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

Measuring the Actual Office Workspace Utilization in a Desk Sharing Environment Based on IoT Sensors

2023 | DESRIST | Citations: 0

Authors: Arz von Straussenburg, Arnold F.; Blazevic, Mevludin; Riehle, Dennis M.

Abstract: Businesses that follow the trend of flexible work arrangements with home offices ... Expand

Abstract: Businesses that follow the trend of flexible work arrangements with home offices and hybrid work models are increasingly utilizing desk sharing to allocate available desks in the office efficiently. In this work, we investigate the utilization of flexible work arrangements, specifically the implementation of desk sharing in hybrid work models. To optimize the occupancy rate of shared desks and simultaneously meet demand and save costs, it is crucial to have real-time knowledge of their availability which can be obtained by recording occupancy over time. We utilize the Design Science Research (DSR) methodology to develop a prototype solution for real-time monitoring of shared desks occupancy in hybrid work models. The proposed prototype is a low-cost solution utilizing Internet of Things (IoT) technology consisting of a motion sensor and accelerometer that collect occupancy data and transmit it via Long Range Wide Area Network (LoRaWAN). Signal processing utilizing cloud computing is employed to enhance the quality and accuracy of the data, which is subsequently made available through a web dashboard. The prototype’s effectiveness is evaluated through a small-scale pilot deployment in a real-world environment over a two-week period, with reference values obtained during periods of non-use serving as a benchmark for the evaluation. Collapse

Topics: mobile system internet of things digital dashboard application programming interface smart wearable device

Methods: design artifact wearables based research design science experiment descriptive statistic

Engaging with self-tracking applications: how do users respond to their performance data?

2023 | European Journal of Information Systems | Citations: 0

Authors: Constantiou, Ioanna; Mukkamala, Alivelu; Sjöklint, Mimmi; Trier, Matthias

Abstract: Self-tracking devices and applications have become popular in recent years and c ... Expand

Abstract: Self-tracking devices and applications have become popular in recent years and changed user behaviour. Previous research has primarily focused on the adoption of self-tracking devices and their effects on self-assessment. As adoption increases, user engagement becomes prominent for the continuous use of the devices and the applications. In this study, we focus on user engagement with activity tracking applications, e.g., Fitbit Flex and Jawbone Up that offer data on user performance. We collected data from semi-structured interviews with 54 participants. We propose a process model comprising four stages which involve distinct user interactions with data: review, react, reflect, and respond. We advance research in this domain by the proposed process model that explicates user engagement in two cases: when the user encounters satisfactory or unsatisfactory results. In the latter case, we depict four response tactics when users are confronted with unsatisfactory results. Collapse

Topics: smart wearable device mobile system fitness tracker user behavior marketing management

Methods: wearables based research business process modeling qualitative interview archival research longitudinal research

IT-based regulation of personal health: Nudging, mobile apps and data

2023 | Journal of Information Technology | Citations: 0

Authors: Davidson, Elizabeth; Winter, Jenifer Sunrise; Chiasson, Mike

Abstract: Mobile health applications and devices (“mobile health apps”) play increasingly ... Expand

Abstract: Mobile health applications and devices (“mobile health apps”) play increasingly important roles in the lives of individuals interested in self-regulating their personal health behaviors. While some appear to be simply consumer products and services, many are embedded in regulatory programs aimed at compliance with expert guidelines. In this paper, we draw on de Vaujany et al.’s framework for organizational IT-based regulation systems to consider how systems operate in open and distributed contexts in which actors have strong agency and regulation is indirect and voluntary. To do so, we consider how IT artifacts become embedded in practices, how data are implicated in regulatory feedback loops, and how individual, organizational and technological actors are mobilized and with what regulatory outcomes. We develop an instrumental case study as a vignette of five regulatory episodes (continuous glucose monitoring systems used by persons with diabetes) to examine how expert rules materialized in mobile health apps, data about bodily states, and IT features such as displays and alarms “nudge” individuals towards compliance with self-regulatory guidelines and practices. Through this analysis, we identify two related regulatory affordances of mobile health apps for predicting and surveilling personal health. We theorize how multilevel networks composed of trifecta of rules, IT artifacts, and practices develop as a regulatory lattice through which social regulation is realized. We conclude by considering the broader implications of this analytical approach to study voluntary, data-enriched regulatory systems. Collapse

Topics: mobile health affordance mobile application sensemaking health information system

Methods: design artifact theory development case study wearables based research

Theories: theory of affordance

A Task-Technology-Identity Fit Model of Smartwatch Utilisation and User Satisfaction: A Hybrid SEM-Neural Network Approach

2023 | Information Systems Frontiers | Citations: 0

Authors: El-Masri, Mazen; Al-Yafi, Karim; Kamal, Muhammad Mustafa

Abstract: Smartwatches are wearable devices intended to be smartphone companions that capt ... Expand

