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

Fighting for the status quo: Threat to tech self-esteem and opposition to competing smartphones

2023 | Information & Management | Citations: 0

Authors: Cano Bejar, Arturo Heyner; Ray, Soumya; Huang, Yu Hsuan

Abstract: Users of rival technology platforms, such as Android-versus-iOS, often harbor ho ... Expand

Abstract: Users of rival technology platforms, such as Android-versus-iOS, often harbor hostile opposition to each other. The dominant perspective of technology resistance, conceptualized as technology inertia, cannot explain such strong oppositional behavior. We propose a threat to tech self-esteem construct that captures distinct threat perceptions and offers a unified explanation for oppositional intentions. We conducted an empirical study of smartphone users, where we find that the threat to technology self-esteem arises from distinct antecedents and offers a singular explanation for opposition. Moreover, we show the unintended consequences of employing lockin strategies and provide advice on why and how to mitigate them. Collapse

Topics: smartphone switching cost self efficacy Android iOS

Methods: survey partial least squares path modeling structural equation modeling cross sectional research statistical hypothesis test

Theories: reactance theory

Criteria Based Evaluation of Cross-Platform Development Frameworks

2023 | HICSS | Citations: 0

Authors: El Tom, Ali; Bogdan, Cristian; Majchrzak, Tim A.; Grønli, Tor-Morten

Abstract: Cross-platform development frameworks continue to play an important role in deve ... Expand

Abstract: Cross-platform development frameworks continue to play an important role in developing of mobile applications. There are a plethora of possible frameworks to choose between, and all claim to give developers an advantage in one or more aspects of the development cycle. To further shed light on this area, our research investigates the selection of mobile cross-platform frameworks by investigating criteria and methods for choosing the best suitable framework for the development context. Our study confirm previous research findings and with that anchor the categorisation for a cross-platform comparison taxonomy. Our research further contribute with a criteria check-list to apply for cross-platform framework selection and showcase its use through a comparative study of a cross-platform application implemented in two frameworks. Although showcasing novelty in the criteria checklist, further research is needed to make the checklist robust and streamlined for public use. Collapse

Topics: mobile application Android iOS JavaScript programming language

Methods: cross sectional research literature study

Webcam Eye Tracking for Desktop and Mobile Devices: A Systematic Review

2023 | HICSS | Citations: 0

Authors: Heck, Melanie; Becker, Christian; Deutscher, Viola

Abstract: Building the Internet of Behaviors (IOB) obviously requires capturing human beha ... Expand

Abstract: Building the Internet of Behaviors (IOB) obviously requires capturing human behavior. Sensor input from eye tracking has been widely used for profiling in market research, adaptive user interfaces, and other smart systems, but requires dedicated hardware. The wide spread of webcams in consumer devices like phones, tablets, notebooks, and smart TVs has fostered eye tracking with commodity cameras. In this paper, we present a systematic review across the IEEE and ACM databases -- complemented by snowballing and input from eye tracking experts at CHI 2021 -- to list and characterize publicly available webcam eye trackers that estimate the point-of-regard on devices with no additional hardware. Information from articles was supplemented by searching author websites and code repositories, and contacting authors. 16 eye trackers were found that can be used. The restrictions regarding license terms and technical performance are presented, enabling developers to choose an appropriate software for their IoB application. Collapse

Topics: tablet source code database system desktop computer website

Methods: oculometry literature sample structured literature research meta analysis synthesis

The Influence of User Feedback on Complementary Innovation in Platform Ecosystems: NLP Evidence on the Value of Multihoming

2023 | HICSS | Citations: 0

Authors: Hoffmann, Philipp; Shmagina, Veronika; Halckenhaeusser, André; Lueker, Nele

Abstract: We study how user feedback affects innovation of multihomed applications within ... Expand

Abstract: We study how user feedback affects innovation of multihomed applications within and across platform ecosystems. Therefore, we conduct a quantitative NLP based case study. Our sample consists of 10 multihomed applications with more than 325,000 user reviews on Apple’s iOS and Google’s Android platform between January and March 2021. We analyze how user reviews translate into functional feature releases of the selected applications within and across platforms. We report three findings. First, we find that about 61% of the functional feature improvements on both platforms were previously demanded by users in the form of user feedback. Second, we show that user feedback of iOS users is more likely to be incorporated compared to Android users’ feedback. Finally, we observe that about 10% of feature releases are inspired by cross-platform feedback, providing initial evidence that user feedback from multihoming applications might stimulate cross-platform innovation and enhance the applications’ quality and innovativeness. Collapse

