Editor's Comments: Producing Significant Research

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Burton-Jones, Andrew; Gray, Peter; Majchrzak, Ann

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Topics: data quality artificial intelligence filter bubble social media citizen science

Methods: field study machine learning computational algorithm field experiment applicability check

Six Inversion Strategies for Avoiding Rejection in Academic Publishing: Lessons from the IS Discipline

2023 | Communications of the Association for Information Systems | Citations: 0

Authors: Ciriello, Raffaele; Thatcher, Jason

Abstract: The publication process in many academic disciplines, including in Information ... Expand

Abstract: The publication process in many academic disciplines, including in Information Systems (IS), can seem arduous and unpredictable, particularly for early career researchers. While the literature offers plentiful guidance on how to pursue publication acceptance, this paper offers a crisp summary of common mistakes that lead to rejection and provides six actionable inversion strategies for avoiding them. Namely, when preparing a paper, we recommend you (1) abstain from methodological promiscuity and (2) never overclaim (but try not to underclaim either); When submitting a paper, it is a good idea to (3) steer clear of bootlicking and (3) avoid sloppiness; and, after receiving the reviews, you should (5) forego belligerence, and (6) stop flogging a dead horse. These inversion strategies can help early career researchers better navigate the review process, increasing the chances of their papers maturing, and helping to avoid mistakes that lower the chance of publishing in high-quality IS journals. Collapse

Topics: IT career electronic mail social media

Methods: mixed method experiment applicability check design science literature study

Threefold Sustainable Neighborhood Energy Systems: Depicting Social Criteria in Decision Support Systems

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Hart, Maria; Eckhoff, Sarah; Schäl, Ann-Kristin; Breitner, Michael H.

Abstract: Despite the consensus that considering social factors is as important as econom ... Expand

Abstract: Despite the consensus that considering social factors is as important as economic and environmental dimensions for long-term successful, sustainable energy development projects, there is a lack of quantifiable assessments in multi-energy simulation decision support systems. Therefore, we applied a design science research approach to develop an energy system simulator that includes all three dimensions of sustainability. Based on a rigorous literature review and expert interviews, we establish a framework for assessing social sustainability. We then implement the derived criteria and indicators in the open-access software NESSI and validate it in an applicability check for a Madagasi neighborhood. With our framework, we aim to provide guidance to researchers and stakeholders on incorporating the social sustainability dimension into their approaches, tools, and decision-making process. The user-friendly, web-based simulation software enables various stakeholders to explore the interrelationships of threefold sustainability in specific energy systems. KeywordsSocial sustainability, multi-energy system simulation, decision support system, design science research, sustainable development goals. Collapse

Topics: energy information system decision support system open access usability IT security threat

Methods: literature study qualitative interview design science literature sample applicability check

Empowering Users with Narratives: Examining the Efficacy of Narratives for Understanding Data-Oriented Conceptual Models

2023 | Information Systems Research | Citations: 0

Authors: Hvalshagen, Merete; Lukyanenko, Roman; Samuel, Binny M.

Abstract: With the ongoing digitalization of human society, regular employees (e.g., nonin ... Expand

Abstract: With the ongoing digitalization of human society, regular employees (e.g., noninformation technology experts) become increasingly autonomous and proactive in using organizational data and information technologies to facilitate data-driven actions and insights. This growing autonomy of regular employees (dubbed here, empowered users) in using information technologies creates new challenges. As most empowered users lack sophisticated information technology skills, they struggle to find and access relevant data, understand their meaning, and extract and adapt them to meet their needs. We propose a powerful way to support empowered users with a combination of conceptual models and narratives. A data narrative is a descriptive and textual representation of one or more aspects of data in a domain that is organized and presented as a connected sequence of sentences in a natural language. We assess the efficacy of narrative representations for conceptual modeling cardinality constraints, which are an essential part of understanding data. We conducted two laboratory experiments and found a positive and strong effect for narratives: cardinality constraints that were described in the narratives were better understood than constraints only represented in the model (i.e., script). This effect was robust across cardinalities, measures, script complexities, and familiarity levels with the conceptual modeling grammar. History: Manju Ahuja, Senior Editor; Choon-Ling Sia, Associate Editor. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1141. Collapse

Topics: data management data literacy database system IT workforce analytical information system

Methods: conceptual modelling experiment entity relationship modeling design science parametric test

Theories: cognitive load theory

The Vicious Circles of Skill Erosion: A Case Study of Cognitive Automation

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Rinta-Kahila, Tapani; Penttinen, Esko; Salovaara, Antti; Soliman, Wael; Ruissalo, Joona

