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  Subjects -> ELECTRONICS (Total: 207 journals)
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Electronic Markets
Journal Prestige (SJR): 0.834
Citation Impact (citeScore): 3
Number of Followers: 6  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1019-6781 - ISSN (Online) 1422-8890
Published by Springer-Verlag Homepage  [2467 journals]
  • Explainable and responsible artificial intelligence

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      PubDate: 2022-11-29
       
  • Standardization for platforms ecosystems

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      PubDate: 2022-11-28
       
  • Trust in artificial intelligence: From a Foundational Trust Framework to
           emerging research opportunities

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      Abstract: Abstract With the rise of artificial intelligence (AI), the issue of trust in AI emerges as a paramount societal concern. Despite increased attention of researchers, the topic remains fragmented without a common conceptual and theoretical foundation. To facilitate systematic research on this topic, we develop a Foundational Trust Framework to provide a conceptual, theoretical, and methodological foundation for trust research in general. The framework positions trust in general and trust in AI specifically as a problem of interaction among systems and applies systems thinking and general systems theory to trust and trust in AI. The Foundational Trust Framework is then used to gain a deeper understanding of the nature of trust in AI. From doing so, a research agenda emerges that proposes significant questions to facilitate further advances in empirical, theoretical, and design research on trust in AI.
      PubDate: 2022-11-28
       
  • Understanding the process of meanings, materials, and competencies in
           adoption of mobile banking

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      Abstract: Abstract COVID-19 has changed the way people live, bank, shop, and work by moving them toward digitalization. It has also driven the trend toward a cashless society, and this change has taken place in an increasingly uncertain and fearful environment. This study explores the social practice of mobile banking (MB) adoption during the global COVID-19 pandemic. Data were collected from banking customers and managers using online customer reviews, semi-structured interviews, and focus groups to develop an in-depth understanding of the subjective realities of their use of MB. This approach also ensured that social distancing practices were maintained during interviews conducted during the COVID-19 outbreak. Analysis of the data suggests that social media, social circles, family members, and teams of customer service agents play an important role in developing the social practice of MB. This study culminates in the presentation of the social practice of MB adoption (SPOTA) framework. This framework is based on extended social practice theory in the context of MB adoption. The study discusses the practical implications of the findings for systems developers. The many expectations of people with or without disabilities of MB are discussed and the findings could be used to improve the accessibility and habitual practice of MB adoption.
      PubDate: 2022-11-28
       
  • Smart cities and smart governance models for future cities

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      PubDate: 2022-11-28
       
  • Decision support for efficient XAI services - A morphological analysis,
           business model archetypes, and a decision tree

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      Abstract: Abstract The black-box nature of Artificial Intelligence (AI) models and their associated explainability limitations create a major adoption barrier. Explainable Artificial Intelligence (XAI) aims to make AI models more transparent to address this challenge. Researchers and practitioners apply XAI services to explore relationships in data, improve AI methods, justify AI decisions, and control AI technologies with the goals to improve knowledge about AI and address user needs. The market volume of XAI services has grown significantly. As a result, trustworthiness, reliability, transferability, fairness, and accessibility are required capabilities of XAI for a range of relevant stakeholders, including managers, regulators, users of XAI models, developers, and consumers. We contribute to theory and practice by deducing XAI archetypes and developing a user-centric decision support framework to identify the XAI services most suitable for the requirements of relevant stakeholders. Our decision tree is founded on a literature-based morphological box and a classification of real-world XAI services. Finally, we discussed archetypical business models of XAI services and exemplary use cases.
      PubDate: 2022-11-23
       
  • Is trust in artificial intelligence systems related to user
           personality' Review of empirical evidence and future research
           directions

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      Abstract: Abstract Artificial intelligence (AI) refers to technologies which support the execution of tasks normally requiring human intelligence (e.g., visual perception, speech recognition, or decision-making). Examples for AI systems are chatbots, robots, or autonomous vehicles, all of which have become an important phenomenon in the economy and society. Determining which AI system to trust and which not to trust is critical, because such systems carry out tasks autonomously and influence human-decision making. This growing importance of trust in AI systems has paralleled another trend: the increasing understanding that user personality is related to trust, thereby affecting the acceptance and adoption of AI systems. We developed a framework of user personality and trust in AI systems which distinguishes universal personality traits (e.g., Big Five), specific personality traits (e.g., propensity to trust), general behavioral tendencies (e.g., trust in a specific AI system), and specific behaviors (e.g., adherence to the recommendation of an AI system in a decision-making context). Based on this framework, we reviewed the scientific literature. We analyzed N = 58 empirical studies published in various scientific disciplines and developed a “big picture” view, revealing significant relationships between personality traits and trust in AI systems. However, our review also shows several unexplored research areas. In particular, it was found that prescriptive knowledge about how to design trustworthy AI systems as a function of user personality lags far behind descriptive knowledge about the use and trust effects of AI systems. Based on these findings, we discuss possible directions for future research, including adaptive systems as focus of future design science research.
      PubDate: 2022-11-23
       
