The emergence of smart service ecosystems—The role of socio-technical antecedents and affordances

2023 | Information Systems Journal | Citations: 0

Authors: Herterich, Matthias M.; Dremel, Christian; Wulf, Jochen; vom Brocke, Jan

Abstract: As physical products are increasingly augmented with digital technology, manufac ... Expand

Abstract: As physical products are increasingly augmented with digital technology, manufacturing firms have become part of the development of so-called smart products and smart services. As such, manufacturing firms are challenged by new market participants and ecosystem partners, particularly from the software development industry, and by the dynamic nature of business relationships. While the academic literature on the distinctive characteristics of ecosystems, particularly digital ecosystems, is rich, the effect of smart service ecosystems' emergence on the foundation of smart products remains uncertain. This study reports on case study research based on 47 semi-structured interviews with four companies that participate in an industrial smart service ecosystem. Taking an affordance-theoretic perspective, we uncover the antecedents of and the process of emergent smart service ecosystems. We find that smart service ecosystems have three socio-technical antecedents: a shared worldview, structural flexibility and integrity, and architecture of participation. We explain the emergence of smart service ecosystems as the result of specialisation in shared affordances and integration of idiosyncratic affordances into collective affordances. We derive seven propositions regarding the emergence of smart services, outline opportunities for further research, and present practical guidelines for manufacturing firms. Collapse

Topics: affordance smart device value creation user experience innovation management

Methods: qualitative interview qualitative coding case study focus group personal interview

Theories: theory of affordance stakeholder theory service-dominant logic

Executive AI Literacy: A Text-Mining Approach to Understand Existing and Demanded AI Skills of Leaders in Unicorn Firms

2023 | International Conference on Business Informatics | Citations: 0

Authors: Pinski, Marc; Hofmann, Thomas; Benlian, Alexander

Abstract: Despite the growing relevance of artificial intelligence (AI) for businesses, t ... Expand

Abstract: Despite the growing relevance of artificial intelligence (AI) for businesses, there is a lack of research on how top-level executives must be skilled in AI. Drawing on upper echelons theory, this paper explores executive AI literacy, defined as the combined AI skills of top-level executives, and its relevance for different executive roles. We conducted a text-mining analysis of 1,625 executives' online profiles and 1,033 executive job postings from unicorn firms retrieved via web-scraping from an online professional social network. We find that AI skills are mostly required in product-related executive roles (vs. administrative roles). Thus, we provide an AI-specific perspective complementing prior information systems research on executives, which asserts that (non-AI) IT is driven by administrative executive roles. Our paper contributes to AI literacy literature by shedding light on the substance of executive AI literacy within firms. Lastly, we provide implications for AI-related information systems strategy. Collapse

Topics: artificial intelligence marketing management social network IT skill IT job

Methods: natural language processing latent semantic analysis factor analysis term frequency–inverse document frequency topic model

Theories: upper echelons theory

Living in a Fishbowl or Not: The Role of Transparency and Privacy in Creative Dialogues on Enterprise Social Media

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Van Osch, Wietske; Bulgurcu, Burcu; Liang, Yuyang

Abstract: Transparency-the observability of activities, behaviors, and performance-is oft ... Expand

Abstract: Transparency-the observability of activities, behaviors, and performance-is often treated as a panacea for modern management. Yet there is a conundrum in the literature, with some studies suggesting that transparency may benefit group creativity and others suggesting that privacy may do so. A similar conundrum exists regarding the effects of different social capital types-structural holes vs. network cohesion-on group creativity. Enterprise social media (ESM) provide a unique opportunity to solve these conundrums by allowing groups to be "transparent" (non-group members can observe and/or participate in group activities) or "private" (group members and activities are hidden from the community) and enabling groups to develop distinct social capital structures. Using data from 28,083 written interactions produced by 109 transparent and 106 private groups in an ESM of a multinational design firm, we found strong support for our contingency hypotheses that both transparent and private groups may produce high levels of creative dialogues, yet in different forms. Specifically, expansion-focused creative dialoguesthose focused on combining or expanding existing concepts-emerge in transparent groups, but only when the group's social capital is characterized by structural holes. Conversely, we found that reframingfocused dialogues-those focused on challenging and rethinking-emerge in private groups but only when the group's social capital is characterized by network cohesion. Theoretically, these findings can help to solve the conundrums in the literature on group creativity and shed light on the role of ESM use in this context. Practically, our findings offer a critical reflection o contemporary initiatives for increasing transparency, whether through physical design or digital transformation.Burcu Bulgurcu is an assistant professor of information systems in the Department of Information Technology Management of the Ted Rogers School of Management. She was formerly a professor at the Carroll School of Management, Boston College. Her research focuses on the behavioral and managerial aspects of cybersecurity and information privacy, security and privacy by design, and the implications of privacy on creativity and innovation in digital enterprises. The results of her research have been published in the Financial Times Top 50 journals and leading conferences, including MIS Quarterly, the Journal of Management Information Systems, and ICIS and AMCIS proceedings. Dr. Bulgurcu's research has been supported by the Social Sciences and Humanities Research Council of Canada and she has received numerous academic honors and awards. She is currently teaching award-winning courses on strategic IT management and data visualization.Yuyang Liang is a postdoctoral scholar in the Department of Information Technology at HEC Montréal. He received his PhD in media and information studies in the Department of Media and Information at Michigan State University. His areas of interest include knowledge sharing and collaboration via online communities and social media, enterprise social media, and computational modeling of social systems. His work has appeared in top computer-supported cooperative work and information system Collapse

