The sharing of consumer data within the Internet of Things (IoT) is currently generating substantial revenue for supply chain participants. Manufacturers collect consumer data through smart devices and share it with IoT platforms to improve technical service delivery. This practice not only enables manufacturers to gain deeper insights into consumers but also raises concerns about consumer privacy. This study employs a game-theoretic model to investigate how the IoT platform’s data-driven service pricing mechanisms and consumer privacy concerns influence data-sharing decisions. The findings are as follows: First, the data-driven strategy remains relatively stable under a usage fee pricing model, regardless of the decision-maker. In contrast, under a flat fee pricing model, data-sharing strategies are significantly influenced by the identity of the decision-maker. Furthermore, when consumer privacy is a major concern, decision-making outcomes consistently lead to lower results, regardless of the decision-maker’s identity. These findings offer important practical implications for organizations within the IoT supply chain, providing valuable insights to enhance decision-making processes related to platform operations and enterprise digital transformation.
Advances in Machine Learning (ML) have led organizations to increasingly implement ML decision aids to enhance employees’ decision-making performance. While such systems can improve organizational efficiency in many contexts, they may inadvertently impact the development of human decision-making skills. Drawing on cognitive theories, this study examines how the use of ML decision aids impact skill development and performance. Using a novel experimental design tailored to address organizational challenges and endogeneity concerns, the study identifies causal effects of reliance on ML predictions on skill development in decision making. Specifically, it is demonstrated that reliance on ML predictions in a prediction-making task can hinder the development of critical decision-making skills, resulting in significant performance drops when the system becomes unavailable. Furthermore, it is found that the extent of trust in the system's predictions strongly influences the severity of this skill deficit. These findings highlight the need for thoughtful integration of ML decision aids, emphasizing the importance of balancing reliance with skill retention to mitigate risks associated with temporary or permanent system disruptions.
This vision paper provides a comprehensive perspective of human trust in software robots used in Robotic Process Automation (RPA) and the associated challenges in establishing RPA systems that foster trust within hybrid workforces of software robots and human employees. Although the increasing technological sophistication of RPA systems enhances the autonomy of software robots, there is no framework for conceptualizing trust in software robots and its impact on human-technology collaboration. This lack of attention could contribute to serious issues remaining unnoticed, as for example the emerging re-manualization phenomenon, wherein tasks revert to manual execution due to a lack of trust in software robots. To address this research gap, this paper proposes a conceptual framework for human-software robot trust in RPA. In addition, the paper discusses five relevant research challenges that need to be tackled to enable a new wave of trusted software robots.
The emergence of Generative AI (GenAI) is rapidly transforming higher education by offering new opportunities for personalized learning and supporting students on their individual learning journeys. Despite GenAI’s potential to enhance student engagement and learning outcomes, many current GenAI-based learning tools remain weakly grounded in pedagogical frameworks necessary for meaningful educational impact. Addressing this gap, this paper investigates how to design GenAI-based agents to effectively support student motivation and learning processes by drawing on self-directed learning as a pedagogical kernel theory. By adopting a multi-cycle Design Science Research approach, prescriptive design knowledge is derived through qualitative interviews and literature in two design cycles. The resulting design principles are instantiated within a GenAI-based prototype for a university course and subsequently evaluated empirically by means of a user study and expert interviews. The study contributes a set of theoretically grounded and empirically informed design principles for GenAI-based learning agents that support self-directed learning while complementing rather than replacing students’ critical thinking. By articulating actionable design knowledge, this research supports the pedagogically informed integration of GenAI into higher education and contributes to the evolving discourse on GenAI-enabled learning in the context of Education 4.0.
Prior to launch, managers, entrepreneurs, and investors need to know whether a nascent or existing market can sustain a platform. Answering this question predates and informs the entry strategy. Establishing startup criteria for platform entry involves combining revenue projections for the market with break-even analysis. Two sidedness significantly complicates the process owing to the role each side plays in value creation for the other side of the platform. This work presents decision metrics for understanding whether room exists for a platform, how many customers it needs on each side to survive, and cost thresholds each side cannot exceed. Additionally, it also identifies which side facilitates entry, when crowded markets should be avoided, and provides a means for evaluating and comparing entry strategies. Taken together, the results translate into a checklist for platform entry.