  • Understanding the adoption of the mask-supply information platforms during
           the COVID-19

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      Abstract: Abstract Since late 2019, coronavirus disease 2019 (COVID-19) has led to a significant increase in the demand for medical resources. To publish data on face mask supplies, the Taiwanese government collaborated with program developers to construct a mask-supply information transitional platform (MITP). To comprehend the adoption of MITP, the study proposes a research model that integrates the health behavior model (HBM) and IS/IT continuance model for examining the factors affecting intention to use an MITP. Survey data collected from 524 respondents indicated that (1) intention to use an MITP is directly influenced by perceived threat of COVID-19 and beliefs toward using the MITP; (2) cues to action directly influence the perceived threat of COVID-19; and (3) perceived ease of use of MITP is a significant determinant of perceived usefulness of MITP. These results provide practical guidelines for health authorities and government to develop health information systems and strategies to control pandemics.
      PubDate: 2022-11-12
       
  • Artificial intelligence and machine learning

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      Abstract: Abstract Within the last decade, the application of “artificial intelligence” and “machine learning” has become popular across multiple disciplines, especially in information systems. The two terms are still used inconsistently in academia and industry—sometimes as synonyms, sometimes with different meanings. With this work, we try to clarify the relationship between these concepts. We review the relevant literature and develop a conceptual framework to specify the role of machine learning in building (artificial) intelligent agents. Additionally, we propose a consistent typology for AI-based information systems. We contribute to a deeper understanding of the nature of both concepts and to more terminological clarity and guidance—as a starting point for interdisciplinary discussions and future research.
      PubDate: 2022-11-09
       
  • Explainable product backorder prediction exploiting CNN: Introducing
           explainable models in businesses

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      Abstract: Abstract Due to expected positive impacts on business, the application of artificial intelligence has been widely increased. The decision-making procedures of those models are often complex and not easily understandable to the company’s stakeholders, i.e. the people having to follow up on recommendations or try to understand automated decisions of a system. This opaqueness and black-box nature might hinder adoption, as users struggle to make sense and trust the predictions of AI models. Recent research on eXplainable Artificial Intelligence (XAI) focused mainly on explaining the models to AI experts with the purpose of debugging and improving the performance of the models. In this article, we explore how such systems could be made explainable to the stakeholders. For doing so, we propose a new convolutional neural network (CNN)-based explainable predictive model for product backorder prediction in inventory management. Backorders are orders that customers place for products that are currently not in stock. The company now takes the risk to produce or acquire the backordered products while in the meantime, customers can cancel their orders if that takes too long, leaving the company with unsold items in their inventory. Hence, for their strategic inventory management, companies need to make decisions based on assumptions. Our argument is that these tasks can be improved by offering explanations for AI recommendations. Hence, our research investigates how such explanations could be provided, employing Shapley additive explanations to explain the overall models’ priority in decision-making. Besides that, we introduce locally interpretable surrogate models that can explain any individual prediction of a model. The experimental results demonstrate effectiveness in predicting backorders in terms of standard evaluation metrics and outperform known related works with AUC 0.9489. Our approach demonstrates how current limitations of predictive technologies can be addressed in the business domain.
      PubDate: 2022-11-09
       
  • User trust in artificial intelligence: A comprehensive conceptual
           framework

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      Abstract: Abstract This paper provides a systematic literature review of current studies between January 2015 and January 2022 on user trust in artificial intelligence (AI) that has been conducted from different perspectives. Such a review and analysis leads to the identification of the various components, influencing factors, and outcomes of users’ trust in AI. Based on the findings, a comprehensive conceptual framework is proposed for a better understanding of users’ trust in AI. This framework can further be tested and validated in various contexts for enhancing our knowledge of users’ trust in AI. This study also provides potential future research avenues. From a practical perspective, it helps AI-supported service providers comprehend the concept of user trust from different perspectives. The findings highlight the importance of building trust based on different facets to facilitate positive cognitive, affective, and behavioral changes among the users.
      PubDate: 2022-11-04
       
