Comparing Platform Owners’ Early and Late Entry into Complementary Markets

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Shi, Runyu; Aaltonen, Aleksi; Henfridsson, Ola; Gopal, Ram

Abstract: Research on platform owners' entry into complementary markets points in diverge ... Expand

Abstract: Research on platform owners' entry into complementary markets points in divergent directions. One strand of the literature reports a squeeze on post-entry complementor profits due to increased competition, while another observes positive effects as increased customer attention and innovation benefit the complementary market as a whole. In this research note, we seek to transcend these conflicting views by comparing the effects of the early and late timing of platform owners' entry. We apply a difference-in-differences design to explore the drivers and effects of the timing of platform owners' entry using data from three entries that Amazon made into its Alexa voice assistant's complementary markets. Our findings suggest that early entry is driven by the motivation to boost the overall value creation of the complementary market, whereas late entry is driven by the motivation to capture value already created in a key complementary market. Importantly, our findings suggest that early entry, in contrast to late entry, creates substantial consumer attention that benefits complementors offering specialized functionality. In addition, the findings also suggest that complementors with more experience are more likely to benefit from the increased consumer attention. We contribute to platform research by showing that the timing of the platform owner's entry matters in a way that can potentially reconcile conflicting findings regarding the consequences of platform owners' entry into complementary markets. Collapse

Topics: smart home value creation innovation management digital innovation smart speaker

Methods: experimental group longitudinal research difference in differences regression analysis method cluster analysis

Improving Information Systems Sustainability by Applying Machine Learning to Detect and Reduce Data Waste

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

Authors: Savarimuthu, Bastin Tony Roy; Corbett, Jacqueline; Yasir, Muhammad; Lakshmi, Vijaya

Abstract: Big data are key building blocks for creating information value. However, infor ... Expand

Abstract: Big data are key building blocks for creating information value. However, information systems are increasingly plagued with useless, waste data that can impede their effective use and threaten sustainability objectives. Using a constructive design science approach, this work first, defines digital data waste. Then, it develops an ensemble artifact comprising two components. The first component comprises 13 machine learning models for detecting data waste. Applying these to 35,576 online reviews in two domains reveals data waste of 1.9% for restaurant reviews compared to 35.8% for app reviews. Machine learning can accurately identify 83% to 99.8% of data waste; deep learning models are particularly promising, with accuracy ranging from 96.4% to 99.8%. The second component comprises a sustainability cost calculator to quantify the social, economic, and environmental benefits of reducing data waste. Eliminating 5948 useless reviews in the sample would result in saving 6.9 person hours, $2.93 in server, middleware and client costs, and 9.52 kg of carbon emissions. Extrapolating these results to reviews on the internet shows substantially greater savings. This work contributes to design knowledge relating to sustainable information systems by highlighting the new class of problem of data waste and by designing approaches for addressing this problem. Collapse

Topics: mobile application online review Python data management Google+

Methods: deep learning product review computational algorithm design artifact RoBERTa

Perceived Warmth and Intelligence of AI: Impact on Employee Performance

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Savoli, Azadeh; Bhatt, Mamta

Abstract: Artificial Intelligence (AI) can extend human capabilities. Thus, when AI techn ... Expand

Abstract: Artificial Intelligence (AI) can extend human capabilities. Thus, when AI technologies are integrated into businesses, they can assist employees in their tasks by augmenting their analytical skills or automating important but manual and time-consuming tasks, freeing up employees to focus on higher-value work. Extant literature has explored the effect of AI on employee performance, with some studies highlighting its benefits (e.g., increased efficiency and effectiveness). However, other studies suggest that AI may also have a detrimental effect on employee performance (e.g., by promoting AI anxiety and threat). Despite their growing use, little is known about how employees interact with and perceive AI and how this might impact their job performance. The current study aims to address this gap in knowledge by adopting an identification perspective and focusing on the perceived warmth and intelligence of AI as two key dimensions of social perception. Our research shows that the perceived warmth and intelligence of AI have a significant impact on how employees identify with AI-based technologies and eventually on their job performance. The data for this investigation were collected from 319 customer service representatives who interacted with chatbots in their daily customer service tasks. Collapse

