Future Information Systems Research Research Articles

Large Language Models for Conducting Advanced Text Analytics Information Systems Research

The exponential growth of digital content has generated massive textual datasets, necessitating the use of advanced analytical approaches. Large Language Models (LLMs) have emerged as tools that are capable of processing and extracting insights from massive unstructured textual datasets. However, how to leverage LLMs for text analytics Information Systems (IS) research is currently unclear. To assist the IS community in understanding how to operationalize LLMs, we propose a Text Analytics for Information Systems Research (TAISR) framework. Our proposed framework provides detailed recommendations grounded in IS and LLM literature on how to conduct meaningful text analytics IS research for design science, behavioral, and econometric streams. We conducted three business intelligence case studies using our TAISR framework to demonstrate its application in several IS research contexts. We also outline the potential challenges and limitations of adopting LLMs for IS. By offering a systematic approach and evidence of its utility, our TAISR framework contributes to future IS research streams looking to incorporate powerful LLMs for text analytics.

User Engagement and Beyond: A Conceptual Framework for Engagement in Information Systems Research

Humans are considered “engaged” once they invest personal resources such as energy, time, or attention beyond a required level. This engagement state occurs when feeling connected to another actor or an object. In information systems (IS) research, engagement is recognized in the concept of ‘user engagement’, which has for many years successfully been employed to understand interaction patterns and user reactions. However, as IS artifacts evolve, we observe contemporary phenomena that are no longer covered by the existing concept: For one, IS have developed from passive resources (such as websites) to human-like actors (such as conversational, nowadays often large language model (LLM)-based, agents) so that a pure ‘user engagement’ perspective does not capture new affordances for engagement with active counterparts. Secondly, IS increasingly act as intermediaries into other engagement objects (e.g., organizations) that are the ultimate target of engagement. Thus, IS research may benefit from a broader perspective on engagement. Our work systematically draws on a structured literature review across adjacent academic disciplines and an in-depth qualitative analysis to develop a more comprehensive conceptual framework for engagement. With this framework, we contribute a refined and broadened conceptual base for engagement and discuss how it can inform future IS research.

From Methodological Symmetry to Gaia: Latour’s Legacy and Untapped Potential for IS Research

A defining concern for information systems (IS) is how to theorize its primary object of study, the digital artifact. Historically, approaches in IS have oscillated between technological determinist and social determinist ones. Bruno Latour’s works contributed to recalibrating a more symmetrical treatment of the two, and he played a distinctive role in stimulating the evolution of empirically committed, process-oriented theorizing in the IS field. We commemorate Latour’s work by revisiting his early influence on IS research and discuss some of the direct and indirect influences on contemporary IS, such as infrastructure studies and sociomateriality. With the interest in agentic technologies via AI, Latour’s perspectives on technological agency are more relevant than ever. In addition, we explore ideas that are relevant to IS but have yet to be taken up, thus representing untapped theoretical and methodological potential for future IS research—for example, approaching data as circulating reference or how his work could contribute to sustainability discussions in IS.

Regulation of data-driven market power in the digital economy: Business value creation and competitive advantages from big data

Recent high-profile antitrust cases and policy proposals have put a spotlight on the relationship between firms’ access to big data and sustained competitive advantages in digital markets. In Europe, concerns about data-driven market power have led policymakers to propose far-reaching regulations of information technology (IT) in these markets. Despite the global policy relevance, the regulation of big data and its competitive effects has so far received little attention in information systems (IS) research. This article addresses this research gap by developing an overarching framework for future IS research on the role of IT for the regulation of data-driven market power. The proposed research framework builds upon a three-part analysis: First, we review the academic literature and show that there is extensive, although nuanced, empirical evidence for business value creation from big (user) data. Second, we draw on the resource-based view of the firm and recent policy reports to derive six facilitating factors that enable firms to establish market power based on sustained data-driven competitive advantages. Third, we characterize three regulatory approaches to govern market power and competition in data-driven digital markets: (i) empowering consumers, (ii) data openness, and (iii) limiting data scale. For each of these approaches, we highlight the key role of IT artifacts in mediating the effect of regulatory rules on actual practice.