  • The effect of transparency and trust on intelligent system acceptance:
           Evidence from a user-based study

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      Abstract: Abstract Contemporary decision support systems are increasingly relying on artificial intelligence technology such as machine learning algorithms to form intelligent systems. These systems have human-like decision capacity for selected applications based on a decision rationale which cannot be looked-up conveniently and constitutes a black box. As a consequence, acceptance by end-users remains somewhat hesitant. While lacking transparency has been said to hinder trust and enforce aversion towards these systems, studies that connect user trust to transparency and subsequently acceptance are scarce. In response, our research is concerned with the development of a theoretical model that explains end-user acceptance of intelligent systems. We utilize the unified theory of acceptance and use in information technology as well as explanation theory and related theories on initial trust and user trust in information systems. The proposed model is tested in an industrial maintenance workplace scenario using maintenance experts as participants to represent the user group. Results show that acceptance is performance-driven at first sight. However, transparency plays an important indirect role in regulating trust and the perception of performance.
      PubDate: 2022-10-24
       
  • Calming the customers by AI: Investigating the role of chatbot acting-cute
           strategies in soothing negative customer emotions

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      Abstract: Abstract Although intelligent chatbot has been widely used in online customer service settings in modern E-business, scholars still have little understanding of the chatbot strategies implemented in product or service failure context. Aiming at this gap, this study explored whether, how, and when two chatbot acting-cute strategies (i.e. whimsical chatbot strategy and kindchenschema chatbot strategy) could soothe negative customer emotions when product or service failure happened. By using two experimental studies, the results demonstrated that both the whimsical chatbot strategy and the kindchenschema (baby schema) chatbot strategy could placate negative customer emotions via two mechanisms. In the high product or service failure severity context, the soothing effects of both strategies would weaken, while the kindchenschema chatbot strategy weakens less. The whimsical chatbot strategy is suitable for customers with high technology anxiety while the kindchenschema chatbot strategy is suitable for those who have low technology anxiety. The whimsical chatbot strategy was more effective with male customers than with female customers, while the kindchenschema chatbot strategy had the opposite effect. Finally, the theoretical and managerial implications were discussed.
      PubDate: 2022-10-24
       
  • The role of design patterns in the development and legal assessment of
           lawful technologies

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      Abstract: Abstract Novel technologies such as smart personal assistants integrate digital services into everyday life. These services use personal data to offer personalized services. While they are subject to special data protection regulations at the time of development, there are few guidelines describing the transition from legal requirements to implementation. To reduce risks, services depend on external legal assessments. With developers and legal experts often missing either legal or technical knowledge, the challenge lies in bridging this gap. We observe that design patterns support both developers and legal experts, and we present an approach in which design patterns are leveraged to provide twofold value for both developers and legal experts when dealing with novel technologies. We conducted a revelatory case study for smart personal assistants and scaffolded the case interpretation through cognitive fit theory. On the basis of the findings, we develop a theoretical model to explain and predict the twofold value of design patterns to develop and assess lawful technologies.
      PubDate: 2022-10-20
       
  • Compatibility promotion between platforms: The role of open technology
           standards and giant platforms

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      Abstract: Abstract Most platform literature focuses on single platforms and their governance, e.g. concerning app developers. Yet, platform competition and dynamics are increasingly important as they form connections with each other and build complex networks. More focus on platform-to-platform relationships and the role of standards is warranted. Therefore, the goal of this study is to investigate how platform sponsors select platforms to promote as compatible with their own products, taking open standards and “giant platforms” into account. To address these questions, we construct a unique data set covering 157 platforms in the smart home market. We conduct a network analysis based on an exponential random graph model (ERGM) to incorporate platform features, dyadic characteristics, and structural processes. We find that platform-to-platform compatibility promotion is determined by a careful selection of platforms with dissimilar industry sectors and ecosystem niches. We identify two strategic approaches to select and promote platforms as compatible, based on standard complementarity and the size of the installed base. We find that platforms more often promote other platforms with similar supported standards. The majority of endorsements are directed at giant platforms, allowing platforms to support a smaller number of standards and thus a reduced degree of openness at the technology level. Platforms often integrate several giant platforms at the same time. Our study makes two major contributions to the literature. First, we extend the concept of selective promotion (Rietveld et al. 2019) to include inter-platform compatibility and open technology standards. Second, we demonstrate how platform sponsors compensate for higher accessibility at the technology level with transparency at the marketplace level.
      PubDate: 2022-10-04
       