Topics: artificial intelligence customer service task productivity computer software individual productivity

Methods: chatbot survey structural equation modeling SEM tool data transformation

Where blockchain could help - An analysis of challenges in ecosystems blockchain technology may help to solve

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Schaefer, Benjamin; Vetterling, Dennis; Wrobel, Lasse; Jung, Reinhard

Abstract: ... Expand

Abstract: Collapse

Topics: blockchain digital ecosystem database system public key encryption peer-to-peer model

Methods: literature study structured literature research case study qualitative coding

A Picture is Worth a Thousand Words – Exploring Bias in Inclusive Chatbot Design

2023 | International Conference on Business Informatics | Citations: 0

Authors: Schlimbach, Ricarda; Stoppel, Anika; Lampka, Lucas; Robra-Bissantz, Susanne

Abstract: This study examines the impact of different avatar pictures (gender & disabilit ... Expand

Abstract: This study examines the impact of different avatar pictures (gender & disability representation) and gendering on students' perceptions of chatbots in an interaction on learning strategies with 180 students from a German university. In the first experiment, we manipulated the chatbot's humanoid profile picture based on gender and the representation of a visible handicap (wheelchair). In the second experiment, we varied its language style. Statistical analysis revealed that displaying a physical disability significantly enhanced trust, credibility, and empathy but reduced perceived competence and dominance. Gender-sensitive language improved perceptions of competence, trust, credibility, and empathy, whereas we did not find significant interaction effects between both factors. Our results imply the necessity of a more inclusive design of information systems and highlight designers' responsibility in raising awareness and mitigating unconscious bias, as digital learning (technologies) continue to advance. Collapse

Topics: online learning system systems design Google+

Methods: data transformation principal component analysis experiment parametric test chatbot

Theories: social response theory

Collaborating with Generative AI: Exploring Algorithm Appreciation in Creative Writing

2023 | International Conference on Information Systems | Citations: 0

Authors: Schmidt, Corinna; Guffler, Michael; Kindermann, Bastian; Flatten, Tessa

Abstract: Generative AI (GenAI) has recently attracted a tremendous amount of public atte ... Expand

Abstract: Generative AI (GenAI) has recently attracted a tremendous amount of public attention showcasing the transformational capacity that AI-based systems have on society. By generating creative outputs in multimodal formats like texts and images, GenAI is entering domains formerly seen exclusive to human ingenuity. This raises concerns about how working with AI-based systems will affect employees. Existing research on human-AI collaboration is focusing on objective decision-making settings. We contribute to the growing IS research stream that considers AI collaboration on creative tasks. In particular, we conduct an online experiment to see whether employees appreciate GenAIgenerated creative texts and how personality traits affect this interaction. We find that getting input from GenAI rather than a colleague relates to fewer modifications performed to the draft. This relationship is moderated by conscientiousness suggesting that conscientious employees are less inclined to accept suggestions and hence may not gain as much from GenAI tools. Collapse

Topics: artificial intelligence personality organizational context technology adoption human AI collaboration

Methods: experiment computational algorithm regression analysis method chatbot parametric test

Theories: unified theory of acceptance and use of technology technology acceptance model

Hello Diversity! Digital Ideation Hackathon: Fostering Gender Equality in Digital Innovation

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

Authors: Schmitt, Franziska; Sundermeier, Janina; Mikkelsen, Kirsten

Abstract: This panel report outlines key insights from the Hello Diversity! Hackathon on ... Expand