Topics: social capital privacy blog spreadsheet open source

Methods: computational algorithm machine learning theory development qualitative coding descriptive statistic

Theories: theory of conceptual combinations

Skipping class: improving human-driven data exploration and querying through instances

2022 | European Journal of Information Systems | Citations: 0

Authors: Saghafi, Arash; Wand, Yair; Parsons, Jeffrey

Abstract: With the growing focus on business analytics and data-driven decision-making, th ... Expand

Abstract: With the growing focus on business analytics and data-driven decision-making, there is a greater need for humans to interact effectively with data. We propose that presenting data to human users in terms of instances and attributes provides a more flexible and usable structure for querying, exploring, and analysing data. Compared to a traditional representation, an instance-based representation does not impose any predefined classification schema over the data when it is presented to users. This paper examines the potential utility of instance-based data through two laboratory experiments – the first focusing on exploration of data for pattern discovery (open-ended tasks) and the second on retrieval of information (closed-ended tasks). In both cases, participants were able to achieve better results in tasks using instance-based data than using class-based representations. Given the growing need for self-service analytics, as well as using information for purposes not anticipated when it was collected, we show that instance-based representations can be an effective way to satisfy the emerging needs of information users. Collapse

Topics: database system decision making Tableau software business intelligence information retrieval

Methods: experiment parametric test Student's t-test experimental group data modeling

Theories: schema theory

News-Induced Dynamic Networks for Market Signaling: Understanding the Impact of News on Firm Equity Value

2021 | Information Systems Research | Citations: 0

Authors: Chen, Kun; Li, Xin; Luo, Peng; Zhao, J. Leon

Abstract: Firm relations, which inform the competitive environments of firms, are critical ... Expand

Abstract: Firm relations, which inform the competitive environments of firms, are critical to firm operations and are often factored into investor decisions. Previous studies and practices have considered relatively stable and long-term business relations as an indicator of firm market value. Public news often reports on business relations, especially dynamic and short-term opportunities and challenges involving different partners. Learning about firm relations from news is commonly done by human investors but has not been studied systematically in previous research, leading to a research opportunity in market signaling via dynamic firm relations. In this study, we propose a new approach to market signaling by leveraging text-mining methods to extract cobenefit/counter-benefit networks based on firms’ mutual or conflicting interests in business events. We find that the resulting networks in the long term are partially aligned with firm cooperation and competition relationships, confirming their semantic implications for investor perception and attention. Our empirical study further shows that dynamic networks formed in a short time period (measured by firm centrality in networks) have significant impacts on firm equity value, after controlling for market activities and other information from news, such as volume, sentiment, and comentions. We show that dynamic networks can provide additional value in predicting firm equity value over stable networks. Moreover, the negative effects of counter-benefit networks emerge rapidly and persist longer than the positive effects of cobenefit networks. This study provides new insights into investor perception of news and suggests new research directions for financial text mining. Our research findings on market signaling via news-induced networks also have an impact on financial practices, such as market analysis and automatic trading. Collapse

Topics: network effect database system supply chain management blog accounting

Methods: computational algorithm machine learning experiment longitudinal research natural language processing

Theories: cognitive attention theory

How to Design IIoT-Platforms Your Partners are Eager to Join: Learnings from an Emerging Ecosystem

2021 | International Conference on Business Informatics | Citations: 0

Authors: Guggenberger, Tobias Moritz; Hunke, Fabian; Möller, Frederik; Eimer, Anne-Cathrine; Satzger, Gerhard; Otto, Boris

Abstract: Building and sustaining a successful platform business remains one of the bigges ... Expand

Abstract: Building and sustaining a successful platform business remains one of the biggest challenges in the age of digitalization and platformization, particularly in the manufacturing industry. The art of managing the partner ecosystem to create and distribute mutual benefits depends on the design of the platform – thus, on the implemented mechanisms and functionalities, typically complemented by third-party applications. Therefore, it is eminently important to attract potential partners to enter the ecosystem. With this article, we provide substantial insight into the case of an emerging platform and its respective ecosystem of stakeholders. We analyze their individual requirements, abstract them into general key requirements, and finally develop design principles. Thus, our research, on the one hand, extends the current knowledge of platform literature with new, generalized knowledge about platform design, especially in the development phase. On the other hand, we contribute to the emerging field of participant attraction previously focusing on complementors. Collapse

Topics: digital platform value creation digital innovation digital transformation industry 4.0

Methods: design principle qualitative interview qualitative content analysis design requirement company material

Augmenting the Future: An Exploratory Analysis of the Main Resources, Use Cases and Implications of Augmented Analytics

