Value Creation Tradeoff in Business Ecosystems: Leveraging Complementarities While Managing Interdependencies

2023 | Organization Science | Citations: 0

Authors: Agarwal, Shiva; Kapoor, Rahul

Abstract: Complementary assets play an important role in shaping an innovation’s commercia ... Expand

Abstract: Complementary assets play an important role in shaping an innovation’s commercialization success. In this paper, we broaden the locus of complementarities to examine the role of complementary technologies residing in the business ecosystems that are becoming an important source of value creation for innovating firms. We argue that, on one hand, complementary technologies help innovations create more value for their users. On the other hand, they can also limit the focal innovation’s value creation by exposing them to performance bottlenecks as the underlying technological architecture of the ecosystem evolves. We further extend the notion of specialization of complementary assets to ecosystems by considering complementary technologies that are specialized to a focal ecosystem and those that are available across multiple ecosystems. We highlight that, although the complementary technologies that are specialized to an ecosystem facilitate greater value creation, they are more likely to subject the focal innovation to performance bottlenecks. Evidence from 244,034 apps launched by software developers for Apple’s iPhone ecosystem during 2008–2015 offers strong support for our framework. In summary, the study sheds light on the value creation tradeoff for firms innovating in business ecosystems—the opportunities associated with leveraging complementarities and the challenges associated with managing technological interdependencies. Funding: S. Agarwal was funded by the McCombs School of Business at The University of Texas at Austin [Research Excellence Grant]. Collapse

Topics: mobile application innovation management value creation Apple iPhone iOS

Methods: statistical hypothesis test instrumental variables estimation cross sectional research proportional hazards model robustness check

Secondary Mental Models: Introducing Conversational Agents in Financial Advisory Service Encounters

2023 | International Conference on Information Systems | Citations: 0

Authors: Eckhardt, Sven; Bucher, Andreas; Kalunder, Madlaina; Dolata, Mateusz; Agotai, Doris; Schwabe, Gerhard

Abstract: When introducing unfamiliar Artificial Intelligence (AI)-based systems, such as ... Expand

Abstract: When introducing unfamiliar Artificial Intelligence (AI)-based systems, such as conversational agents (CAs), one needs to ensure that users interact with them according to their design. While past research has studied single-user environments, many practical settings involve multiple parties. This study addresses this gap and focuses on financial advisory service encounters and how mental models evolve in multi-party contexts. A multimodal interactive CA is developed and tested in financial consultations with 24 clients. The observations of these consultations and subsequent interviews provide insights into the challenges of using CAs in unfamiliar contexts. The clients have difficulties effectively using the system. This is linked to the institutional setting of financial advisory service encounters and a mismatch between the designer's conceptual model and the client's mental model, which we call secondary mental model. Collapse

Topics: artificial intelligence service quality Apple Siri information system use website

Methods: qualitative interview conceptual modelling

Working with AI: How Attitudes Shape Human-AI Collaboration

2023 | International Conference on Information Systems | Citations: 0

Authors: Glienke, Maximilian; Hartwich, Nicole; Antons, David

Abstract: Recent research shows that people tend to avoid relying on artificial intellige ... Expand

Abstract: Recent research shows that people tend to avoid relying on artificial intelligence (AI) when making decisions at work. Although previous studies have investigated the causes and effects of this phenomenon, the role of attitudes, which are considered as a crucial factor in biased decision-making, remains unexplored. To investigate whether and how attitudes toward AI influence decision-making in work-related situations, we developed and conducted a lab experiment in which 156 participants received AI advice on process improvement while performing a manufacturing task. Our results suggest that negative attitudes toward AI are associated with greater reluctance to accept AI advice, resulting in lower performance in work-related situations. Furthermore, we show that human involvement in the process of providing AI advice cannot mitigate the negative effects of strong negative attitudes. Collapse

Topics: artificial intelligence decision making business process improvement human AI collaboration task productivity

Methods: experiment experimental group computational algorithm laboratory experiment regression analysis method

Theories: attitude theory

The role of user perceptions of intelligence, anthropomorphism, and self-extension on continuance of use of personal intelligent agents

2023 | European Journal of Information Systems | Citations: 0

Authors: Moussawi, Sara; Koufaris, Marios; Benbunan-Fich, Raquel

Abstract: Personal Intelligent Agents (PIAs), such as Apple’s Siri and Amazon’s Alexa, are ... Expand