Abstract: This panel report outlines key insights from the Hello Diversity! Hackathon on gender equality in the workplace held digitally in November 2020. The one-day event with more than 150 participants featured panel discussions and ideation sessions to enable the participants to develop innovative tools, strategies, and processes to minimize gender inequality in workforces involved in digital innovation. Overall, the hackathon aimed to build bridges between research and practice to derive answers on how existing barriers towards more gender equality in digital innovation processes and outcomes can be addressed. The theoretically informed challenges that were tackled during the event concerned topics such as stereotypes and discrimination, incentivizing workforce equality, and necessary support infrastructures in public and private spheres. The developed solutions indicate that much remains to be done to address the lack of processes, organizational structures, and holistic knowledge regarding the importance and benefits of diversity and inclusion in digital innovation. The hackathon culminated in pitches made on the envisioned solutions to kick-start their implementation and encourage research support and progress. Collapse

Topics: innovation management digital innovation LinkedIn decision making Instagram

Methods: design artifact design methodology

Preferred Gamification Elements in a Health Behavior Change Support System for Stress Management

2023 | HICSS | Citations: 0

Authors: Schoch, M.; Berger, M.; Lahmer, S.; Reuther, M.

Abstract: Stress is a serious hazard to individuals. Health behavior change support syste ... Expand

Abstract: Stress is a serious hazard to individuals. Health behavior change support systems (HBCSSs) may support individuals to modify their behaviors toward a healthy lifestyle. Previous studies have shown that HBCSSs for stress management can improve individual coping behavior but their success depends on the users' adoption and long-term use. Gamification elements (GEs) can contribute to continuous use by motivating their users, enabling sustained healthy stress coping behavior. With a mixed-methods approach, we identified suitable GEs through six interviews with users of a mobile coping assistant prototype. Based on those insights, we designed GE mockups and surveyed 204 participants using the best-worst-scaling method to examine the users' preferences. The results demonstrate that users mostly prefer feedback elements, such as scoreboards and progress bars in HBCSSs for stress management. Social interaction GEs score worst. Our interviews indicate that this could be due to privacy reasons. Collapse

Topics: mobile application gamification mobile system mobile game privacy

Methods: qualitative interview survey design artifact mixed method descriptive statistic

Sustainable Information Security Sensitization in SMEs: Designing Measures with Long-Term Effect

2023 | HICSS | Citations: 0

Authors: Scholl, Margit

Abstract: This paper outlines an overall scenario for ongoing personnel development measur ... Expand

Abstract: This paper outlines an overall scenario for ongoing personnel development measures designed to increase information security awareness in small and medium-sized enterprises (SMEs) in Germany and to help small businesses improve their security levels and defenses. The three-year project combines different actors and a multitude of methods, with a focus on conducting interviews and online surveys with companies, developing customized game-based awareness trainings, tests, and on-site attacks, and creating measurements and evaluations as well as maturity statements, guidelines, and low-threshold security concepts. A mix of analog/digital serious games and operational trainings with reviews are of key importance here. Compared with the findings from the applied scientific literature on behavioral research and design, the ultimate goal at project’s end is to extrapolate statements on the success and efficacy of the measures and their long-term effect. Collapse

Topics: cybersecurity awareness anonymity

Methods: qualitative interview survey

Design Features and User Perspectives of Patient Confidentiality and Consent Features for Substance Use Disorder

2023 | HICSS | Citations: 0

Authors: Schooley, Benjamin; Feldman, Sue

Abstract: Software initiatives that collect, store, and share substance use disorder (SUD) ... Expand

Abstract: Software initiatives that collect, store, and share substance use disorder (SUD) patient data experience significant barriers, including additional patient privacy and confidentiality requirements beyond HIPAA. This paper reports on a design science research effort aimed at managing peer support and clinician interactions with individuals with SUD and sharing their pertinent information with emergency rooms (ERs). Privacy and confidentiality challenges are presented along with the resulting system design that was implemented in the state of Alabama. A usability and security survey was administered to system end users (n=18) to assess information security perceptions. Findings suggest respondents were satisfied with the system, while several users felt that the system was unnecessarily complex due to MFA and auto-log off features. Specific confidentiality considerations and logic mechanisms should be designed into the application functionality at the project outset to facilitate transitions of care benefits for SUD patients. Collapse

Topics: usability privacy data security information exchange healthcare data

Methods: survey design artifact design science qualitative interview usability testing