App stores like Google Play and Apple AppStore have over 3 million apps covering nearly every kind of software and service. Billions of users regularly download, use, and review these apps. Recent studies have shown that reviews written by the users represent a rich source of information for the app vendors and the developers, as they include information about bugs, ideas for new features, or documentation of released features. The majority of the reviews, however, is rather non-informative just praising the app and repeating to the star ratings in words. This paper introduces several probabilistic techniques to classify app reviews into four types: bug reports, feature requests, user experiences, and text ratings. For this, we use review metadata such as the star rating and the tense, as well as, text classification, natural language processing, and sentiment analysis techniques. We conducted a series of experiments to compare the accuracy of the techniques and compared them with simple string matching. We found that metadata alone results in a poor classification accuracy. When combined with simple text classification and natural language preprocessing of the text--particularly with bigrams and lemmatization--the classification precision for all review types got up to 88---92 % and the recall up to 90---99 %. Multiple binary classifiers outperformed single multiclass classifiers. Our results inspired the design of a review analytics tool, which should help app vendors and developers deal with the large amount of reviews, filter critical reviews, and assign them to the appropriate stakeholders. We describe the tool main features and summarize nine interviews with practitioners on how review analytics tools including ours could be used in practice.

On the Automatic Classification of App Reviews.

This article explores a service-dominant (S-D) logic, service-ecosystems approach to studying value cocreation and the (re)formation of service systems. We outline the central premises of S-D logic and elaborate the concept of a service ecosystem to propose a framework that focuses on resource integration as a central means for connecting people and technology within and among service systems. This ecosystems view emphasizes the social factors that influence, and are influenced by, service-for-service exchange. We draw on systems theory and a structurational model of technology to underscore the importance of networks of actors, as well as institutions---e.g., rules, social norms---as critical components of service systems. We argue that this service-ecosystems framework provides a robust and dynamic approach for studying resource integration, value cocreation, and the (re)formation of service systems, and provides important insights for systematically innovating service.

Value cocreation and service systems (Re)formation: A service ecosystems view

Crowdsourcing has gained much attention in practice over the last years. Numerous companies have drawn on this concept for performing different tasks and value creation activities. Nevertheless, despite its popularity, there is still comparatively little well-founded knowledge on crowdsourcing, particularly with regard to crowdsourcing intermediaries. Crowdsourcing intermediaries play a key role in crowdsourcing initiatives as they assure the connection between the crowdsourcing companies and the crowd. However, the issue of how crowdsourcing intermediaries manage crowdsourcing initiatives and the associated challenges has not been addresses by research yet. We address these issues by conducting a case study with a German start-up crowdsourcing intermediary called testCloud that offers software testing services for companies intending to partly or fully outsource their testing activities to a certain crowd. The case study shows that testCloud faces three main challenges, these are: managing the process, managing the crowd and managing the technology. For each dimension, we outline mechanisms that testCloud applies for facing the challenges associated with crowdsourcing projects.

Managing Crowdsourced Software Testing: A Case Study Based Insight on the Challenges of a Crowdsourcing Intermediary

Digitization, artificial intelligence, and service robots carry serious ethical, privacy, and fairness risks. Using the lens of corporate digital responsibility (CDR), we examine these risks and their mitigation in service firms and make five contributions. First, we show that CDR is critical in service contexts because of the vast streams of customer data involved and digital service technology’s omnipresence, opacity, and complexity. Second, we synthesize the ethics, privacy, and fairness literature using the CDR data and technology life-cycle perspective to understand better the nature of these risks in a service context. Third, to provide insights on the origins of these risks, we examine the digital service ecosystem and the related flows of money, service, data, insights, and technologies. Fourth, we deduct that the underlying causes of CDR issues are trade-offs between good CDR practices and organizational objectives (e.g., profit opportunities versus CDR risks) and introduce the CDR calculus to capture this. We also conclude that regulation will need to step in when a firm’s CDR calculus becomes so negative that good CDR is unlikely. Finally, we advance a set of strategies, tools, and practices service firms can use to manage these trade-offs and build a strong CDR culture. Graphical Abstract

https://journals.sagepub.com/doi/pdf/10.1177/10946705221130467

Corporate Digital Responsibility in Service Firms and Their Ecosystems

The working standard of shared office spaces has evolved in recent years. Due to the ongoing COVID-19 pandemic, many companies have instituted work from home (WFH) policies in accordance with public health guidelines in order to increase social distancing and decrease the spread of COVID-19. As the pandemic and WFH-related policies have continued for more than a year, there has been a rise in people becoming accustomed to the remote environments; however, others are more enthusiastic about returning to in-person work environments, reflecting the desire to restore pre-pandemic environments. As working from home is related to transportation issues such as changing commuting patterns and decreased congestion, motorized trips, and emission, there is a need to explore the extent of public attitudes on this important issue. This study used unique open-source survey data that provides substantial information on this topic. Using an advanced categorical data analysis method known as cluster correspondence analysis, this study identified several key findings. Not having prior WFH experiences, being eager to interact with colleagues, difficulties with adapting to virtual meeting technologies, and challenges with self-discipline while WFH were strongly associated with individuals who refused to continuously WFH at all after the pandemic. Individuals holding a strong view against the seriousness of the COVID-19 pandemic were also largely associated with never choosing WFH during and after the pandemic. For individuals with some prior WFH experiences, the transition to WFH every day in response to the outbreak was much easier, compared to those without prior experiences. Moreover, being forced to WFH during the COVID-19 pandemic positively influences the choice of WFH after the pandemic. The findings of this study will be beneficial to help policymakers and sustainable city planners understand public opinions about WFH. 

