Orchestrating a digital platform ecosystem to address societal challenges: A robust action perspective

2022 | Journal of Information Technology | Citations: 0

Authors: Addo, Atta

Abstract: Orchestration of digital platform ecosystems has been well examined in the conte ... Expand

Abstract: Orchestration of digital platform ecosystems has been well examined in the context of markets and the private sector where an orchestrator is a resourceful firm exploiting commercial opportunities in an industry. However, little is known about how it occurs when a government organization orchestrates to address societal challenges. We build upon the construct of ?robust action??identified with chess masters who play to advance a broad strategy while simultaneously maintaining flexibility?to explain how orchestration by a government organization might overcome initiation, stabilization, expansion, and meta-governance challenges through digital platform-enabled participative architecture, multivocal inscriptions, and distributed experimentation. Evidence for our theoretical framework is drawn from empirical studies of the Aadhaar, a platform ecosystem orchestrated to address multiple societal challenges stemming from identity and its management. Collapse

Topics: societal challenge digital ecosystem innovation management digital innovation digital platform

Methods: theory development qualitative content analysis literature sample archival research

Theories: action theory socio technical theory sociotechnical systems theory

Creating Humanistic Value with Process Mining for Improving Work Conditions - A Sociotechnical Perspective

2020 | International Conference on Information Systems | Citations: 0

Authors: Tang, Willi; Matzner, Martin

Abstract: Process mining has emerged as a specific approach to data mining which leverages ... Expand

Abstract: Process mining has emerged as a specific approach to data mining which leverages historical process data from process-aware information systems to derive insights and visual representations of an organisations business processes. It affords companies the ability to identify critical paths. Research has so far focused on analysing the instrumental value of process mining on firm performance and how to improve and extend this set of tools for more accurate and comprehensive predictions. However, humanistic outcomes of process mining usage (e.g., job satisfaction, workload) have largely been neglected. In this research-in-progress report, we analyse process mining from a sociotechnical perspective. We use work systems theory to conceptualise how process mining can be used to improve work conditions through the alignment of processes and employees within a work system. Collapse

Topics: process mining business process management job satisfaction social network IS discipline

Methods: event log social network analysis qualitative interview literature study psychometrics

Theories: work systems theory

Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research

2018 | Journal of the Association for Information Systems | Citations: 0

Authors: Maass, Wolfgang; Parsons, Jeffrey; Purao, Sandeep; Storey, Veda C; Woo, Carson

Abstract: The era of big data provides many opportunities for conducting impactful researc ... Expand

Abstract: The era of big data provides many opportunities for conducting impactful research from both datadriven and theory-driven perspectives. However, data-driven and theory-driven research have progressed somewhat independently. In this paper, we develop a framework that articulates important differences between these two perspectives and propose a role for information systems research at their intersection. The framework presents a set of pathways that combine the datadriven and theory-driven perspectives. From these pathways, we derive a set of challenges, and show how they can be addressed by research in information systems. By doing so, we identify an important role that information systems research can play in advancing both data-driven and theory-driven research in the era of big data. Collapse

Topics: big data analytical information system

Methods: survey design triangulation theory development

Views on the Past, Present, and Future of Business and Information Systems Engineering

2018 | Business & Information Systems Engineering | Citations: 0

Authors: van der Aalst, Wil M. P.; Becker, Jörg; Bichler, Martin; Buhl, Hans Ulrich; Dibbern, Jens; Frank, Ulrich; Hasenkamp, Ulrich; Heinzl, Armin; Hinz, Oliver; Hui, Kai-Lung; Jarke, Matthias; Karagiannis, Dimitris; Kliewer, Natalia; König, Wolfgang; Mendling, Jan; Mertens, Peter; Rossi, Matti; Voss, Stefan; Weinhardt, Christof; Winter, Robert; Zdravkovic, Jelena

Abstract: ... Expand

Abstract: Collapse

Topics: marketing management information system use

Methods: design science literature study action research machine learning mathematical model

Intelligent Knowledge Beyond Data Mining: Influences of Habitual Domains

2014 | Communications of the Association for Information Systems | Citations: 0

Authors: Yu, Xiaodan; Shi, Yong; Zhang, Lingling; Nie, Guangli; Huang, Anqiang

Abstract: Data mining is a useful analytic method and has been increasingly used by organi ... Expand

Abstract: Data mining is a useful analytic method and has been increasingly used by organizations to gain insights from largescale data. Prior studies of data mining have focused on developing automatic data mining models that belong to first-order data mining. Recently, researchers have called for more study of the second-order data mining process. Second-order data mining process is an important step to convert data mining results into intelligent knowledge, i.e., actionable knowledge. Specifically, second-order data mining refers to the post-stage of data mining projects in which humans collectively make judgments on data mining models’ performance. Understanding the second-order data mining process is valuable in addressing how data mining can be used best by organizations in order to achieve competitive advantages. Drawing on the theory of habitual domains, this study developed a conceptual model for understanding the impact of human cognition characteristics on second-order data mining. Results from a field survey study showed significant correlations between habitual domain characteristics, such as educational level and prior experience with data mining, and human judgments on classifiers’ performance. Collapse

Topics: database system implicit knowledge explicit knowledge knowledge management problem solving

Methods: machine learning survey descriptive statistic computational algorithm conceptual modelling

Facilitating Collaboration with Problem Structuring Methods: A Case Study of an Inter-Organisational Construction Partnership

2008 | Group Decision and Negotiation | Citations: 0

Authors: Franco, L. Alberto

Abstract: Despite the potential of inter-organisational collaboration to create ‘collabora ... Expand