The rapid advancement and increasing integration of Artificial Intelligence (AI) are reshaping the competencies required in various professional and personal contexts. While much research has focused on specialized competencies, the role and evolving significance of basic competencies remain underexplored. This study addresses this problem by investigating how the relevance of basic competencies shifting in the AI era. Using a Delphi study involving 34 experts from academia and practice, 18 basic competencies were identified and evaluated, and their future importance in the context of weak and strong AI was examined. The findings reveal that, for example, competency in critical thinking and decision-making competency are expected to grow, while others, like foreign language competency, may diminish. These results provide valuable insights for education and organizations aiming to adapt their strategies to better prepare individuals for an AI-driven world.
In intralogistics, fleets of automated guided vehicles are becoming a key technology offering competitive advantages in performance. Scheduling automated guided vehicle fleets is a complex problem involving the spatial and timely coordination of vehicles and transported cargo. So far, scheduling has been addressed with different traditional approaches as well as machine learning approaches, for example deep (reinforcement) learning models. However, despite the increasing number of approaches, there is no guidance for their selection to optimize fleet efficiency. Hence, decision-makers risk selecting a subpar approach sacrificing efficiency and potentials for competitive advantages. Drawing on the “No Free Lunch Theorem” and using design science, this research contributes with five design requirements, three design principles, and seven design features for a benchmark that ranks approaches for the scheduling of fleets of automated guided vehicles. For the purpose of demonstration and evaluation, the authors instantiated the design knowledge in a benchmark applied in a case study. In terms of implications for practice and theory, the normative knowledge on approach selection can be appropriated to related problems against the background of proliferating AI-based models.
Generative Artificial Intelligence (GenAI) systems, such as large language models (LLMs), are increasingly permeating everyday tasks; yet, they sometimes present false or misleading information as fact – a phenomenon known as hallucination. As GenAI becomes more accessible to the public, it is essential to understand how users’ AI literacy shapes their reliance on imperfect advice from AI systems. Building on the concept of correspondence bias, the study examines how individuals with different levels of AI literacy respond to faulty GenAI advice. Evidence from an online programming experiment with 542 U.S. programmers shows that individuals with higher AI literacy rely less on GenAI advice, particularly when the advice is flawed. Correspondence bias provides a plausible explanatory mechanism for these findings and helps reconcile mixed results in prior research on AI literacy. Overall, the findings offer a more nuanced perspective on the benefits and risks of AI-literacy-driven mistrust. This informs education, integration, and evaluation initiatives for GenAI while cautioning against naive evaluation strategies.
Algorithmic management (AM) is increasingly used and offers several benefits to organizations, such as increased efficiency of processes and greater accuracy. At the same time, employees associate AM systems negatively with justice, which hinders the realization of expected benefits. To understand how justice evaluations of AM are shaped, this study investigated the impact of prior human and algorithmic discrimination experience on this relationship. Three online experiments (n1 = 82; n2 = 83; n3 = 216) demonstrated that AM use is negatively associated with justice evaluations. Study 1 shows that the negative effect of AM use on justice evaluations was weakened if participants had prior human discrimination experience. Study 2 does not show a moderating effect of prior algorithmic discrimination experience on the relationship between AM use and justice evaluations. Study 3 further demonstrates that algorithmic discrimination is perceived less negatively compared to human discrimination. Qualitative responses show that individuals associated algorithmic discrimination with biased data and opacity, whereas human discrimination was associated with favoritism and increased human bias. These findings suggest that justice evaluations of AM systems are shaped by individuals’ prior experiences. Understanding these patterns can help organizations design and implement AM systems that promote justice.