Abstract: Smartwatches are wearable devices intended to be smartphone companions that capture health data and ease access to notifications. They have also become personalisable standing as a fashion statement. This combination resulted in staggering adoption rates recently leading to question whether smartwatch users’ choice and use satisfaction emerge from utility features or from its fashion characteristics. This paper proposes and validates a fit theory to investigate the antecedents of adopters’ satisfaction. Besides evaluating fit with identity, the model assesses both perceived and actual task-technology fit of smartwatches. A questionnaire-based quantitative approach is used to collect data from about 300 smartwatch users in Qatar. To test the proposed model, data is analysed using structural equation modeling (SEM) and artificial neural networks (ANN). Furthermore, ANN sensitivity analysis ranks the importance of the fit factors affecting users’ choice during pre- and post-adoption stages. Both task-technology and technology-identity fit factors are quasi-equally important in explaining 62% of satisfaction variance. ANN analysis revealed that post-adoption satisfaction is primarily attributed to smartwatches’ ability to fit with users’ identity and secondarily to its perceived fit with tasks. Nevertheless, pre-adoption choice of smartwatches is mainly guided by their functionality. This paper is the first to propose and validate an integrated task-technology-identity fit model to explain smartwatch utilization and users’ satisfaction. The originality also lies in assessing actual task-technology fit and as perceived by users. Employing two modes of analysis revealed extra insights too. Collapse

Topics: smart watch mobile system user satisfaction personality smart wearable device

Methods: structural equation modeling data transformation survey factor analysis principal component analysis

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

Near-Infrared Spectroscopy for Bladder Monitoring: A Machine Learning Approach

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Fechner, Pascal; König, Fabian; Kratsch, Wolfgang; Lockl, Jannik; Röglinger, Maximilian

Abstract: Patients living with neurogenic bladder dysfunction can lose the sensation of th ... Expand

Abstract: Patients living with neurogenic bladder dysfunction can lose the sensation of their bladder filling. To avoid over-distension of the urinary bladder and prevent long-term damage to the urinary tract, the gold standard treatment is clean intermittent catheterization at predefined time intervals. However, the emptying schedule does not consider actual bladder volume, meaning that catheterization is performed more often than necessary, which can lead to complications such as urinary tract infections. Time-consuming catheterization also interferes with patients' daily routines and, in the case of an empty bladder, uses human and material resources unnecessarily. To enable individually tailored and volume-responsive bladder management, we design a model for the continuous monitoring of bladder volume. During our design science research process, we evaluate the model's applicability and usefulness through interviews with affected patients, prototyping, and application to a real-world in vivo dataset. The developed prototype predicts bladder volume based on relevant sensor data (i.e., near-infrared spectroscopy and acceleration) and the time elapsed since the previous micturition. Our comparison of several supervised state-of-the-art machine and deep learning models reveals that a long short-term memory network architecture achieves a mean absolute error of 116.7 ml that can improve bladder management for patients. Collapse

Topics: computer hardware open source mobile application user interface programming language

Methods: design artifact qualitative interview design process support vector machine computational algorithm

SLEEP TRACKING AS A STRESSOR: EXPERIENCES FROM SMART RING USERS

2023 | European Conference On Information Systems | Citations: 0

Authors: Feng, Shan; Mäntymäki, Matti; Salmela, Hannu

Abstract: This study explores the stress that users attribute to sleep tracking with a sm ... Expand

Abstract: This study explores the stress that users attribute to sleep tracking with a smart ring. Prior research has discussed the challenges and barriers related to sleep-tracking technologies, but specific research on the stress caused by these technologies is scarce. This study explores the potential stressors that users attribute to sleep tracking executed via a smart ring. We conducted 38 semi-structured interviews and used thematic analysis to identify eight potential stressors from sleep tracking: complexity, invasion, inaccuracy, unreliability, data-perception discrepancy, pursuit of perfect data, vague guidance and interpretations, and overload with multiple self-tracking technologies. We further theorize these potential stressors using the person-technology fit model. The paper contributes to the nascent Information Systems research on technostress in the context of sleep tracking. Collapse

Topics: technostress website smart wearable device smart watch smartphone

Methods: qualitative interview qualitative content analysis theory development personal interview wearables based research

Introduction to the Minitrack on Personal and Population Health Technologies in Disease Surveillance

2023 | HICSS | Citations: 0

Authors: Goldfine, Charlotte; Chai, Peter R.; Lai, Jeffrey; Carreiro, Stephanie

Abstract: Recent advances in wearable, implantable and ingestible technologies permit unp ... Expand

