Understanding Business Process Evolution in Digital Ventures

2023 | International Conference on Business Informatics | Citations: 0

Authors: Wuttke, Tobias; Haskamp, Thomas; Perscheid, Michael; Uebernickel, Falk

Abstract: Business processes are at each company's core and must be adapted permanently t ... Expand

Abstract: Business processes are at each company's core and must be adapted permanently to react to changing markets, substantial growth, or legal regulations. Especially digital ventures have the potential to evolve fast, and consequently, their business processes need to change at the same speed. Two streams of literature have looked into this. Traditional business process management sees business processes, once implemented, as relatively stable. In contrast, digital entrepreneurship literature highlights the inherent flexibility of digital ventures. Based on a multiple case study of five digital ventures, we analyze how entrepreneurs deal with this tension when business processes evolve. Building on entrepreneurial bricolage, we propose two types of resource recombination that we find, namely, usage of existing private resources and re-configuring of resources already being used within the venture. These insights contribute to extending our understanding of the evolution of business processes. Collapse

Topics: business process management new product development agile software development business model digital entrepreneurship

Methods: qualitative interview multiple case study design methodology digital trace data triangulation

Theories: ambidexterity theory

Towards technology of global governance – a novel approach to health system performance assessment by MCDA

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Wątróbski, Jarosław; Rudawska, Iga; Bączkiewicz, Aleksandra

Abstract: ... Expand

Abstract: Collapse

Topics: pandemic health information system system efficiency

Methods: trade-off analysis method TOPSIS descriptive statistic survey computational algorithm

Unveiling the Secrets of Collaboration on Video-Sharing Platforms

2023 | International Conference on Information Systems | Citations: 0

Authors: Xi, Dinghao; LU, Ying; Qiao, Dandan; Xu, Wei

Abstract: In online video platforms, collaborative content creation gains popularity as v ... Expand

Abstract: In online video platforms, collaborative content creation gains popularity as vloggers exchange social capital to boost content's appeal and personal visibility. However, existing research lacks rigorous verification of collaboration efficacy in this context. This study investigates the benefits of collaboration on vlogger influence within the online video domain. Utilizing a staggered difference-in-differences approach, we found that collaboration indeed generates positive impact on vloggers' influence, as evidenced by the significant increases of their fan and like numbers among the audience. However, managing collaboration activities with suitable intensity is vital, as the marginal utility of collaborations diminishes with increased collaboration numbers. Furthermore, vloggers should avoid excessive non-core activities (e.g., uploading photo albums, audios, and articles) post-collaboration, as these peripheral activities negatively affect collaboration benefits in online video contexts. We believe that the findings of our study will provide helpful guidelines for designing more effective collaboration strategies on video sharing platforms. Collapse

Topics: sharing platform social capital social media marketing management missing data

Methods: longitudinal research experimental group difference in differences propensity score matching regression analysis method

Theories: social capital theory resource based view of the firm

The challenges of entering the metaverse: An experiment on the effect of extended reality on workload

2023 | Information Systems Frontiers | Citations: 4

Authors: Xi, Nannan; Chen, Juan; Gama, Filipe; Riar, Marc; Hamari, Juho

Abstract: Information technologies exist to enable us to either do things we have not done ... Expand

Abstract: Information technologies exist to enable us to either do things we have not done before or do familiar things more efficiently. Metaverse (i.e. extended reality: XR) enables novel forms of engrossing telepresence, but it also may make mundate tasks more effortless. Such technologies increasingly facilitate our work, education, healthcare, consumption and entertainment; however, at the same time, metaverse bring a host of challenges. Therefore, we pose the question whether XR technologies, specifically Augmented Reality (AR) and Virtual Reality (VR), either increase or decrease the difficulties of carrying out everyday tasks. In the current study we conducted a 2 (AR: with vs. without) × 2 (VR: with vs. without) between-subject experiment where participants faced a shopping-related task (including navigating, movement, hand-interaction, information processing, information searching, storing, decision making, and simple calculation) to examine a proposed series of hypotheses. The NASA Task Load Index (NASA-TLX) was used to measure subjective workload when using an XR-mediated information system including six sub-dimensions of frustration, performance, effort, physical, mental, and temporal demand. The findings indicate that AR was significantly associated with overall workload, especially mental demand and effort, while VR had no significant effect on any workload sub-dimensions. There was a significant interaction effect between AR and VR on physical demand, effort, and overall workload. The results imply that the resources and cost of operating XR-mediated realities are different and higher than physical reality. Collapse