  • Anthropomorphism in AI-enabled technology: A literature review

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      Abstract: Abstract Research advances in artificial intelligence (AI) capabilities have resulted in intelligent and humanlike AI-enabled technology (AIET). The concept of anthropomorphism—the attribution of human characteristics to nonhuman beings or entities—has received increasing attention from academia and industries. However, research on anthropomorphism in the AIET context is relatively new and fragmented, with limited efforts to evaluate current research or consolidate existing knowledge. To bridge this gap, this descriptive literature review of 55 studies seeks to identify research trends, AIET types, theoretical foundations, and methods. The study also analyzes how anthropomorphism has been conceptualized and operationalized in the AIET context, and the thematic analysis identifies research gaps and suggests future explorations. The proposed conceptual framework for exploring the interplay of anthropomorphism with its antecedents and consequences provides a nomological network for future research.
      PubDate: 2022-09-29
       
  • Exploring engagement, well-being, and welfare on engagement platforms:
           Insight into the personal service sector from the DACH region

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      Abstract: Abstract Engagement platforms (EPs) are an essential technology to enable co-creation and service innovation. Therefore, the design and governance of these platforms are receiving increasing attention in research. In this study, we aim to identify which activities and mechanisms foster engagement and which governance mechanisms are implemented to avoid harm on EPs. To this end, we conducted expert interviews with founders, CEOs, and managers of 14 personal and household-related service platform companies from the DACH region (Germany(D), Austria(A), Switzerland(CH)), to gain insights into their activities and mechanisms for creating and maintaining successful EPs. We found eight mechanisms, e.g., moderation of content, limitations of entry and certification, employed by personal EPs (PEPs) as self-regulatory mechanisms to avoid misconduct and negative experiences of actors. The identified governance mechanisms may guide the design and governing of PEPs by providing tangible examples to foster actor engagement while considering externalities on a societal and individual level.
      PubDate: 2022-09-23
       
  • Open government data: A systematic literature review of empirical research

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      Abstract: Abstract Open government data (OGD) holds great potential for firms and the digital economy as a whole and has attracted increasing interest in research and practice in recent years. Governments and organizations worldwide are struggling in exploiting the full potential of OGD and require a comprehensive understanding of this phenomenon. Although scientific debates in OGD research are intense and heterogeneous, the field lacks theoretical integration of OGD topics and their systematic consideration in the context of the digital economy. In addition, OGD has been widely neglected by information systems (IS) research, which promises great potential for advancing our knowledge of the OGD concept and its role in the digital economy. To fill in this gap, this study conducts a systematic literature review of 169 empirical OGD studies. In doing so, we develop a theoretical review framework of Antecedents, Decisions, Outcomes (ADO) to unify and grasp the accumulating isolated evidence on OGD in context of the digital economy and provide a theory-informed research agenda to tap the potential of IS research for OGD. Our findings reveal six related key topic clusters of OGD research and substantial gaps, opening up prospective research avenues and particularly outlining how IS research can inform and advance OGD research.
      PubDate: 2022-09-20
      DOI: 10.1007/s12525-022-00582-8
       
  • What factors influence grassroots knowledge supplier performance in online
           knowledge platforms' Evidence from a paid Q&A service

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      Abstract: Abstract The popularity of online paid knowledge platforms offers opportunities for massive grassroots knowledge suppliers to participate in knowledge sharing services and get financial rewards, but little is known about the determinants influencing users’ payment decisions in the particular knowledge transaction such as paid Q&A. This study examines the factors that influence the performance of grassroots knowledge supplier in paid Q&A platforms. We develop a research model integrating reputation, experience, and authority signal to explain the knowledge payment behavior based on signaling theory. Using a panel data analysis of 12,419 records from Zhihu, the largest online Q&A platform in China, our empirical study reveals that user payment behavior is significantly influenced by reputation signal and experience signal of a knowledge supplier. Interestingly, different from previous conclusions on professional knowledge payment platforms, authority signal of grassroots knowledge supplier has no significant impact on the payment behavior of online Q&A platform users.
      PubDate: 2022-09-09
      DOI: 10.1007/s12525-022-00588-2
       
  • Correction to: Effects of perceived risks and benefits in the formation of
           the consumption privacy paradox: a study of the use of wearables in people
           practicing physical activities

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      PubDate: 2022-09-01
       
 
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