Abstract: Cognitive automation powered by advanced intelligent technologies is increasing ... Expand

Abstract: Cognitive automation powered by advanced intelligent technologies is increasingly enabling organizations to automate more of their knowledge work tasks. Although this often offers higher efficiency and lower costs, cognitive automation exacerbates the erosion of human skill and expertise in automated tasks. Accepting the erosion of obsolete skills is necessary to reap the benefits of technology-however, the erosion of essential human expertise is problematic if workers remain accountable for tasks for which they lack sufficient understanding, rendering them incapable of responding if the automation fails. Though the phenomenon is widely acknowledged, the dynamics behind such undesired skill erosion are poorly understood. Thus, taking the perspective of sociotechnical systems, we conducted a case study of an accounting firm that had experienced skill erosion over a number of years due to reliance on their software's automated functions. We synthesized our findings using causal loop modeling based on system dynamics. The resulting dynamic model explains skill erosion via an interplay between humans' automation reliance, complacency, and mindful conduction. It shows how increasing reliance on automation fosters complacency at both individual and organizational levels, weakening workers' mindfulness across three work task facets (activity awareness, competence maintenance, and output assessment), resulting in skill erosion. Such skill erosion may remain obscure, acknowledged by neither workers nor managers. We conclude by discussing the implications for theory and practice and identifying directions for future research.Walter Fernandez was the accepting senior editor. This research article was submitted on August 17, 2021 and underwent three revisions.Tapani Rinta-Kahila is a lecturer of business information systems at The University of Queensland Business School. His research focuses on issues around the implementation and management of artificial intelligence systems in organizations, unintended consequences of IT use, and decommissioning of organizational IT systems. Tapani obtained his doctoral degree from Aalto University School of Business in 2018 with an award-winning dissertation. Rinta-Kahila's research has appeared in top IS journals, including MIS Quarterly, Journal of the Association for Information Systems, European Journal of Information Systems, Information & Management, and MIS Quarterly Executive. His work has also informed practice through industry reports co-authored with The SAP Institute for Digital Government.Esko Penttinen is an associate professor (tenured) of information systems at the Aalto University School of Business. Penttinen holds a DSc in information systems science and an MSc in economics from the Helsinki School of Economics. Penttinen's research helps organizations understand the intricacies related to the design and implementation of various forms of artificial intelligence, harnessing its benefits and avoiding the pitfalls. Penttinen is an avid student of the interplay between humans and machines, curious to generate insights on how to coordinate work tasks efficiently between the two. Penttinen's research has also addressed the more traditional information systems topics such as organizational governance issues related to outsourcing and virtual work. Penttinen's main practical expertise lies in the assimilation and economic implications of interorganizational information systems, focusing on application areas such as electronic financial systems, government reporting, and electronic invoicing. Penttinen's research has appeared in leading IS outlets such as MIS Quarterly, Joona Ruissalo is a doctoral researcher in information systems science at the Aalto University School of Business. His research explores how the design, development, and use of cognitive technologies such as robotic process automation and artificial intelligence transform professional knowledge-intensive work. He currently studies these sociotechnical issues in the domain of financial accounting. Collapse

Topics: accounting mindfulness artificial intelligence knowledge work process automation system

Methods: qualitative interview case study applicability check personal interview triangulation

Theories: sociotechnical systems theory socio technical theory

HOW PARTS CONNECT TO WHOLE IN BUILDING DIGITAL GENERATIVITY IN DIGITAL PLATFORM ECOSYSTEMS

2023 | European Conference On Information Systems | Citations: 0

Authors: Sun, Jiamei; Xu, Dongming; Karanasios, Stan

Abstract: Generativity drives digital innovation and platform growth by engaging many oth ... Expand

Abstract: Generativity drives digital innovation and platform growth by engaging many other businesses with diverse digital skills and resources in a digital platform. As the proliferation of generativity research grows, the Information Systems (IS) literature demonstrates the basic understanding of this notion in the areas of properties of digital technologies, social events, and/or the interaction between these two without an integrated view of how generativity is raised to enable the digital innovation. Therefore, considering that digital platforms are a kind of ecosystem, we aim to develop a new understanding of this emerging phenomenon by employing a holistic perspective. Through the information ecology theoretical lens, we develop a digital generativity process model that explains how the technological and social resources interact to generate perpetual digital innovation in digital platform ecosystems (DPE). This study contributes to generativity research by providing a dynamic and holistic view of generativity formalization in DPEs. Collapse

Topics: innovation management digital ecosystem digital innovation digital platform technological change

Methods: business process modeling qualitative content analysis literature sample grounded theory applicability check

