further developing dashboards, increasing data and analytics resources (such as enterprise data warehouse, big data management tools, data lake environment, and data analytics algorithms), and defining key roles necessary for digitalization and DDDM (such as Data Engineer, Data Scientist, Business Intelligence Specialist, Chief Data Officer, and Data Warehouse Designer/Administrator). The contribution of this study is the DDDM readiness assessment model, accompanied by a questionnaire for determining the readiness level in organizations.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Data-driven decision-making, decision theory, information technology, case study, Swedish food industry
National Category
Computer Systems
Research subject
Information Systems
Identifiers
urn:nbn:se:ltu:diva-103896 (URN)10.1016/j.dajour.2024.100405 (DOI)
Note

Validerad;2024;Nivå 1;2024-01-30 (signyg);

License full text: CC BY-NC-ND

Available from: 2024-01-23 Created: 2024-01-23 Last updated: 2024-02-02Bibliographically approved
Elgendy, N., Elragal, A. & Päivärinta, T. (2023). Evaluating collaborative rationality-based decisions: a literature review. In: Ricardo Martinho; Rui Rij; Maria Manuela Cruz-Cunha; Dulce Domingos; Emanuel Peres (Ed.), CENTERIS – International Conference on ENTERprise Information Systems / ProjMAN – International Conference on Project MANagement / HCist – International Conference on Health and Social Care Information Systems and Technologies 2022: . Paper presented at International Conference on ENTERprise Information Systems (CENTERIS 2022), International Conference on Project MANagement (ProjMAN 2022) and International Conference on Health and Social Care Information Systems and Technologies (HCist 2022), Lisbon, Portugal, November 9-11, 2022 (pp. 647-657). Elsevier
Open this publication in new window or tab >>Evaluating collaborative rationality-based decisions: a literature review
2023 (English)In: CENTERIS – International Conference on ENTERprise Information Systems / ProjMAN – International Conference on Project MANagement / HCist – International Conference on Health and Social Care Information Systems and Technologies 2022 / [ed] Ricardo Martinho; Rui Rij; Maria Manuela Cruz-Cunha; Dulce Domingos; Emanuel Peres, Elsevier, 2023, p. 647-657Conference paper, Published paper (Refereed)
Abstract [en]

Decision making has evolved throughout the years, nowadays harnessing massive amounts and types of data through the unprecedented capabilities of data science, analytics, machine learning, and artificial intelligence. This has potentially led to higher quality and more informed decisions based on the collaborative rationality between humans and machines, no longer bounded by the cognitive capacity and limited rationality of each on their own. However, the multiplicity of modes of collaboration and interaction between humans and machines has also increased the complexity of decision making, consequentially complicating ex-ante and ex-post decision evaluation. Nevertheless, evaluation remains crucial to enable human and machine learning, rationalization, and sensemaking. This paper addresses the need for more research on why and how to evaluate collaborative rationality-based decisions, setting the stage for future studies in developing holistic evaluation solutions. By analyzing four relevant streams of literature: 1) classical decision theory and organizational management, 2) cognitive and neuroscience, 3) AI and ML, and 4) data-driven decision making, we highlight the limitations of current literature in considering a holistic evaluation perspective. Finally, we elaborate the theoretical underpinnings from the knowledge base on how humans and machines evaluate decisions, and the considerations for evaluating collaborative rationality-based decisions.

Place, publisher, year, edition, pages
Elsevier, 2023
Series
Procedia Computer Science, E-ISSN 1877-0509 ; 219
Keywords
Collaborative rationality-based decisions, decision evaluation, human-machine collaboration, data-driven decision making, data science, literature review
National Category
Computer Systems
Research subject
Information Systems
Identifiers
urn:nbn:se:ltu:diva-92477 (URN)10.1016/j.procs.2023.01.335 (DOI)2-s2.0-85164252094 (Scopus ID)
Conference
International Conference on ENTERprise Information Systems (CENTERIS 2022), International Conference on Project MANagement (ProjMAN 2022) and International Conference on Health and Social Care Information Systems and Technologies (HCist 2022), Lisbon, Portugal, November 9-11, 2022
Note