Abstract: Recent advances in wearable, implantable and ingestible technologies permit unprecedented assessments of various aspects of health and wellness. The ubiquity of smartphones, smart watches and fitness tracking tools has provided vast amounts of digital physiologic and behavioral data. These "phenotypes" of health -prosody of smartphone use, biometric data surrounding health events, geolocation and ambient environment conditions -provide an opportunity for social scientists, engineers and clinicians to advance health interventions antecedent to exacerbations of disease. For example, phenotypes of smartphone use have been correlated with changes in mental health and pain, ingestible sensors measure real-time changes and contexts of medication adherence in various diseases, and biometric data may predict different states of health conditions prior to their clinically apparent manifestation.The COVID-19 pandemic continues to spur increasing acceptance of digital biomarkers and personal technologies as health management tools. Additionally, technologies that leverage public health infrastructure such as wastewater-based epidemiology and crowdsourced street maps provide novel insights into the effects of disease in communities at the population health level.The goal of this minitrack is to facilitate multidisciplinary collaboration and exploration of various facets of health technologies and their ability to surveil and predict disease. Last year's minitrack explored the design and implementation challenges as innovative technologies were deployed in clinical settings: virtual windows to enhance the patient experience in the emergency department, wearable sensors to detect alcohol consumption, agent-based modeling to evaluate the cost-savings of poison control centers, and the use of noise-cancelling earbuds to improve sleep quality for healthcare shift workers.This year's minitrack features four papers that explore innovative technological applications to improve the treatment and detection of disease states.In our first paper, the authors present a novel augmented reality (AR) training for two emergency medical procedures (bag-valve-mask ventilation and needle decompression of pneumothorax), demonstrating no difference between AR and standard video training in usability and usefulness scores or expert rating of the technique. Our second paper details the development of a novel asynchronous learning module for medical toxicology education that can uniquely be delivered via text-messaging, an intervention that emergency medicine residents and physician assistants at various stages of training find to be easy-to-use and of high educational value. The third paper presents formative qualitative work toward the development of a gamified app to help individuals in Vietnam maintain adherence to antituberculosis therapy. Our fourth paper examines the use of a wearable sensor system to detect stress and craving in individuals with substance use disorder. Finally, we conclude with a discussion regarding the ability to leverage technology to detect and prevent abuse in academic medicine.We are excited to re-convene in person this year at HICSS-56. We are looking forward to generating fruitful discussions of the featured papers and fostering the development of new ideas for future work. Collapse

Topics: smart wearable device mobile application online chat usability smart device

Methods: wearables based research literature sample agent based simulation

A Human-in-the-Loop Segmented Mixed-Effects Modeling Method for Analyzing Wearables Data

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Srinivasan, Karthik; Currim, Faiz; Ram, Sudha

Abstract: Wearables are an important source of big data, as they provide real-time high-re ... Expand

Abstract: Wearables are an important source of big data, as they provide real-time high-resolution data logs of health indicators of individuals. Higher-order associations between pairs of variables is common in wearables data. Representing higher-order association curves as piecewise linear segments in a regression model makes them more interpretable. However, existing methods for identifying the change points for segmented modeling either overfit or have low external validity for wearables data containing repeated measures. Therefore, we propose a human-in-the-loop method for segmented modeling of higher-order pairwise associations between variables in wearables data. Our method uses the smooth function estimated by a generalized additive mixed model to allow the analyst to annotate change point estimates for a segmented mixed-effects model, and thereafter employs Brent's constrained optimization procedure to fine-tune the manually provided estimates. We validate our method using three real-world wearables datasets. Our method not only outperforms state-of-the-art modeling methods in terms of prediction performance but also provides more interpretable results. Our study contributes to health data science in terms of developing a new method for interpretable modeling of wearables data. Our analysis uncovers interesting insights on higher-order associations for health researchers. Collapse

Topics: smart wearable device mobile system analytical information system human computer interaction human in the loop

Methods: design science wearables based research conceptual modelling hierarchical linear modeling computational algorithm

User-Centered Visual Design of Alarms in Manufacturing Dashboards: Insights on Comprehensibility and Preferences

2023 | International Conference on Information Systems | Citations: 0

Authors: Stahmann, Philip; Gravemeier, Laura; Dittmer, Anke; Janiesch, Christian; Thomas, Oliver

Abstract: The digitization of the manufacturing domain results in an enormous amount of a ... Expand

Abstract: The digitization of the manufacturing domain results in an enormous amount of available data decision-makers are continuously exposed to. Consequently, critical alarm information must be easily comprehensible and suit decision-makers' preferences to facilitate timely reactions and thus prevent harm to production processes and employees. However, despite its importance, academia and practice show little consensus regarding alarm design in manufacturing dashboards. Against this background, the purpose of our study is twofold. First, we identified 5 factors for visual alarm design with overall 12 design options in a structured literature review. Second, we investigated the effect these design options have on users' comprehension and preferences in a conjoint study and a best-worst scaling approach with 98 participants with experience working in manufacturing. Our results show that alarm description and their visual integration are the most important factors for alarm design. In this regard, a cluster analysis reveals more nuanced and more stable preferences in more experienced users. Further, we find that color-coding-based content classification entails best performance in recognizing alarms. We contribute to academia and practice by providing actionable insights that may support improving alarm design in manufacturing. Collapse

Topics: digital dashboard usability user interface virtual reality tablet

Methods: cluster analysis machine learning structured literature research descriptive statistic factorial experiment