Abstract: Personal Intelligent Agents (PIAs), such as Apple’s Siri and Amazon’s Alexa, are different from traditional information systems. They possess unique design features and are increasingly available through various technological devices. Due to PIAs’ relative novelty, little is known about the determinants of their continued use. An investigation into PIAs’ characteristics and their impact on users’ post-adoption evaluations is expected to have theoretical and practical implications for PIA design and sustained usage. Our research model integrates perceptions of intelligence, anthropomorphism, and self-extension into the unified model of information technology continuance. Our findings show the key role of perceived intelligence of the PIA on continuance intention and indicate that hedonic perceptions of the agent become less important during post-adoption. Our results also highlight the role of perceived ownership and personalisation as antecedents of perceived self-extension. Collapse

Topics: perceived usefulness usage intention conversational agent user satisfaction Google+

Methods: cross sectional research survey longitudinal research cluster analysis structured literature research

Theories: extended unified theory of acceptance and use of technology

Effects of gender congruity in human-robot service interactions: The moderating role of masculinity

2023 | International Journal of Information Management | Citations: 0

Authors: Pitardi, Valentina; Bartikowski, Boris; Osburg, Victoria-Sophie; Yoganathan, Vignesh

Abstract: Despite humanoid service robots having attracted considerable research attention ... Expand

Abstract: Despite humanoid service robots having attracted considerable research attention, it remains unclear how consumers respond to some specific human characteristics of robots. Drawing from theories on social catego­ rization and identification, we study the role of consumer perceived control as a psychological mechanism to explain how human-robot gender congruity alters consumers’ affective reactions (feelings of comfort in the service encounter and service brand attitudes). We also consider that such gender congruity effects may be contingent on the individual cultural value of masculinity. We demonstrate experimentally that human-robot gender congruity (vs. incongruity) elicits more positive affect, while masculinity moderates some of these ef­ fects. Moreover, perceived control mediates effects of gender congruity on affective reactions only for consumers high on masculinity. We offer three major theoretical contributions as we 1) focus on social identity theory to shed light on how human-robot gender congruity affects consumer behavior in service encounters, 2) demon­ strate the role of perceptions of control as a psychological process variable to explain these effects, and 3) provide insights into the role of the cultural value of masculinity as a factor that shapes human-robot gender congruity effects. Collapse

Topics: robotic consumer behavior social norm human computer interaction artificial intelligence

Methods: experiment survey ANOVA parametric test statistical hypothesis test

Theories: social identity theory

Is Artificial Intelligence Attractive? An Empirical Study on User’s Intention to Use AI-Enabled Applications

2023 | HICSS | Citations: 0

Authors: Zhao, Yu (Gary); El-Gayar, Omar; Tu, Zhiling

Abstract: Artificial Intelligence (AI) technologies such as machine learning (ML), natural ... Expand

Abstract: Artificial Intelligence (AI) technologies such as machine learning (ML), natural language processing (NLP), and image recognition, are being incorporated into a wide variety of applications. These AI-enabled applications (AIapps) promise to reshape people's lives. However, despite the proliferation of AI-related research, very little research has focused on how AIapps' unique characteristics affect an individual's adoption behavior. This study examines factors influencing an individual's intention to use AIapps with a proposed research model based on the Task-Technology Fit (TTF) as the underlying theoretical framework. The research model is empirically evaluated using the survey data and SEM method. Theoretically, this study focuses on how the unique characteristics of AIapps influence the task-technology fit and drive the intention of use. The findings are expected to help AIapp developers to evaluate the relative importance of AIapp features which can provide insights into the technology characteristics and identify priorities for further research and development. Collapse

Topics: usage intention self efficacy artificial intelligence knowledge representation task characteristic

Methods: machine learning survey structural equation modeling PLS tool data transformation

Theories: theory of task technology fit

Can you help me scale? A Systematic Analysis of Scaling Capabilities of Conversational Agents for Digital Services

2022 | International Conference on Business Informatics | Citations: 0

Authors: Benner, Dennis; Schöbel, Sofia; Janson, Andreas; Leimeister, Jan Marco

Abstract: Today’s servitized and digitalized society has significant implications for the ... Expand

Abstract: Today’s servitized and digitalized society has significant implications for the economic system as value creation increasingly shifts towards digital services. Organizations are increasingly relying of artificial intelligence, which enables them to operate with novel technological artifacts like conversational agents (CA). These human-like AI-driven artifacts open new possibilities and channels for service providers to scale up their business even further and to create a business value or even to make processes more efficient. While we witness the results of that each day, we do not fully understand how such CAs can contribute to scaling up a business. Therefore, we conduct an analysis of service research and popular CAs to determine the status quo and highlight opportunities for scaling services with CAs. We contribute to theory by clarifying the meaning of scalability in combination with CAs and support practitioners by providing an overview about how organizations successfully scale their CAs. Collapse