Editorial: There Are Promises to Keep and Miles to Go Before I Leave…

Editorial: There Are Promises to Keep and Miles to Go Before I Leave…

Shocks and IS user behavior: a taxonomy and future research directions

PurposeJarring events, be they global crises such as COVID-19 or technological events such as the Cambridge Analytica data incident, have bullwhip effects on billions of people's daily lives. Such “shocks” vary in their characteristics. While some shocks cause, for example, widespread adoption of information systems (IS) as diverse as Netflix and Teams, others lead users to stop using IS, such as Facebook. To offer insights into the multifaceted ways shocks influence user behavior, this study aims to assess the status quo of shock-related literature in the IS discipline and develop a taxonomy that paves the path for future IS research on shocks.Design/methodology/approachThis study conducted a literature review (N = 70) to assess the status quo of shock-related research in the IS discipline. Through a qualitative study based on users who experienced shocks (N = 39), it confirmed the findings of previous literature in an illustrative IS research context. Integrating the findings of the literature review and qualitative study, this study informs a taxonomy of shocks impacting IS use.FindingsThis study identifies different ways that shocks influence user behavior. The taxonomy reveals that IS research could profit from considering environmental, private and work shocks and shedding light on positive shocks. IS research could also benefit from examining the urgency of shocks, as there are indications that this influences how and when individuals react to a specific shock.Originality/valueFindings complement previous rational explanations for user behavior by showing technology use can be influenced by shocks. This study offers a foundation for forward-looking research that connects jarring events to patterns of technology use.

Orchestrating the digital transformation of a business ecosystem

The nature, scope, and impact of digital transformation reaches well beyond the boundaries of a single firm. This suggests information systems research should consider how digital transformation unfolds within business ecosystems that consist of multiple interdependent firms, and how this process can ideally be managed. We pursue this research opportunity by introducing orchestration as a concept through which to view digital transformation in business ecosystems, and by presenting empirical insights from a longitudinal in-depth case study that highlights how a focal firm became the orchestrator of digital transformation in its business ecosystem. We explain that becoming an orchestrator of digital transformation occurs through three distinct phases: initiating, opening-up, and integrating. We also identify the interplay of activities by which a focal firm strategizes, mobilizes, and aligns other actors and their resources, as it orchestrates the digital transformation of its business ecosystem. We conclude by outlining how our work serves as an important foundation for future information systems research and offer managerial guidelines outlining how to orchestrate digital transformation processes within business ecosystems.

Interoperability in the era of digital innovation: An information systems research agenda

Digital innovation enables new forms of cyber-physical innovation based on digital business ecosystems. However, the realization of the opportunities arising from such innovation substantially hinges on interoperability, that is, the ability of two or more systems to exchange information and understand that exchanged information. While interoperability is a long-standing topic in research and practice alike and a valuable knowledge base exists, digital innovation poses novel challenges that might not be covered by conventional wisdom. Accordingly, we review the literature and, first, propose an organizing framework for existing interoperability knowledge involving the contextual conditions of low interoperability, the mechanisms to increase interoperability, and the associated outcomes. Second, we use the framework to identify previous research foci in the scholarly discourse about interoperability and to discuss the potential limitations of this past work in light of digital innovation. Third, we propose a research agenda that enables information systems (IS) research to address the identified limitations—involving conceptual, scoping, and methodological issues—and provide specific recommendations on how to address the identified issues in future IS research. Finally, we propose five major research topics for further inquiry by combining the challenges identified in current knowledge with the current shift toward hyper-connected ecosystems underlying digital innovation.