Funder: ITEA3 project Oxilate

Available from: 2022-08-15 Created: 2022-08-15 Last updated: 2023-10-11Bibliographically approved
Elragal, R., Elragal, A. & Habibipour, A. (2023). Healthcare analytics—A literature review and proposed research agenda. Frontiers in Big Data, 6, Article ID 1277976.
Open this publication in new window or tab >>Healthcare analytics—A literature review and proposed research agenda
2023 (English)In: Frontiers in Big Data, ISSN 2624-909X, Vol. 6, article id 1277976Article, review/survey (Refereed) Published
Abstract [en]

This research addresses the demanding need for research in healthcare analytics, by explaining how previous studies have used big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to examine the literature, identify research gaps, and propose a research agenda for researchers, academic institutions, and governmental healthcare organizations. The study contributes to the body of literature by providing a state-of-the-art review of healthcare analytics as well as proposing a research agenda to advance the knowledge in this area. The results of this research can be beneficial for both healthcare science and data science researchers as well as practitioners in the field.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2023
Keywords
healthcare, data science, data analytics, AI, big data, machine learning, literature review
National Category
Computer Sciences Information Systems Nursing
Research subject
Information Systems
Identifiers
urn:nbn:se:ltu:diva-101411 (URN)10.3389/fdata.2023.1277976 (DOI)
Funder
Luleå University of Technology, 383211
Note

Validerad;2023;Nivå 2;2023-10-05 (hanlid)

Available from: 2023-09-22 Created: 2023-09-22 Last updated: 2023-10-05Bibliographically approved
Raghavendran, K. R. & Elragal, A. (2023). Low-Code Machine Learning Platforms: A Fastlane to Digitalization. Informatics, 10(2), Article ID 50.
Open this publication in new window or tab >>Low-Code Machine Learning Platforms: A Fastlane to Digitalization
2023 (English)In: Informatics, E-ISSN 2227-9709, Vol. 10, no 2, article id 50Article in journal (Refereed) Published
Abstract [en]

In the context of developing machine learning models, until and unless we have the required data engineering and machine learning development competencies as well as the time to train and test different machine learning models and tune their hyperparameters, it is worth trying out the automatic machine learning features provided by several cloud-based and cloud-agnostic platforms. This paper explores the possibility of generating automatic machine learning models with low-code experience. We developed criteria to compare different machine learning platforms for generating automatic machine learning models and presenting their results. Thereafter, lessons learned by developing automatic machine learning models from a sample dataset across four different machine learning platforms were elucidated. We also interviewed machine learning experts to conceptualize their domain-specific problems that automatic machine learning platforms can address. Results showed that automatic machine learning platforms can provide a fast track for organizations seeking the digitalization of their businesses. Automatic machine learning platforms help produce results, especially for time-constrained projects where resources are lacking. The contribution of this paper is in the form of a lab experiment in which we demonstrate how low-code platforms can provide a viable option to many business cases and, henceforth, provide a lane that is faster than the usual hiring and training of already scarce data scientists and to analytics projects that suffer from overruns.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
low-code, no-code, machine learning, auto ML, ML platform, data scientist scarcity, projects overruns
National Category
Computer Sciences Software Engineering
Research subject
Information Systems
Identifiers
urn:nbn:se:ltu:diva-98212 (URN)10.3390/informatics10020050 (DOI)2-s2.0-85163757868 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-06-30 (hanlid)

Available from: 2023-06-12 Created: 2023-06-12 Last updated: 2023-10-11Bibliographically approved
Basuony, M. A. .., Mohamed, E. K. .., Elragal, A. & Hussainey, K. (2022). Big data analytics of corporate internet disclosures. Accounting Research Journal, 35(1), 4-20
Open this publication in new window or tab >>Big data analytics of corporate internet disclosures
2022 (English)In: Accounting Research Journal, ISSN 1030-9616, E-ISSN 1839-5465, Vol. 35, no 1, p. 4-20Article in journal (Refereed) Published
Abstract [en]

Purpose: This study aims to investigate the extent and characteristics of corporate internet disclosure via companies’ websites as well via social media and networks sites in the four leading English-speaking stock markets, namely, Australia, Canada, the UK and the USA.