Generative artificial intelligence (GenAI) has attracted worldwide attention across numerous domains, with particularly strong implications for software development. Effective integration of GenAI requires a thorough understanding of both its opportunities and pitfalls for software developers and the organizations they are embedded in. In this study, 30 expert interviews were conducted to examine the sociotechnical impact of GenAI from the perspective of software development practitioners. The findings show that GenAI can enhance individual and organizational productivity and stimulate creative problem-solving. At the same time, concerns emerge around over-reliance, skill degradation, and homogenization among developers. Moreover, with GenAI rapidly providing valuable information, communication between developers may decline, potentially undermining interpersonal relationships and team cohesion. These insights contribute to information systems theory by examining GenAI’s effects on social capital, machine-induced reflections, and developers’ professional identity, while also offering practical implications for the effective and sustainable adoption of GenAI in software development.
Blockchain technology holds significant promise for organizational transformation by enabling automation, transparency, decentralization, and immutable data flows. However, theoretical clarity regarding the factors that enable or inhibit blockchain technology adoption (BTA) in business organizations remains limited. This study aims to develop a unified and validated framework for BTA in business organizations by refining and validating key enablers and inhibitors across technological, organizational, environmental, supply chain, and user dimensions. Using a systematic literature review as a foundation, the study conducts a Delphi investigation with blockchain practitioners and academics. Drawing on its findings, the study develops a unified and validated BTA framework that identifies key enablers, such as automation benefits, efficiency improvements, regulatory support, supply chain traceability, and user trust in blockchain, as well as key inhibitors, including integration issues, skill shortages, compliance challenges, fragmentation in supply chains, and complex password and private key management. By consolidating and validating these factors, the study addresses the broader problem of fragmented guidance for stakeholders and the absence of an integrated theoretical foundation in information systems research.
The article explores the tensions between the opportunities and challenges of generative artificial intelligence (GenAI) in management consulting, highlighting its potential to drive efficiency while mitigating risks such as hallucinations and loss of skill retention. Through a Task-GenAI Fit (TGAIF) framework, deduced from qualitative interviews with leading German consulting firms, the article outlines how aligning tasks with GenAI capabilities can optimize task performance in consulting workflows. The recommendations support the efficient and responsible use of GenAI in complex consulting environments, balancing organizational and individual perspectives. This study contributes to information systems research by advancing efficient human-GenAI collaboration and task-technology alignment in knowledge-intensive contexts.
Online interactions between companies and customers increasingly occur via conversational agents (CAs) (e.g., chatbots), which are commonly designed to appear humanlike. At the same time, customers are frequently guided to choose healthier or more sustainable options, which can elicit negative moral emotions (e.g., guilt). This leads to the question of how humanlike CAs influence the formation of negative emotions when users are guided. Based on the Computers-are-Social-Actors theory and Norm Activation Theory, it is theorized that humanlike CAs are viewed by users as social agents that are aware of social norms, catalyzing the formation of negative moral emotions. Two experiments suggest that a humanlike CA can induce negative moral emotions, whereas traditional point-and-click interfaces cannot. Similarly, the Macbeth effect (i.e., the act of cleansing decreases negative moral emotions) can be observed for CAs: the depiction of handwashing reduced negative moral emotions in CA users. For theory, insights are provided into the perception of humanlike CAs in the context of user guidance. For practice, the results suggest that companies should be careful when implementing CAs to guide users.
Business process improvement remains a central activity in organizations seeking to address process deviations and enhance operational performance. This paper introduces an innovative and systematic process improvement approach that leverages knowledge derived from recurrent workarounds performed by process participants. Building on prior evidence that such workarounds often originate from perceived goal and process misalignments, a structured procedure is proposed that detects, analyzes, and interprets workarounds to uncover their underlying root causes. The main artifact developed in this study is the Workaround-Driven Improvement Cycle (WDIC), an iterative improvement cycle that connects recurring workaround patterns to targeted improvement directions. To operate this cycle, a complementary improvement decision framework is introduced that links misalignment categories to appropriate improvement directions. The approach was developed using a Design Science Research (DSR) methodology and informed by goal-based analysis, the workaround motivational model, and established categorizations of misfits and process redesign patterns. The WDIC and its integrated decision framework were developed across three organizational settings, each involving numerous recurring workarounds, with a fourth case providing a full demonstration of the WDIC’s practical application. The results demonstrate that systematically investigating workarounds provides focused insights into socio-technical misalignments and supports the design of precise, context-aware process improvements.