https://doi.org/10.1016/j.cstp.2022.04.002

Work from home in the post-COVID world

Smart cities and communities aim for social wellbeing. Mobilizing and integrating various institutions, actors, and resources are crucial when building and instantiating smart community initiatives. The design of such an arrangement is a complex phenomenon, difficult to conduct systematically and to observe empirically. We address this challenge by applying a multilevel design framework for service systems to an ongoing design science research project. The research project pursues the goal of building a neighborhood community as an instantiation of smart communities by activating and leveraging local institutions, actors, and resources on an IT-enabled engagement platform. We demonstrate how this multilevel perspective informs the design process for building smart communities. Based on micro-level observations, the interdependence of engagement-stimulating mechanisms related to the platform’s design at the meso-level, and design implications for the institutional arrangement at the macro-level are emphasized as inseparable design activities for mobilizing and integrating actors and resources.

/pdf/multilevel-design-for-smart-communities-the-case-of-building-qowgz4h37a.pdf

Multilevel Design for Smart Communities – The Case of Building a Local Online Neighborhood Social Community

Public health measures are curtailing the COVID-19 pandemic's spread but also impact individual and societal well-being. Altogether, they test the social resilience of communities, their collective ability to cope with crises. The pandemic highlights the significance of the immediate local community or neighborhood, be it for providing assistance to individuals in need, the sensible sharing of public spaces or a renewed conscience for supporting local businesses. We argue that online neighborhood social networks (ONSNs) represent a viable solution for improving social resilience as they enhance a community's resistance to disruptions, quicken recovery to a normal level of functioning and can become a platform for creative solutions to strengthening social resilience. We conduct a multiple case study to demonstrate how ONSNs foster social resilience in the focal crisis and beyond. Furthermore, we identify design dilemmas and highlight avenues for IS research with a high impact on local communities and their well-being. © 2021 IEEE Computer Society. All rights reserved.

/pdf/fostering-social-resilience-via-online-neighborhood-social-347yki0yl0.pdf

Fostering social resilience via online neighborhood social networks during the CoviD-19 pandemic and beyond: Status quo, design dilemmas and research opportunities

Open phenomena including open resources, processes such as crowdsourcing and their effects have initiated fundamental shifts in the way organizations conduct their business. With increasing recognition of the value of openness, represented by principles such as transparency, access, participation and democracy, crowdsourcing has established itself as a suitable mechanism for various use-cases ranging from decision-making to idea generation, microtasking and problem-solving. Increasingly, organizations have begun to crowdsource to their employees instead of external crowds. However, few studies analyze concrete use-cases of internal crowdsourcing and further research is needed. We propose that internal crowdsourcing is particularly useful for software requirements engineering (RE) within organizations. Crowd-based RE alleviates shortcomings of traditional RE approaches such as requiring copresence or lacking representativeness. Research on crowd-based RE remains scarce, particularly concerning intra-organizational settings and the post-implementation phase of software projects. Defining high-quality requirements demands contextual expertise as well as experience and internal crowds seem to be decidedly well-suited for solving RE-related tasks as they exhibit these exact traits. Following a design science research approach, we develop design principles for internal crowd-based RE and instantiate them in a public-sector organization leading to a holistic evaluation. Subsequently, we formulate recommendations for establishing crowd-based RE in intra-organizational settings.

Leveraging the Internal Crowd for continuous Requirements Engineering - Lessons Learned from a Design Science Research Project.

Conceptualizing Design Parameters of Online Neighborhood Social Networks.

Due to declining fertility rates and rising life expectancy, the world’s population is ageing at an unprecedented pace. This demographic change is expected to exert pressure on social security as well as healthcare systems and poses the risk of social exclusion of the elderly. As urban areas are home to the majority of the global elderly population, they are disproportionately affected by this development. Cities have begun responding with strategies ranging from policy and regulation reform to investments in innovative healthcare technologies with the goal of becoming “age-friendly”. Enabling the elderly to live a socially active, healthy and self-determined lifestyle past retirement are among the prime objectives for alleviating the challenges of an ageing society. With increasing urbanization, human, technological and infrastructural resources of urban contexts or neighborhoods have presented themselves as important determinants of elderly well-being. We propose that an age-friendly digital neighborhood platform can activate and leverage these resources to the benefit of the elderly population, contributing to the mitigation of the challenges of an ageing society. Following a design science research approach, we develop design principles for such an age-friendly digital neighborhood platform and evaluate a prototypical instantiation in two case neighborhoods in a German metropolitan area.

Pascal Vogel

Papers

Multilevel Design for Smart Communities – The Case of Building a Local Online Neighborhood Social Community

Fostering social resilience via online neighborhood social networks during the CoviD-19 pandemic and beyond: Status quo, design dilemmas and research opportunities

Leveraging the Internal Crowd for continuous Requirements Engineering - Lessons Learned from a Design Science Research Project.

Conceptualizing Design Parameters of Online Neighborhood Social Networks.

Healthy, active and connected: towards designing an age-friendly digital neighborhood platform