Design/methodology/approach: A disclosure index comprising a set of items that encompasses two facets of online disclosure, namely, company websites and social media sites, is used. This paper adopts a data science approach to investigate corporate internet disclosure practices among top listed firms in Australia, Canada, the UK and the USA.

Findings: The results reveal the underlying relations between the determining factors of corporate disclosure, i.e. profitability, leverage, liquidity and firm size. Profitability in its own has no great effect on the degree of corporate internet disclosure whether via company websites or social media sites. Liquidity has an impact on the degree of disclosure. Firm size and leverage appear to be the most important factors driving better disclosure via social media. American companies tend to be on the cutting edge of technology when it comes to corporate disclosure.

Practical implications: This paper provides new insights into corporate internet disclosure that will benefit all stakeholders with an interest in corporate reporting. Social media is an influential means of communication that can enable corporate office to get instant feedback enhancing their decision-making process.

Originality/value: To the best of the authors’ knowledge, this study is amongst few studies of corporate disclosure via social media platforms. This study has adopted disclosure index incorporating social media as well as applying data science approach in disclosure in an attempt to unfold how accounting could benefit from data science techniques.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2022
Keywords
Social media, Big data, Disclosure, Data science, Internet, Australia, Canada, UK, USA, Corporate disclosure
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-78093 (URN)10.1108/ARJ-09-2019-0165 (DOI)000535262000001 ()2-s2.0-85085003300 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-03-08 (joosat)

Available from: 2020-03-18 Created: 2020-03-18 Last updated: 2022-07-04Bibliographically approved
Osman, A. M. S., Elragal, A. & Ståhlbröst, A. (2022). Data-Driven Decisions in Smart Cities: A Digital Transformation Case Study. Applied Sciences, 12(3), Article ID 1732.
Open this publication in new window or tab >>Data-Driven Decisions in Smart Cities: A Digital Transformation Case Study
2022 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 3, article id 1732Article in journal (Refereed) Published
Abstract [en]

The relationship between big data analytics (BDA) and smart cities (SCs) has been addressed in several articles. However, few articles have investigated the influence of exploiting BDA in datadriven decision-making from an empirical perspective in a case study context. Accordingly, we aim to tackle this scarcity of case-study research addressing the interrelationships between SCs, BDA, anddecision-making. Filling this gap will shed light on the challenges and design principles that shouldbe considered in designing a BDA artifact in the domain of smart cities. We analyze a case study of a digital transformation project in Egypt. Results show a tangible positive effect of utilizing dataanalytics in support of the decision-making process.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2022
Keywords
digital transformation, smart cities, big data analytics, data-driven decision-making
National Category
Information Systems
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-89192 (URN)10.3390/app12031732 (DOI)000755975900001 ()2-s2.0-85124459966 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-02-09 (joosat)

Available from: 2022-02-08 Created: 2022-02-08 Last updated: 2023-09-04Bibliographically approved
Rizk, A., Ståhlbröst, A. & Elragal, A. (2022). Data-driven innovation processes within federated networks. European Journal of Innovation Management, 25(6), 498-526
Open this publication in new window or tab >>Data-driven innovation processes within federated networks
2022 (English)In: European Journal of Innovation Management, ISSN 1460-1060, E-ISSN 1758-7115, Vol. 25, no 6, p. 498-526Article in journal (Refereed) Published
Abstract [en]

Purpose

Within digital innovation, there are two significant consequences of the pervasiveness of digital technology: (1) the increasing connectivity is enabling a wider reach and scope of innovation structures, such as innovation networks and (2) the unprecedented availability of digital data is creating new opportunities for innovation. Accordingly, there is a growing domain for studying data-driven innovation (DDI), especially in contemporary contexts of innovation networks. The purpose of this study is to explore how DDI processes take form in a specific type of innovation networks, namely federated networks.

Design/methodology/approach

A multiple case study design is applied in this paper. We draw our analysis from data collected over six months from four cases of DDI. The within-analysis is aimed at constructing the DDI process instance in each case, while the crosscase analysis focuses on pattern matching and cross-case synthesis of common and unique characteristics in the constructed processes.