Topics: innovation management Android iOS mobile application mobile application market

Methods: natural language processing case study word2vec NLTK theoretical contribution

Chatbot commerce—How contextual factors affect Chatbot effectiveness

2023 | Electronic Markets | Citations: 0

Authors: Hsu, Pei-Fang; Nguyen, Tuan (Kellan); Wang, Chen-Ya; Huang, Pei-Ju

Abstract: The emergence of Chatbots has attracted many firms to sell their merchandise via ... Expand

Abstract: The emergence of Chatbots has attracted many firms to sell their merchandise via chats and bots. Although Chatbots have received tremendous interest, little is understood about how different usage contexts affect Chatbots’ effectiveness in mobile commerce. Due to differences in their nature, not all shopping contexts are suitable for Chatbots. To address this research gap, this study examines how contextual factors (i.e., intrinsic task complexity that embraces shopping task attributes and group shopping environment, and extrinsic task complexity that entails information intensity) affect user perceptions and adoption intentions of Chatbots as recommendation agents in mobile commerce. Applying the lenses of cognitive load theory (CLT) and common ground theory (CGT), we perform an experiment and apply quantitative analytical approaches. The results show that Chatbots are more suitable in the context of one-attribute, information-light, and group-buying tasks, whereas traditional Apps are suitable for multi-attribute, information-intensive, and single-buying scenarios. These findings make important theoretical contributions to the IT adoption literature as well as to CLT and CGT theory by contextualizing the evolving state of Chatbot commerce and providing guidelines for designing better Chatbot user experiences, thereby enhancing user perceptions and adoption intentions. Collapse

Topics: mobile application task complexity electronic commerce technology adoption recommender system

Methods: chatbot experiment qualitative interview chi squared test survey

Theories: cognitive load theory

Competing for Temporary Advantage in a Hypercompetitive Mobile App Market

2023 | Management Information Systems Quarterly | Citations: 0

Authors: James, Tabitha; Qiao, Zhilei; Shen, Wenqi; Wang, G. Alan; Fan, Weiguo

Abstract: Hypercompetitive mobile app stores are characterized by rapid innovation and in ... Expand

Abstract: Hypercompetitive mobile app stores are characterized by rapid innovation and intense competition. App firms must vie for temporary competitive advantage through competitive actions such as releasing product improvements. We study how competitive indicators influence a particular competitive actionapp updates-in a mobile game app market. Our results reveal that app firms take action to improve or sustain their temporary competitive advantage, updating their apps when there are opportunities to capitalize on popularity (e.g., rank and rating volume are increasing) and when their apps' advantages are threatened (e.g., customer ratings are decreasing). We also find that app updates are released in response to competitors' actions-specifically, when competitors update their apps and new competitors enter ranking lists. Moreover, our findings show that app firms release app updates when an app's customer rating volume is increasing or when an app firm's portfolio of game apps is less diverse, relative to its competitors. We conduct additional analyses that show that older app firms are responsive to more competitive indicators than younger ones and major updates are primarily used to respond to serious threats to apps' competitive positions. Overall, our results indicate that app updates are competitive actions used to improve or sustain temporary advantage when competitive indicators reveal opportunities to improve or threats to apps' competitive positions. Collapse

Topics: mobile application mobile application market digital distribution innovation management packaged software

Methods: robustness check instrumental variables estimation computational algorithm archival research longitudinal research

Design for Social Sharing: The Case of Mobile Apps

2023 | Information Systems Research | Citations: 1

Authors: Karanam, Subrahmanyam Aditya; Agarwal, Ashish; Barua, Anitesh

Abstract: With a large number of mobile apps on platforms such as iOS and Android, app dev ... Expand

