A Service Science Perspective on the Role of ICT in Service Innovation

Information and Communication Technology (ICT) is often considered the main enabler of service innovation. The unique role of ICT in service innovation, however, is not fully understood and advancing knowledge in this area emerged as the top research priority in the fields of service science and information systems research. To date, substantial insights regarding the role of ICT in service innovation are not available, and new theoretical lenses and perspectives are needed to develop these. In this conceptual paper, we define service innovation as service system reconfiguration, which allows us to classify the role of ICT in this process more succinctly and ultimately overcome the shortcomings in the existing body of literature. Specifically, we deconstruct and extend previous views of ICT as a “black box” in service innovation research, and focus on the actual innovation process and its mechanisms. We define and delineate these as resource shifting and resource access, explain the role of ICTs in each, and outline further research opportunities that result from these new insights

Ethical Issues in Big Data Analytics: A Stakeholder Perspective

Big data analytics is a fast-evolving phenomenon shaped by interactions among individuals, organizations, and society. However, its ethical implications for these stakeholders remain empirically underexplored and not well understood. We present empirical findings from a Delphi study that identified, defined, and examined the key concepts that underlie ethical issues in big data analytics. We then analyze those concepts using stakeholder theory and discourse ethics and suggest ways to balance interactions between individuals, organizations, and society in order to promote the ethical use of big data analytics. Our findings inform practitioners and policymakers concerned with ethically using big data analytics and provide a basis for future research

Self-Organizing Service Ecosystems: Exploring a New Concept for Service Science

The rapid advancements on digital technologies have positioned digital transformation as a central topic of interest to information systems (IS) researchers. However, our understanding of the nature, extent and dynamics of digital service ecosystems remains limited. This short paper contributes to IS and service science research by introducing the conceptualization of self-organizing service ecosystem as an analytical lens for understanding digital transformative phenomena in service ecosystems. To achieve this, we draw on the most recent narrative of value co-creation from service-dominant logic and on key definitions from the theory of self-organization. This paper also discusses future research directions emphasizing on the role and impact of technology in self-organizing service ecosystems

What We Don’t Know (Yet) about Human-AI Collaboration

New questions about how humans can – and should – collaborate with Artificial Intelligence (AI) are emerging rapidly with the emergence of generative AI solutions like “ChatGPT”. The AI solutions available today go far beyond previously considered IT because it can re-code procedures, transform data, generate content, and thus alter the process and outcomes of work at an unprecedented scale. The consequence of this development is the question of whether AI is outperforming and replacing humans at non-routine tasks such as knowledge work (KW). This is a non-trivial question because knowledge worker (KWers) and the knowledge-intensive organizations embedded in were, for a long time, seen to be seemingly unaffected by the technological developments stemming from AI. Today, however, there is very limited understanding of the ways that KWers adjust to, and integrate, AI at work. This includes questions addressing ethical concerns related whether technology inhibits or facilitates KWers. With these theoretical challenges in mind, this research in progress sets out to sets out to address the existing research gaps existing in human-AI collaboration within knowledge-intensive domain: 1) there is out-of-dated understanding of relationship between the use of technology and the evolution of KW; 2) how are KWers highly attached with technology influenced by AI; and 3) the expectation about how human-AI collaboration should shape the nature of KW still remain unclear. Thus, this research aims to revisit the concept of KW in light of ongoing AI technology progress, outline the AI-driven phenomenon in knowledge-intensive domain and generate in-depth insights on how human–AI collaboration is reshaping the nature of KW

Theorizing about resource integration through service-dominant logic

Resource integration, as it relates to value creation, has recently been a key aspect of the discussions about service-dominant (S-D) logic. However, the majority of research pays relatively little explicit attention to the process of theorizing and the epistomological and ontological assumptions upon which the theorizing process is based. This article addresses these issues. The processes that relate to theorizing and developing strong theory are discussed. We then examine how to conceptualize ‘resources’ and ‘resource integration’ following differing ontological and epistemological assumptions that guide the theorizing process. Research recommendations to help navigate through the finer details underlying the theorizing process and to advance a general theory of resource integration are developed

Connectivity: a socio-technical construct to examine ICT-enabled service

Advances in ICT have changed, and continue to change, interactions between service providers and customers. Service industries like health care or consulting traditionally relied on interpersonal “high touch, low tech” (Bitner, Brown, and Meuter 2000: 138) exchanges. Today, however, service providers and customers increasingly interact through virtual, rather than physical interfaces (Breidbach, Kolb, and Srinivasan 2013a). But, service research to date has focused predominantly on face-to-face settings (e.g., Froehle and Roth 2004), while technology-enabled value co-creation processes remain largely unexplored and misunderstood (Breidbach and Maglio 2015). Consequently, the understanding of ICT-enabled service is incomplete, and exploring the broader role and implications of ICT in service represents a key research priority for service science (e.g., Srinivasan, Breidbach, and Kolb 2015) and IS scholars alike (Maglio and Breidbach 2014)

Accountable algorithms? The ethical implications of data-driven business models

Purpose The purpose of this study is to identify, analyze and explain the ethical implications that can result from the datafication of service. Design/methodology/approach This study uses a midrange theorizing approach to integrate currently disconnected perspectives on technology-enabled service, data-driven business models, data ethics and business ethics to introduce a novel analytical framework centered on data-driven business models as the general metatheoretical unit of analysis. The authors then contextualize the framework using data-intensive insurance services. Findings The resulting midrange theory offers new insights into how using machine learning, AI and big data sets can lead to unethical implications. Centered around 13 ethical challenges, this work outlines how data-driven business models redefine the value network, alter the roles of individual actors as cocreators of value, lead to the emergence of new data-driven value propositions, as well as novel revenue and cost models. Practical implications Future research based on the framework can help guide practitioners to implement and use advanced analytics more effectively and ethically. Originality/value At a time when future technological developments related to AI, machine learning or other forms of advanced data analytics are unpredictable, this study instigates a critical and timely discourse within the service research community about the ethical implications that can arise from the datafication of service by introducing much-needed theory and terminology