How to Design More Empathetic Recommender Systems in Social Media

2022 | International Conference on Information Systems | Citations: 0

Authors: Bonenberger, Lukas; Graf-Drasch, Valerie; Lanzl, Julia

Abstract: Social media's value proposition heavily relies on recommender systems suggesti ... Expand

Abstract: Social media's value proposition heavily relies on recommender systems suggesting products to buy, events to attend, or people to connect with. These systems currently prioritize user engagement and social media providers' profit generation over individual users' well-being. However, making these systems more "empathetic" would benefit social media providers and content creators as users would use social media more often, longer, and increasingly recommend it to other users. By way of a design science research approach, including twelve interviews with system designers, social media experts, psychologists, and users, we develop user-centric design knowledge on making recommender systems in social media more "empathetic." This design knowledge comprises a conceptual framework, four meta-requirements, and six design principles. It contributes to the research streams "digital responsibility" and "IS for resilience" and provides practical guidance in developing socially responsible recommender systems as next-generation social media services. Collapse

Topics: social media recommender system user acceptance privacy evaluation criteria

Methods: qualitative interview design principle design science design requirement literature study

Theories: sociotechnical systems theory

PERSONAL DATA PROTECTION RULES! GUIDELINES FOR PRIVACY-FRIENDLY SMART ENERGY SERVICES

2022 | European Conference On Information Systems | Citations: 0

Authors: Gerlach, Jana; Scheunert, Alexandra; Breitner, Michael H.

Abstract: Privacy-friendly processing of personal data is proving to be increasingly chal ... Expand

Abstract: Privacy-friendly processing of personal data is proving to be increasingly challenging in today's energy systems as the amount of data grows. Smart energy services provide value creation and co-creation by processing sensible user data collected from smart meters, smart home devices, storage systems, and renewable energy plants. To address this challenge, we analyze key topics and develop design requirements and design principles for privacy-friendly personal data processing in smart energy services. We identify these key topics through expert interviews, text-mining, and topic modelling techniques based on 149 publications. Following this, we derive our design requirements and principles and evaluate these with experts and an applicability check with three real-world smart energy services. Based on our results and findings, we establish a further research agenda consisting of five specific research directions. Collapse

Topics: privacy legal context General Data Protection Regulation data protection knowledge base

Methods: qualitative interview archival research natural language processing design science topic model

Value-driven IT Project Portfolio Management: Process Model, Evaluation Framework, and Decision Support

2022 | International Conference on Information Systems | Citations: 0

Authors: Karrenbauer, Christin; Breitner, Michael H.

Abstract: Companies must optimize their information technology (IT) project portfolio to ... Expand