Topics: digital service business-to-business innovation management business model business process innovation

Methods: chatbot structured literature research meta analysis theoretical contribution qualitative content analysis

On the Design of and Interaction with Conversational Agents: An Organizing and Assessing Review of Human-Computer Interaction Research

2022 | Journal of the Association for Information Systems | Citations: 0

Authors: Diederich, Stephan; Brendel, Alfred; Morana, Stefan; Kolbe, Lutz

Abstract: Conversational agents (CAs), described as software with which humans interact t ... Expand

Abstract: Conversational agents (CAs), described as software with which humans interact through natural language, have increasingly attracted interest in both academia and practice because of improved capabilities driven by advances in artificial intelligence and, specifically, natural language processing. CAs are used in contexts such as people's private lives, education, and healthcare, as well as in organizations to innovate or automate tasks-for example, in marketing, sales, or customer service. In addition to these application contexts, CAs take on different forms in terms of their embodiment, the communication mode, and their (often human-like) design. Despite their popularity, many CAs are unable to fulfill expectations, and fostering a positive user experience is challenging. To better understand how CAs can be designed to fulfill their intended purpose and how humans interact with them, a number of studies focusing on human-computer interaction have been carried out in recent years, which have contributed to our understanding of this technology. However, currently, a structured overview of this research is lacking, thus impeding the systematic identification of research gaps and knowledge on which future studies can build. To address this issue, we conducted an organizing and assessing review of 262 studies, applying a sociotechnical lens to analyze CA research regarding user interaction, context, agent design, as well as CA perceptions and outcomes. This study contributes an overview of the status quo of CA research, identifies four research streams through cluster analysis, and proposes a research agenda comprising six avenues and sixteen directions to move the field forward. Collapse

Topics: usability human computer interaction IS discipline graphical user interface collaborative work

Methods: cluster analysis literature study chatbot design artifact laboratory experiment

Theories: unified theory of acceptance and use of technology uncanny valley theory

Comparing Platform Core Features with Third-Party Complements. Machine-Learning Evidence from Apple iOS.

2022 | HICSS | Citations: 0

Authors: Halckenhaeusser, André; Mann, Felix; Foerderer, Jens; Hoffmann, Philipp

Abstract: Software-based platforms have become omnipresent both in private and professiona ... Expand

Abstract: Software-based platforms have become omnipresent both in private and professional contexts. Platform owners constantly invest in platform evolution in that they update the technological core and enrich its feature base. The question arises how such platform core feature changes can be compared with third-party complements. We investigate this question in the context of an exploratory machine-learning based case study on Apple’s mobile platform iOS. By analyzing the changes to iOS over time and developing an approach using natural language processing, we are able identify functional overlaps between platform core features and complements. Our results suggest that platform core features are indeed functionally related to those of complementors and that the strategy of releasing novel platform core features changes over time. Besides, our approach enables us to assign platform core features to app categories. The analysis of functional overlaps raises relevant implications for research and practice. Collapse

Topics: mobile application iOS platform governance mobile platform digital ecosystem

Methods: natural language processing machine learning case study GloVe binary classification

Trust, but Verify! - An Empirical Investigation of Students’ Initial Trust in AI-Based Essay Scoring

2022 | Americas Conference on Information Systems | Citations: 0

Authors: Hartmann, Philipp; Hobert, Sebastian; Schumann, Matthias

Abstract: AI is becoming increasingly important in supporting education. Nowadays, AI-bas ... Expand

Abstract: AI is becoming increasingly important in supporting education. Nowadays, AI-based systems can score essays in high-stakes exams not only by comparing words but also by evaluating content. However, for AI-based essay scoring systems to be used, they must be trusted. Based on a scenario-based experiment with 260 students at a German university, we were able to show that their initial trust in AI-based essay scoring systems is significantly lower than in human examiners. Human control of AI-scoring can partially reduce the negative effect. The perceived system characteristics and the personality traits of the students are important factors which positively influence trustworthiness and trust, respectively. Furthermore, we could show that the more complex the essay scoring is perceived, the less trustworthy the AI-based system is classified. No influence could be seen regarding the relevance of the scoring for the students, their AI-experience and technology affinity. Collapse

Topics: artificial intelligence personality task complexity electronic mail decision support system

Methods: survey data transformation factor analysis structural equation modeling survey design

Theories: big five model