The effects of business analytics capability on circular economy implementation, resource orchestration capability, and firm performance

Today, most organizations are undergoing a digital transformation. At the same time, the gravity of environmental issues has put sustainability and the circular economy at the top of corporate agendas. To this end, information systems, in particular business analytics, are being highlighted as essential enablers of an accelerated circular economy transition. However, effectively managing this joint transformation is a challenge. Firms struggle to identify which organizational resources they should target and how those should be leveraged towards a firm-wide business analytics capability for circular economy. To address these questions, this study draws on recent literature dealing with smart circular economy and business analytics capabilities along with the resource-based and resource orchestration view to (1) create an instrument to measure firms’ business analytics capability for circular economy, and (2) examine the relationship among a circular economy-specific business analytics capability, circular economy implementation, resource orchestration capability, and firm performance. The proposed research model was tested using partial least squares structural equation modeling of survey data from 125 top-level managers at companies across Europe. The results show that firms with a strong business analytics capability have an increased resource orchestration capability and a greater ability to excel in the circular economy, resulting in improved organizational performance in building a more sustainable competitive advantage in an increasingly competitive business landscape. The effect of business analytics capability on firm performance is not direct but fully mediated through resource orchestration capability and circular economy implementation. The results empirically validate the proposed research model and offer pathways to future information systems research streams to support the operationalization of circular strategies. The study provides the first empirical evidence of a business analytics capability for circular economy and its effect on firm performance.

Qualitative comparative analysis in the information systems discipline: a literature review and methodological recommendations

PurposeQualitative Comparative Analysis (QCA) is a promising, powerful method that is increasingly used for IS research. However, the Information Systems (IS) discipline still lacks a shared understanding of how to conduct and report QCA. This paper introduces the fundamental concepts of QCA, summarizes the status quo, and derives recommendations for future research.Design/methodology/approachA descriptive literature review in major IS outlets summarizes how and why QCA has been used in the IS discipline, critically evaluates the status quo, and derives recommendations for future QCA studies.FindingsThe literature review reveals 32 empirical research articles in major IS journals that have used the QCA method. Articles applied QCA to a broad range of research topics at the individual and organizational levels, mainly as a standalone analysis for theory development, elaboration and testing. The authors also provide evidence that most published IS research articles do not take full advantage of the potential QCA, such as analyzing necessary causal conditions or testing the robustness of QCA results. The authors provide seven actionable recommendations for future IS research using QCA.Originality/valueThe literature review assesses the status quo of QCA’s application in the IS discipline and provides specific recommendations on how IS researchers can leverage the full potential of QCA.

Regulation of Data-driven Market Power in the Digital Economy: Business Value Creation and Competitive Advantages from Big Data

Recent high-profile antitrust cases and policy proposals have put a spotlight on the relationship between firms’ access to big data and sustained competitive advantages in digital markets. In Europe, concerns about data-driven market power have led policy makers to propose far-reaching regulations of information technology (IT) in these markets. Despite the global policy relevance, the regulation of big data and its competitive effects has so far received little attention in information systems (IS) research. This article addresses this research gap by developing an overarching framework for future IS research on the role of IT for the regulation of data-driven market power. The proposed research framework builds upon a three-part analysis: First, we review the academic literature and show that there is extensive, although nuanced, empirical evidence for business value creation from big (user) data. Second, we draw on the resource-based view of the firm and recent policy reports to derive six facilitating factors that enable firms to establish market power based on sustained data-driven competitive advantages. Third, we characterize three regulatory approaches to govern market power and competition in data-driven digital markets: (i) empowering consumers, (ii) data openness and (iii) limiting data scale. For each of these approaches, we highlight the key role of IT artifacts in mediating the effect of regulatory rules on actual practice.

The Usefulness of the Two-Step Normality Transformation in Retesting Existing Theories

The Two-Step normality transformation has been shown to reliably transform continuous variables toward normality. The procedure offers researchers a capable alternative to more prominent methods, such as winsorization, ranking, and power transformations. We demonstrate its utility in the context of the Productivity Paradox literature stream, which is renowned for inconsistent results. This paper demonstrates that the Two-Step normality transformation, which has not been used in Productivity Paradox research, may produce greater goodness-of-fit and affect theoretical understandings on the topic. We use a classic Productivity Paradox dataset to show that compared to the prominent normality transformations, the Two-Step produces unique findings, including 1) regression coefficients more closely resembling the original data, 2) different effect sizes and significance levels, and 3) strengthening evidence for fundamental theories in Productivity Paradox literature. We demonstrate results that challenge uncertainties about the relationship between IT investment and firm performance. Our results imply that the Two-Step procedure should be considered a viable transformation option in future information systems research.