Abstract: Companies must optimize their information technology (IT) project portfolio to achieve goals. However, IT projects often exceed resources and do not create their promised value, for example, because of missing structured processes and evaluation methods. Continuous IT portfolio management is thus of importance and a critical business activity to reach value-driven goals. Guided by Design Science Research with literature reviews and expert interviews, we develop, evaluate, and adjust an IT project portfolio management process model, a holistic IT project evaluation framework, and implement a decision support system prototype. Our results and findings synthesize and extend previous research and expert opinions and guide decision-makers to make more informed and objective IT project portfolio management decisions aligned with optimal value creation. Furthermore, we deduce new research opportunities for IT project portfolio management process models, decision tools, and evaluation frameworks. Introduction and MotivationInformation technology (IT) impacts a company's long-term performance and competitiveness and forms a critical success factor (Bezdrob et al. 2020). Thereby, IT projects are characterized by complexity, crossfunctionality, dynamics, non-routine, temporality, and uncertainty. These make IT project portfolio management (ITPPM) a challenging task (Chiang and Nunez 2013;Kester et al. 2011). Selecting the "right" IT projects is essential to create optimal value. Nevertheless, ITPPM is often unstructured and decisions are made ad hoc instead of long-term planning. Thus, many IT projects deviate from their defined objectives, are not completed, or completely fail (Varajão and Trigo 2016). According to the Project Management Institute (PMI 2017a), roughly $97 million US Dollars per $ 1 billion investments in IT projects are wasted. Similarly, Lee et al. (2021) refer that on average 66% of implemented IT projects are more expensive and 33% require longer as planned. Failed IT projects due to weak ITPPM processes lead to resource losses and exceedances (e.g., Hershey: $150 in lost sales, 19% drop in earnings), project abandonment (e.g., Dell: $200 million), or bankruptcies (e.g., FoxMeyer) (Fadlalla and Amani 2015; Hughes et al. 2017). Thus, companies need adequate and resilient methods for the critical business activity of ITPPM. Considering existing interdependencies and constraints, these methods ensure that selected IT projects fit the company's strategy and create value (Chiang and Nunez 2013;Kester et al. 2011). If departments and functions are aligned to strategy, IT projects are more likely to be completed successfully ITPPM: Process Model, Evaluation Framework, and DSS Forty-Third International Conference on Information Systems, Copenhagen 2022 2 (PMI 2017a). Additionally, uniform IT guidelines stabilize the performance of IT projects (Martin 2006). However, many decisions are motivated by subjectivity, personal experiences and perceptions. Often uniform ITPPM methods, evaluation and selection criteria, and their consistent usage are missing (Varajão and Trigo 2016). An ITPPM tool can further support decision-makers (DM) to ensure efficient, transparent, and consistent decisions more rapidly (Caniëls and Bakens 2012;Killen et al. 2020).Even though ITPPM is considered to be a structured process, besides best practices for PPM (e.g., PMI 2017b) only few ITPPM process models exist in literature which are rather perfunctory. Mostly, existing models encompass three (Ajjan et al. 2016) to five main phases (Archer and Ghasemzadeh 1999). Typically, they include activities such as the definition of baseline conditions (Alaeddini and Mir-Amini 2020), IT project identification (Montgomery 2007), evaluation (Chiang and Nunez 2013), selection (Ajjan et al. 2016), and monitoring with adaptions (Miller 2002). Other than that, existing models vary in their phases and activities. Moreover, the procedure is mostly sequential with no or few re-cycles between and within phases. To address this and better meet IT project characteristics, we propose an integrated ITPPM process model that allows an objective and value-driven ITPPM. To do this, we synthesize existing process models and expand them with expert interviews. It comprises activities from different models combined within an integrated one. Unlike many existing models, it also allows re-cycles between and within individual phases and allows to address peculiarities of IT projects. It enables, e.g., a flexible reaction to changes and uncertainties and a discussion between stakeholders. In addition, IT project evaluation, prioritization, and selection are critical activities (Varajão and Trigo 2016). Several methods have been researched on this topic ranging from rather simple financial project selections (Rosacker and Olson 2009), and balance scorecard approaches (Asosheh et al. 2010), to more advanced optimization models (Cho and Shaw 2013) and fuzzy programming (Heidary et al. 2020), among others (Mohagheghi et al. 2019). In rather simple methods, IT project decisions are often based only on the evaluations. In more advanced methods, several constraints are considered, however, the project evaluation is often already available and not described in more detail (Archer and Ghasemzadeh 1999). We combine both approaches and develop a holistic scoring framework that enables a quantification of subjective estimations and thus an objective evaluation of IT projects of different types and sizes. The scoring results then serve as an input for our decision support system (DSS) prototype. It allows to select from a large number of IT project proposals those that maximize the value proposition, considering interdependencies and limitations. In doing so, we mitigate disadvantages resulting from the sole use of scoring approaches (Mohagheghi et al. 2019) and decisions are not made on an individual project level but consider other factors such as resource constraints and dependencies. Our main contributions are our three artifacts, an integrated ITPPM process model, a holistic scoring framework, and our DSS prototype. These Level 1 and Level 2 artifacts (Gregor and Hevner 2013) support ITPPM and enhance and contribute to the knowledge base. We provide knowledge to increase transparency and objectivity to make more informed IT portfolio decisions and thus decrease IT project failure rates, minimize subject manipulations, increase value creation, and goal achievement. We follow a Design Science Research (DSR) process (Hevner et al. 2004) to address the following research questions (RQ):RQ 1: What activities constitute a value-driven ITPPM process? RQ 2: How can IT projects (and proposals) be uniformly evaluated to generate a value-driven IT portfolio? RQ 3: How can a DSS support IT project selection and scheduling?First, we describe the theoretical background of ITPPM. Then, we motivate our DSR-oriented research design. Afterwards, we present our results and findings of our literature reviews and expert interviews, including an ITPPM process model and a scoring framework. Then, we introduce our DSS prototype and provide an applicability check. Finally, we discuss our results and findings, their implications, deduce recommendations for theory and practice and a research agenda, present limitations, and conclusions. Collapse

Topics: IT project project management decision support system value creation knowledge base

Methods: qualitative interview business process modeling design artifact literature study design science

How the Application of Machine Learning Systems Changes Business Processes: A Multiple Case Study

2022 | European Conference On Information Systems | Citations: 0

Authors: Kunz, Pascal; Jussupow, Ekaterina; Spohrer, Kai; Heinzl, Armin

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Topics: business process management task characteristic IS value task structure IS impact

Methods: qualitative interview archival research qualitative coding group interview design artifact