Abstract: With a large number of mobile apps on platforms such as iOS and Android, app developers face a significant challenge in generating market demand. Apps can incorporate social features to share information and create awareness. We focus on the impact of these features and their interplay with intrinsic features in the head, body, and tail of the demand distribution. Using a panel of version release notes from the iOS platform, we develop a novel hierarchical deep learning model to extract intrinsic and social features. Our results suggest that social features help increase the demand for tail apps and are also useful for head apps in informing users about new intrinsic features. To explore possible mechanisms, we analyze how different types of social features (personal and platform), intrinsic features (common versus differentiating), and app quality influence demand. We find that social features that allow sharing on platforms with large audiences are more effective at increasing demand than those on personal messaging systems. Furthermore, within platforms, social features perform better on those with stronger ties, such as Facebook as opposed to Twitter. Our analysis of different types of intrinsic features reveals that social features can help increase the demand for all apps when introduced with differentiating or less common intrinsic features. Furthermore, we find that there is a negative effect on the demand of tail apps when differentiating intrinsic features are combined with platform-based social features that have weaker ties. However, this negative effect is limited to low-quality tail apps only. Our results underscore the differences in the effect of different types of social and intrinsic features in various parts of the demand distribution. Our study provides managerial guidance to app developers in enabling social sharing through design choices and generating higher demand.History: Kai Lung Hui, Senior Editor; Beibei Li, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1151. Collapse

Topics: mobile application Facebook Twitter iOS accounting

Methods: deep learning robustness check computational algorithm word embedding natural language processing

Catching the Viewer's Eye: Examining Exploration and Exploitation Strategies in the Live Streaming Market

2023 | International Conference on Information Systems | Citations: 0

Authors: Kwon, Sung Hyun; Hann, Il-Horn

Abstract: Live streaming has become an important feature on social media platforms like F ... Expand

Abstract: Live streaming has become an important feature on social media platforms like Facebook and Instagram. More and more entrepreneurs (content creators) are competing in live streaming platforms like Twitch to maximize the attention they receive from consumers (viewers). In this competitive landscape, it is crucial for entrepreneurs to develop and provide new compelling content that can maximize the consumers' attention and aid the discovery of their content. We adopt an exploration-exploitation framework and assess the four strategies these new entrepreneurs could use to attract viewership and position themselves on Twitch: exploration, exploitation, learning from viewers, and their neighbor streamers. We combined the natural language processing techniques with theory-driven measures to accomplish this. Using our proposed measures, we estimate the utility of consumers from these different strategies using the discrete choice demand model. Collapse

Topics: Twitch database system decision making wiki online game

Methods: natural language processing RoBERTa transformer model BERT language model

Pushing Yourself Harder: The Effects of Mobile Touch Modes on Users’ Self-Regulation

2023 | Information Systems Research | Citations: 0

Authors: Liu, Yang (Alison); Jiang, Zhenhui (Jack); Choi, Ben C. F.

Abstract: Many mobile applications use push notifications and reminders to explicitly educ ... Expand

Abstract: Many mobile applications use push notifications and reminders to explicitly educate, remind, and motivate users to perform healthy behaviors. However, users do not always act according to these explicit digital interventions. Our study investigates whether users’ self-regulation can be implicitly facilitated with a proper mobile interaction design. Specifically, we investigate the impacts of two touch modes that are supported by force-based interaction technology, that is, pressing and tapping. Drawing on the theory of embodied cognition, which suggests that people automatically infer meanings from their bodily actions, we conjecture that pressing, compared with tapping, enhances self-regulation because the action of pressing on the touchscreen embodies resolute approach motivation toward goals. We test our hypotheses in three experiments. The first experiment investigates beverage choices on a mobile app; the second experiment examines goal setting on a fitness app; and the third experiment focuses on personal hygiene learning on a mobile education app. The results from the three experiments show that pressing actions can improve users’ self-regulation in selecting a healthier but less tasty beverage (Study 1), setting higher exercise goals and performing more physical exercise (Study 2), and reducing lapses in maintaining personal hygiene (Study 3). In addition, such effects were more salient among users with a higher level of health knowledge and a promotion-focused health orientation. This study contributes to healthcare IT research by showing that mobile interaction can be leveraged to nudge users toward enhanced self-regulation. History: Alok Gupta, Senior Editor; David (Jingjun) Xu, Associate Editor. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72202172 and 72032006], the Research Grants Council of the Hong Kong Special Administrative Region, China [Grant HKU 17502921], the University Research Committee of the University of Hong Kong [Grant 201905159007], and the Ministry of Education of Singapore [Grants 2018-T1-002-097 and MOE2017-T2-1-127]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1155. Collapse

Topics: mobile application smartphone mobile system mobile fitness app health information system

Methods: experiment survey parametric test ANOVA data transformation

Theories: cognitive theory