Special Issue Editorial: Artificial Intelligence in Organizations: Implications for Information Systems Research

Artificial intelligence (AI) technologies offer novel, distinctive opportunities and pose new significant challenges to organizations that set them apart from other forms of digital technologies. This article discusses the distinct effects of AI technologies in organizations, the tensions they raise and the opportunities they present for information systems (IS) research. We explore these opportunities in term of four business capabilities: automation, engagement, insight/decision making and innovation. We discuss the differentiated effects that AI brings about and the implications for future IS research.

A text mining-based review of the literature on dynamic capabilities perspective in information systems research

PurposeWhile the dynamic capabilities perspective is the most cited strategic theory in the information systems field of research, little effort has been made to review and integrate the associate literature of this perspective in the field. Accordingly, this paper aims to systematically analyze the information systems literature on dynamic capabilities and provide a holistic understanding of the topical composition and trend of dynamic capabilities studies in information systems research.Design/methodology/approachUsing latent Dirichlet allocation as the text analysis algorithm, the author conducted a topic modeling of the dynamic capabilities corpus in the information systems field of research to quantitatively review, summarize and classify the prior literature. The review covered 191 articles published on dynamic capabilities between 1998 and 2018 in pioneering information systems journals and conference proceedings.FindingsIn accordance with the topic modeling results, the topical composition of the dynamic capabilities corpus in information systems research dominantly includes seven themes titled T1. Information systems value, T2. Information systems change, T3. Digitalization, T4. Information systems agility, T5. Big data, T6. Information systems innovation and T7. Information systems alignment. Also, the overall and topical trend of dynamic capabilities studies in the information systems field of research were revealed. The trends indicated that the investigated domain and its prominent sub-domains have generally had positive productivity over the past years.Originality/valueThe current study contributes to the domain by developing knowledge and improving literature on dynamic capabilities in information systems research, discovering the main topics of interest for information systems researchers to deploying the dynamic capabilities perspective in their studies, and prioritizing the future information systems research on dynamic capabilities based on the identified trends of topics.

Polynomial Regression and Measurement Error

Many of the phenomena of interest in information systems (IS) research are nonlinear, and it has consequently been recognized that by applying linear statistical models (e.g., linear regression), we may ignore important aspects of these phenomena. To address this issue, IS researchers are increasingly applying nonlinear models to their datasets. One popular analytical technique for the modeling and analysis of nonlinear relationships is polynomial regression, which in its simplest form fits a "U-shaped" curve to the data. However, the use of polynomial regression can be problematic when the independent variables are contaminated with measurement error, and the implications of error can be more severe than in linear models. In this research, we discuss a number of techniques that can be used for modeling polynomial relationships while simultaneously taking measurement error into account and examine their performance by using a simulation study. In addition, we discuss the use of marginal and response surface plots as interpretational aides when evaluating the results of polynomial models and showcase their use through a practical example using a well-known dataset. Our results clearly indicate that the use of a linear regression analysis for this kind of model is problematic, and we provide a set of recommendations for future IS research practice.

Persuasion: An Analysis and Common Frame of Reference for IS Research

Information Systems (IS) researchers persistently examine how Information and Communications Technology (ICT) changes attitudes and behaviours but rarely leverage the persuasion literature when doing so. The hesitance of IS researchers to leverage persuasion literature may be due to this literature’s well-documented complexity. This study aims to reduce the difficulty of understanding and applying persuasion theory within IS research. The study achieves this aim by developing a common frame of reference to help IS researchers to conceptualise persuasion and to conceptually differentiate persuasion from related concepts. In doing this, the study also comprehensively summarises existing research and theory and provides a set of suggestions to guide future IS research into persuasion and behaviour change.