2021 | European Conference On Information Systems | Citations: 0

Authors: Oesterreich, Thuy Duong; Anton, Eduard; Xu, Feipeng

Abstract: Recently, augmented analytics has increasingly gained attention as one of the m ... Expand

Abstract: Recently, augmented analytics has increasingly gained attention as one of the more advanced, novel approaches for handling big data. Based on machine learning and natural language processing, augmented analytics benefits from recent advancements in the artificial intelligence field to automate the analytics cycle. Despite the various benefits that augmented analytics offers for business and society, research on this topic is scarce to date. Based on the IT business value model, we examine the role of technological and social resources as well as the main use cases of augmented analytics. Therefore, we combine quantitative text mining with qualitative content analysis for an exploratory study of 350 academic and practical publications as well as 49 datasets of companies offering augmented analytics software and services. The findings contribute to the body of knowledge by enhancing the understanding of the augmented analytics concept, uncovering prevalent research gaps, and highlighting future research directions. Collapse

Topics: artificial intelligence IS value programming language big data human resource management

Methods: qualitative content analysis natural language processing network analysis literature study named entity recognition

Theories: socio technical theory

Idea Generation in Enterprise Social Media: Open versus Closed Groups and Their Network Structures

2020 | Journal of Management Information Systems | Citations: 0

Authors: Osch, Wietske van; Bulgurcu, Burcu

Abstract: Social network theory has produced conflicting results regarding the link betwee ... Expand

Abstract: Social network theory has produced conflicting results regarding the link between different social network structures—bridging versus bonding—and idea generation. To address this conundrum, we conduct a naturally occurring quasi-experiment of 126 open and 108 closed groups within an Enterprise Social Media (ESM) system of a multinational enterprise. Our findings show that idea generation occurs when the type of social network structure—bridging or bonding—is matched to a group’s openness or closedness, respectively. We further show that the reverse is counterproductive: when closed groups display bridging ties and open groups display bonding ties, idea generation is significantly undermined. Theoretically, these findings clarify the conditions and mechanisms by which both bridging and bonding can result in idea generation and provide a deeper understanding of the use of ESM for idea generation. Practically, our findings provide valuable and actionable insights regarding the use of ESM for idea generation in groups. Collapse

Topics: idea generation social network innovation management virtual teamwork enterprise social media

Methods: computational algorithm theory development regression analysis method descriptive statistic qualitative coding

Forecasting demand profiles of new products

2020 | Decision Support Systems | Citations: 3

Authors: van Steenbergen, R.M.; Mes, M.R.K.

Abstract: Nowadays, many companies face shorter product life cycles, increasing the need t ... Expand

Abstract: Nowadays, many companies face shorter product life cycles, increasing the need to properly forecast demand for newly introduced products. These forecasts allow them to support operational decisions, such as procurement and inventory control. However, forecasting the demand of new products is challenging compared to existing products, since historical sales data is not available as an indicator of future sales. Moreover, little attention has been paid in literature to quantitative methods for new product forecasting, especially with respect to quanti­ fying the uncertainty in demand. In this paper, we present a novel demand forecasting method denoted by DemandForest, which combines K-means, Random Forest, and Quantile Regression Forest. This machine learning-based approach combines the historical sales data of previously introduced products and product characteristics of existing and new products to make prelaunch forecasts and support inventory management decisions for new products. DemandForest clusters and predicts demand patterns, and predicts the quantiles of the total demand during an introduction period. We validate and illustrate our approach for forecasting and inventory management using real-world data sets of several companies. Compared to several benchmark methods, DemandForest provides the most accurate predictions, resulting in potential inventory savings of around 15% depending on lead times and service levels. Collapse

Topics: supply chain management database system inventory management system business-to-consumer business-to-business

Methods: computational algorithm random forest classification experiment cluster analysis k-means clustering

It Takes a Village: Understanding the Collective Security Efficacy of Employee Groups

2019 | Journal of the Association for Information Systems | Citations: 35

Authors: Johnston, Allen C.; Di Gangi, Paul M.; Howard, Jack; Worrell, James

Abstract: An organization’s ability to successfully manage information security incidents ... Expand

Abstract: An organization’s ability to successfully manage information security incidents is determined by the actions of its employees, as well as the actions of various groups of employees within its organizational boundaries. To date, information security research has primarily focused on individual-level phenomena and has not yet explored group-level phenomena such as how employee groups recognize and respond to security incidents in a way that is consistent with the organization’s security goals and objectives. The current study addresses this gap, thereby answering the research call for group-level security research perspectives. The present study explores how employee groups formulate their collective security efficacy, which influences how group members recognize and respond to information security incidents. Using a case study of a large healthcare research organization (HRO), we analyze two security incidents, a malware attack, and a physical security breach, to identify a unique set of ecological and social properties of employee groups that are salient to their collective security efficacy. Collapse

Topics: data security IT security threat malware organizational memory security policy

Methods: qualitative interview qualitative content analysis case study synthesis internal company material

Theories: social disorganization theory social capital theory