Topics: virtual reality extended reality usability ease of use augmented reality

Methods: experiment parametric test multivariate analysis of variance ANOVA survey

Theories: information systems theory

Female-exclusive Support and Gender Gap on Digital Platforms

2023 | International Conference on Information Systems | Citations: 0

Authors: Xia, Yu; Chen, Hailiang

Abstract: This study investigates the gender gap issue in the mobile platform context by ... Expand

Abstract: This study investigates the gender gap issue in the mobile platform context by analyzing performance disparities between male and female-led app companies. First, we identify the existence of the performance gap between the apps from two gender groups, utilizing a representative sample of mobile apps on Apple App Store. By employing the occurrence of Apple Entrepreneur Camp 2019 as an exogenous event, we assess the effectiveness of the platform's female-exclusive support in mitigating the gap. The results demonstrate that following the event, apps from the supported female-led companies exhibit improved performance compared to their male-led counterparts, whereas there is no significant increase in the performance of apps from unsupported female-led companies. We further show that as the age of apps from the unsupported female group increases, the event shows an increasingly positive impact on app performance. This finding suggests the presence of a conditional spillover effect of the platform's support. This study contributes to understanding the gender gap and emphasizes the importance and limitations of platform support initiatives for promoting gender equality within the platform. Managerial implications highlight the need for implementing supporting strategies to foster diversity and inclusion in the platform ecosystem. Collapse

Topics: mobile application digital distribution mobile application market mobile platform

Methods: probit regression natural experiment propensity score matching robustness check experimental group

A Multi-criteria Group Decision Making Based on Possibility Degree Matrix and $$\mathbb {ZPDHL}$$-VIKOR Method

2023 | Group Decision and Negotiation | Citations: 0

Authors: Xian, Sidong; Qing, Ke; Tang, Ling; Pan, Huilan

Abstract: In this paper, we firstly put forward a new linguistic set called Z-probabilisti ... Expand

Abstract: In this paper, we firstly put forward a new linguistic set called Z-probabilistic double hierarchy linguistic term set (Z-PDHLTS) based on Z-number, which considers both the complexity of linguistic variables and the credibility of evaluation information. Then, a new comparison method of Z-PDHLTS based on possibility degree is put forward to compare the size of two sets. In addition, a possibility degree matrix method and $$\mathbb {ZPDHL}$$-VIKOR method for multi-criteria group decision making problem with Z-PDHLTS are proposed to rank alternatives. Finally, an example of green mine selection is used and some comparative analyses are made to explain its effectiveness. Collapse

Topics: decision making

Methods: trade-off analysis method TOPSIS cross sectional research VIKOR method business process modeling

How the Uniqueness of Initial Posts Affect User Participation in Online Cancer Communities

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Xu, Anqi; Gao, Yuanyuan

Abstract: Online cancer communities (OCCs) are crucial resources for cancer patients, pro ... Expand

Abstract: Online cancer communities (OCCs) are crucial resources for cancer patients, providing support for the complex and challenging aspects of the disease. Patients often face unique problems, and OCCs offer a place to seek support when friends and family may not understand. This study explores how unique topics discussed in OCCs impact user participation in a thread, measured by the number of replies, repliers, length of replies, and response speed. A deep-learning-based natural language processing topic model is used to calculate the uniqueness scores of posts in OCCs, based on the prevalence of each topic. The study has implications for OCC administrators, researchers, and physicians in terms of understanding and addressing unique health topics expressed in online communities. Collapse