Abstract: Despite the potential of inter-organisational collaboration to create ‘collaborative advantage’ among participant organisations, not all collaborations realise this potential due to the complexities and challenges faced by potential collaborators. To address these difficulties and increase the likelihood of collaborative success, different forms of intervention approaches for fostering inter-organisational collaboration has been advocated by collaboration researchers and practitioners. These intervention approaches all facilitate interaction and consensus formation among the participants. However their ‘added value’ is procedural rather than substantive in nature. They do not incorporate tools which can enable participants to structure the complexity of the web of factors that are implicated in their collaboration, and thus make it more manageable. This paper argues that problem structuring methods (PSMs), a family of model-based approaches to group decision and negotiation support, are a form of intervention which can provide a balanced attention to both the process and the content of inter-organisational collaboration, and reports the experience of applying a particular PSM to an inter-organisational collaborative partnership in the UK construction industry. Drawing on the rich data generated from the intervention, the paper discusses the impact of the PSM in supporting the joint appreciation activities carried out by collaborators to address their problematic situation. Implications of the experience for the research and practice of PSMs within collaborative contexts are then presented. Collapse

Topics: organization learning group decision support system

Methods: grounded theory survey action research case study personal interview

Incorporating domain knowledge into data mining classifiers: An application in indirect lending

2008 | Decision Support Systems | Citations: 25

Authors: Sinha, Atish P.; Zhao, Huimin

Abstract: Data mining techniques have been applied to solve classification problems for a ... Expand

Abstract: Data mining techniques have been applied to solve classification problems for a variety of applications such as credit scoring, bankruptcy prediction, insurance underwriting, and management fraud detection. In many of those application domains, there exist human experts whose knowledge could have a bearing on the effectiveness of the classification decision. The lack of research in combining data mining techniques with domain knowledge has prompted researchers to identify the fusion of data mining and knowledge-based expert systems as an important future direction. In this paper, we compare the performance of seven data mining classification methods—naive Bayes, logistic regression, decision tree, decision table, neural network, k-nearest neighbor, and support vector machine—with and without incorporating domain knowledge. The application we focus on is in the domain of indirect bank lending. An expert system capturing a lending expert's knowledge of rating a borrower's credit is used in combination with data mining to study if the incorporation of domain knowledge improves classification performance. We use two performance measures: misclassification cost and AUC (area under the curve). A 2×7 factorial, repeated-measures ANOVA, with the two factors being domain knowledge (present or absent) and data mining method (seven methods), as well as a special statistical test for comparing AUCs, is used for analyzing the results. Analysis of the results reveals that incorporation of domain knowledge significantly improves classification performance with respect to both misclassification cost and AUC. There is interaction between classification method and domain knowledge. Incorporation of domain knowledge has a higher influence on performance for some methods than for others. Both measures—misclassification cost and AUC—yield similar results, indicating that the findings of the study are robust. Collapse

Topics: expert system knowledge base

Methods: machine learning decision tree classification classification method artificial neural network support vector machine

Explaining Ethical IT Behavior, Judgment and Awareness Using a Domain Theory Context

2007 | Americas Conference on Information Systems | Citations: 0

Authors: Schilavy, Richard

Abstract: Unethical information technology (IT) behavior can lead to severe consequences ... Expand

Abstract: Unethical information technology (IT) behavior can lead to severe consequences in the workplace. Inappropriate and unethical use of IT is a prominent concern for managers governing their employee's workplace behavior, having significant impacts for organizations beyond a decrease in productivity. However, moral values and norms of employees may not be consistent with organizational policies and ethical codes. The study investigates the context dependence of ethical IT behaviors and how people organization ethical IT dilemmas affect people's behaviors. A survey method is used to investigate this phenomenon, using ethical dilemma scenarios Keywords Collapse

Methods: survey

Theories: theory of planned behavior

Morality and Computers: Attitudes and Differences in Moral Judgments

1999 | Information Systems Research | Citations: 38

Authors: Gattiker, Urs E.; Kelley, Helen

Abstract: Business ethics is an emerging area of research in many subfields of management, ... Expand

Abstract: Business ethics is an emerging area of research in many subfields of management, including information systems (IS). Empirical IS research has studied differences in users' attitudes and in moral judgments regarding ethical computer-related behavior. This study applied the “domains of morality” approach to determine how users felt about certain computer-related behaviors. Vignettes describing ethical dilemmas involving computer technology (e.g., uploading a computer virus on an electronic network/bulletin board system) were presented to a sample of Internet users. The research findings offered several interesting and, in some cases, unexpected results. The empirical results indicated that older computer users have a less permissive sense of what is right and wrong for an illegal game. When computers were used to test a banned game, men and women differed in their assessment of its appropriateness. A surprising finding was that participants were not likely to endorse civil liberties, and were more concerned about the harm to, and violations of, social norms when the scenario described a situation involving a computer virus. How users perceive, prejudge, and discriminate computer ethics and abusive computer actions raises numerous questions and implications for IS researchers, IS practitioners, and policy makers. The results of this study foster a better understanding of Internet users' moral categorization of specific computer behaviors and, hopefully, help to further reduce risks and vulnerabilities of systems by identifying computer actions deemed ethically acceptable by users. Opportunities for IS researchers to further explore this timely issue are also discussed. Collapse

Topics: information system use internet technology data encryption video game individual characteristic

Methods: survey multivariate analysis of variance F-test chi squared test post-hoc analysis