Findings

Evidence from the crosscase analysis suggests that the widely accepted four-phase digital innovation process (including discovery, development, diffusion and post-diffusion) does not account for the explorative nature of data analytics and DDI. We propose an extended process comprising an explicit exploration phase before development, where refinement of the innovation concept and exploring social relationships are essential. Our analysis also suggests two modes of DDI: (1) asynchronous, i.e. data acquired before development and (2) synchronous, i.e. data acquired after (or during) development. We discuss the implications of these modes on the DDI process and the participants in the innovation network.

Originality/value

The paper proposes an extended version of the digital innovation process that is more specifically suited for DDI. We also provide an early explanation to the variation in DDI process complexities by highlighting the different modes of DDI processes. To the best of our knowledge, this is the first empirical investigation of DDI following the process from early stages of discovery till postdiffusion.

Place, publisher, year, edition, pages
Emerald Group Publishing Limited, 2022
Keywords
data-driven innovation, data analytics, data science, innovation process, networks, analytics, smart cities, case studies
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-80853 (URN)10.1108/EJIM-05-2020-0190 (DOI)000586490900001 ()2-s2.0-85094166831 (Scopus ID)
Funder
EU, Horizon 2020, 645198
Note

Validerad;2022;Nivå 2;2022-04-13 (sofila)

Available from: 2020-09-21 Created: 2020-09-21 Last updated: 2022-05-10Bibliographically approved
Elgendy, N., Elragal, A. & Päivärinta, T. (2022). DECAS: A Modern Data-Driven Decision Theory for Big Data and Analytics. Journal of Decision Systems, 31(4), 337-373
Open this publication in new window or tab >>DECAS: A Modern Data-Driven Decision Theory for Big Data and Analytics
2022 (English)In: Journal of Decision Systems, ISSN 1246-0125, E-ISSN 2116-7052, Vol. 31, no 4, p. 337-373Article in journal (Refereed) Published
Abstract [en]

Decisions continue to be an essential topic of utmost importance in every research field and era. However, while decision research has extensively offered a wide range of theories, it remains delved in the past, and needs robustness to sustain the future of data-driven decision-making, encompassing topics and technologies such as big data, analytics, machine learning, and automated decisions. Nowadays, decision processes have evolved, the role of humans as decision makers has changed and become inevitably intertwined with the support of machines, rationalities are no longer limited in the same way, data has become an abundant commodity, and the optimizing of decisions is not so far-fetched a tale as it once was in classical times. Accordingly, there is a dire need for new theories to support new phenomena. This paper aims to propose a modern data-driven decision theory, DECAS, to support the new elements of today’s decisions. Our theory extends upon classical decision theory by proposing three main claims: the (big) data and analytics should be considered as separate elements along with the decision-making process, the decision maker, and the decision; the appropriate collaboration between the decision maker and the analytics (machine) can result in a “collaborative rationality,” extending beyond the bounded rationality which decision makers were classically characterized by; and finally, the proper integration of the five elements, and the correct selection of data and analytics, can lead to more informed, and possibly better, decisions.  Hence, the theory is elaborated in the paper, and introduced to some data-driven decision examples.

Place, publisher, year, edition, pages
Taylor & Francis, 2022
Keywords
Data-driven decision making, Big data, Analytics, Automated decisions, Decision theory, Algorithmic decisions
National Category
Information Systems, Social aspects
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-83033 (URN)10.1080/12460125.2021.1894674 (DOI)000626975800001 ()2-s2.0-85114626599 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-06-30 (sofila);

Funder: ITEA3 (Project Oxilate)

Available from: 2021-02-22 Created: 2021-02-22 Last updated: 2023-05-08Bibliographically approved
Bakumenko, A. & Elragal, A. (2022). Detecting Anomalies in Financial Data using Machine Learning Algorithms. Systems, 10(5), Article ID 130.
Open this publication in new window or tab >>Detecting Anomalies in Financial Data using Machine Learning Algorithms
2022 (English)In: Systems, E-ISSN 2079-8954, Vol. 10, no 5, article id 130Article in journal (Refereed) Published
Abstract [en]