Topics: participatory design database system online community

Methods: design methodology topic model econometric modeling natural language processing deep learning

Efficient fraud detection using deep boosting decision trees

2023 | Decision Support Systems | Citations: 0

Authors: Xu, Biao; Wang, Yao; Liao, Xiuwu; Wang, Kaidong

Abstract: Fraud detection is to identify, monitor, and prevent potentially fraudulent acti ... Expand

Abstract: Fraud detection is to identify, monitor, and prevent potentially fraudulent activities from complex data. The recent development and success in AI, especially machine learning, provides a new data-driven way to deal with fraud. From a methodological point of view, machine learning based fraud detection can be divided into two categories, i.e., conventional methods (decision tree, tree boosting methods, etc.) and deep learning, both of which have significant limitations in terms of the lack of representation learning ability for the former and interpretability for the latter. Furthermore, due to the rarity of detected fraud cases, the associated data is usually imbalanced, which seriously degrades the performance of classification algorithms. Collapse

Topics: fraud detection marketing management

Methods: machine learning computational algorithm decision tree classification artificial neural network gradient boosting

Deep Learning-Based Imputation Method to Enhance Crowdsourced Data on Online Business Directory Platforms for Improved Services

2023 | Journal of Management Information Systems | Citations: 0

Authors: Xu, Da; Hu, Paul Jen-Hwa; Fang, Xiao

Abstract: Popular online business directory (OBD) platforms, such as Yelp and TripAdvisor, ... Expand

Abstract: Popular online business directory (OBD) platforms, such as Yelp and TripAdvisor, depend on voluntarily user-submitted data about various businesses to assist consumers in finding appropriate options for transactions. Yet the crowdsourced nature of such data restricts the availability of attribute values for many businesses on the platform. Crowdsourced data often suffer serious completeness and timeliness constraints, with negative implications for key stakeholders such as users, businesses, and the platform. We thus develop a novel, deep learning–based imputation method, premised in institutional theory, to estimate missing attribute values of individual businesses on an OBD platform. The proposed method leverages a deep model architecture and considers both inter-business and inter-attribute relationships for imputations. An application to a Yelp data set reveals our method's greater imputation effectiveness relative to prevalent methods. To illustrate the method's practical utilities and values, we further examine the efficacy of business recommendations empowered by its imputed business attribute values, in comparison with those enabled by data imputed by benchmark methods. The results affirm that the proposed method substantially outperforms benchmarks for imputing missing attribute values and empowers more effective business recommendations. This study addresses crucial, prominent completeness and timeliness constraints in crowdsourced data on OBD platforms and offers insights for downstream applications that can improve user experiences, firm performance, and platform services. Collapse

Topics: missing data crowdsourcing data completeness wireless LAN healthcare data

Methods: data imputation autoencoder data transformation deep learning multivariate imputation with chained equations

Theories: institutional theory social learning theory

Towards a reconceptualization of data in organizations: a literature review

2023 | European Conference On Information Systems | Citations: 0

Authors: Xu, Daisy; Someh, Ida Asadi; Indulska, Marta

Abstract: Information Systems researchers have sought to demonstrate the strategic value ... Expand

Abstract: Information Systems researchers have sought to demonstrate the strategic value of data in organizations through robust evidence. Over time, the ways data benefit organizations have evolved and become more diverse, yet definitions of data and their value propositions have not kept up and remain disconnected. The field still lacks clear understanding of various roles data play in organizations and how to define them. This paper presents a comprehensive review of related literature in the Information Systems and Management fields from the past two decades. We first conduct a systematic literature search and organize them into key research themes by the purpose of data use. We then propose a reconceptualization of data that takes into account their distinct features. Our aim is to provide an explanation for the unique nature of data and the diverse sources of their value in organizations. Collapse

Topics: decision making value creation business process management innovation management organizational context

Methods: literature study design artifact literature sample synthesis theory development