Bringing compassion into information systems research: A research agenda and call to action

In this article, we assert that compassion-driven approaches are the sustainable way for information and communication technologies to contribute to economic value. We urge future information systems research to emphasize, with equal vigor, the joint goals of compassion and financial gains from information and communication technologies. We present a broad agenda for future information systems research based on this premise. We also discuss how certain core assumptions underlying traditional information systems research—so far, driven primarily by economic value as outcome—would need to change in order to support this new agenda emphasizing compassion and economic value as complementary and synergistic outcomes. We provide a brief concrete illustration of this proposed agenda, and its underlying revised assumptions, by drawing on the example of a prominent field of study in information systems research, namely health information systems research.

Directions for research on gender imbalance in the IT profession

ABSTRACTThere is a significant shortage of expert Information Technology (IT) personnel in Europe and elsewhere and a marked under-representation of women in the field. This paper identifies important gaps in research on gender imbalance in the IT profession and motivates future Information Systems research to address each of them. First among these gaps is the lack of research on the far-reaching consequences of gender imbalance in the IT profession. Second, despite a considerable body of research, there is the lack of coherent explanation for this imbalance. Third, although many intervention programmes have been implemented in this area, gender diversity in practice has not improved significantly. This research field also requires theorisation based on the cumulative research efforts in the field, comparative studies in various contexts, and longitudinal studies. We point to opportunities to investigate each of these issues and recommend directions for future research and actionable research questions.

Demystifying the Influential IS Legends of Positivism

Positivism has been used to establish a standard that Information Systems (IS) research must meet to be scientific. According to such positivistic beliefs in IS, scientific research should: 1) be generalizable, 2) focus on stable independent variables, 3) have certain ontological assumptions, and 4) use statistical or quantitative methods rather than qualitative methods. We argue that logical positivist philosophers required none of these. On the contrary, logical positivist philosophers regarded philosophizing in general and ontological considerations in particular as nonsense. Moreover, the positivists’ preferred empirical research method was not a survey, but rather a qualitative observation recorded by field notes. In addition, positivist philosophers required neither statistical nor nonstatistical generalizability. At least some positivist philosophers also acknowledged the study of singular cases as being scientific. Many research orientations (e.g., single-setting research, examination of change, qualitative research) that are deemed “unscientific” by positivism in IS seem to be “scientific” (in principle) according to logical positivism. In turn, generally speaking, what has been justified as scientific by positivism in IS (e.g., requirements of statistical or nonstatistical generalizability, surveys, independent variables, ontological views) were either not required by logical positivists or were regarded as nonsensical by logical positivists. Furthermore, given that positivism is sometimes associated (or confused) with logical empiricism in IS, we also briefly discuss logical empiricism. Finally, realizing that certain influential, taken-for-granted assumptions that underlie IS research are unwarranted could have ground-breaking implications for future IS research.

Once upon a time: Crafting allegories to analyze and share the cultural complexity of strategic alignment

Allegories are fictional tales that convey meaning not explicitly set out in their narratives. In writing them, researchers move beyond the “realistic” tale to frame coherent organizational metaphors and symbols and to offer a multi-layered truth that lies “between the lines” in the often subconscious spaces of organizational life. As such, this genre offers an evocative, yet concise alternative to traditional approaches for information systems research. Drawing on a 2-year cultural study of strategic alignment of IT in two insurance organizations as illustrative context, the allegory is introduced and demonstrated in five steps to guide researchers in creating their own allegories. This illustration uses allegory to recast diverse cultural and historical data into short stories that involve magic dragons and wizards, thereby demonstrating the usefulness of the genre for comparative, multi-case designs to translate organizational features to achieve a common representation. In conclusion, the paper offers reflections on how the genre of allegory may contribute to future information systems research, to alternative styles of presentation, and to reflexive practices.