Bookkeeping data free of fraud and errors is a cornerstone of legitimate business operations. Highly complex and laborious financial auditors’ work calls for finding new solutions and algorithms to ensure the correctness of financial statements. Both supervised and unsupervised machine learning (ML) techniques, nowadays, are being successfully applied to detect fraud and anomalies in data. In accounting, it is a long-established problem to detect financial misstatements deemed anomalies in General Ledger (GL) data. Currently, widely used techniques such as random sampling and manual assessment of bookkeeping rules become challenging and unreliable due to increasing data volumes and unknown fraudulent patterns. To address the sampling risk and financial audit inefficiency, we applied seven supervised ML techniques inclusive of Deep Learning and two unsupervised ML techniques such as Isolation Forest and Autoencoders. We trained and evaluated our models on a real-life GL dataset and used data vectorization to resolve journal entry size variability. The evaluation results showed that the best trained supervised and unsupervised models have high potential in detecting predefined anomaly types as well as in efficiently sampling data to discern higher-risk journal entries. Based on our findings, we discussed possible practical implications of the resulting solutions in the accounting context.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
general ledger, accounting, anomaly detection, machine learning
National Category
Computer Systems
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-92592 (URN)10.3390/systems10050130 (DOI)000873811200001 ()2-s2.0-85140607821 (Scopus ID)
Note

Validerad;2022;Nivå 2;2022-09-02 (hanlid)

Available from: 2022-08-19 Created: 2022-08-19 Last updated: 2022-11-11Bibliographically approved
Elgendy, N., Elragal, A., Ohenoja, M. & Päivärinta, T. (2022). Ex-Post Evaluation of Data-Driven Decisions: Conceptualizing Design Objectives. In: Perspectives in Business Informatics Research: 21st International Conference on Business Informatics Research, BIR 2022, Rostock, Germany, September 21–23, 2022, Proceedings. Paper presented at 21st International Conference on Business Informatics Research (BIR 2022), Rostock, Germany, September 21–23, 2022 (pp. 18-34). Springer Nature
Open this publication in new window or tab >>Ex-Post Evaluation of Data-Driven Decisions: Conceptualizing Design Objectives
2022 (English)In: Perspectives in Business Informatics Research: 21st International Conference on Business Informatics Research, BIR 2022, Rostock, Germany, September 21–23, 2022, Proceedings, Springer Nature, 2022, p. 18-34Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses a need for developing ex-post evaluation for data-driven decisions resulting in collaboration between humans and machines. As a first step of a design science project, we propose four design objectives for an ex-post evaluation solution, from the perspectives of both theory (concepts from the literature) and practice (through a case of industrial production planning): (1) incorporate multi-faceted decision evaluation criteria across the levels of environment, organization, and decision itself and (2) acknowledge temporal requirements of the decision contexts at hand, (3) define applicable mode(s) of collaboration between humans and machines to pursue collaborative rationality, and (4) enable a (potentially automated) feedback loop for learning from the (discrete or continuous) evaluations of past decisions. The design objectives contribute by supporting development of solutions for the observed lack of ex-post methods for evaluating data-driven decisions to enhance human-machine collaboration in decision making. Our future research involves design and implementation efforts through on-going industry-academia cooperation.

Place, publisher, year, edition, pages
Springer Nature, 2022
Series
Lecture Notes in Business Information Processing (LNBIP), ISSN 1865-1348, E-ISSN 1865-1356 ; 462
Keywords
Data-driven decisions, ex-post evaluation, design objectives, collaborative rationality, human-machine collaboration
National Category
Computer Systems
Research subject
Information systems
Identifiers
urn:nbn:se:ltu:diva-92478 (URN)10.1007/978-3-031-16947-2_2 (DOI)2-s2.0-85138781306 (Scopus ID)
Conference
21st International Conference on Business Informatics Research (BIR 2022), Rostock, Germany, September 21–23, 2022
Note

ISBN för värdpublikation: 978-3-031-16946-5; 978-3-031-16947-2

Available from: 2022-08-15 Created: 2022-08-15 Last updated: 2022-12-13Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-4250-4752

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