Boundary resource interactions in solution networks

Purpose This study aims to explore the interactions between two different and potentially complementary boundary resources in coordinating solution networks in a digital platform context: boundary spanners (those individuals who span interorganizational boundaries) and boundary interfaces (the devices that help coordinate interfirm relationships, e.g. electronic data interchanges, algorithms or chatbots). Design/methodology/approach The authors conducted a multiple case study of three firms using digital platforms to coordinate solution networks in the information communication technology and lighting facility industries. Data were collected from 30 semi-structured interviews, which are complemented by secondary data. Findings As task complexity increases, smarter digital interfaces are adopted. When the intelligence level of interfaces is low or moderate, they are only used as tools by boundary spanners or to support boundary spanners’ functions. When the intelligence level of interfaces is high or very high, boundary spanners design the interfaces and let them perform tasks autonomously. They are also sometimes employed to complement interfaces’ technological limitations and customers’ limited user ability. Research limitations/implications The industry contexts of the cases may influence the results. Qualitative case data has limited generalizability. Practical implications This study offers a practical tool for solution providers to effectively deploy boundary employees and digital technologies to offer diverse customized solutions simultaneously. Originality This study contributes to the solution business literature by putting forward a framework of boundary resource interactions in coordinating solution networks in a digital platform context. It contributes to the boundary spanning literature by revealing the shifting functions of boundary spanners and boundary interfaces.

Who Bears the Burden of a Pandemic? COVID-19 and the Transfer of Risk to Digital Platform Workers

In this paper, we analyze the recessionary effects of the COVID-19 pandemic on digital platform workers. The crisis has been described as a great work-from-home experiment, with platform ecosystems positing as its most advanced form. Our analysis differentiates the direct (health) and indirect (economic) risks incurred by workers, to critically assess the portrayal of platforms as buffers against crisis-induced layoffs. We submit that platform-mediated labor may eventually increase precarity, without necessarily reducing health risks for workers. Our argument is based on a comparison of the three main categories of platform work—“on-demand labor” (gigs such as delivery and transportation), “online labor” (tasks performed remotely, such as data annotation), and “social networking labor” (content generation and moderation). We discuss the strategies that platforms deploy to transfer risk from clients onto workers, thus deepening existing power imbalances between them. These results question the problematic equivalence between work-from-home and platform labor. Instead of attaining the advantages of the former in terms of direct and indirect risk mitigation, an increasing number of platformized jobs drift toward high economic and insuppressible health risks.

Working with Wolt

In this paper, we present the results of an ethnographic study focusing on food deliveries for the digital platform Wolt. The platform manages food transport ordered by customers to be delivered at home from restaurants, and subcontracts the transport to workers called 'couriers', who act as independent firms or entrepreneurs. The paper is based on six months of participant observation, during which time the first author worked as a courier, as well as on ad-hoc conversations and semi-structured interviews with other couriers. We describe couriers' work for the platform and discuss our findings using Möhlmann and Zalmanson's definition of algorithmic management. We found both similarities and differences. It was noticeable that the couriers were positive about their work that no penalties or wage reductions were enforced, and that human support complemented the platform's algorithmic management. Thus, the algorithmic management we observed is neither harsh (as it has been described on other platforms including Uber), nor like the algorithmic despotism present on Instacart, for example. Hence, we refer to it as 'lenient algorithmic management' and underline the importance of adding new perspectives to our understanding of what algorithmic management can be, as well as looking at the context in which it is practised. To complement this finding of lenient algorithmic management, we present a set of strategies couriers must engage in to be effective on the platform: Thus, couriers must 1) schedule their work for peak hours to limit the amount of time they waste, 2) bundle orders to increase their payment per tour, 3) make use of support to handle customers and cancel orders involving delays, and 4) make use of the ecology of local support structures. The contribution of this paper is to add new perspectives to the way we perceive algorithmic management by presenting a lenient form of algorithmic management and indicating the importance of looking at the context in which it is practised, while describing what it takes to be an effective worker on the Wolt platform.

Digitalization and higher education for sustainable development in the context of the Covid-19 pandemic: A content analysis approach

Higher education for sustainable development (HESD) in the Covid-19 pandemic faces different challenges. Empirically few studies to date have introduced much on the digitalization of higher education for sustainable development. This study aims to explore and explain the digitalization of HESD from different attitudes and to build linkages of the digitalization in HESD. Furthermore, the study makes content analysis where 1,200 tweets on digitalization in higher education for sustainable development are collected from Twitter, and 19 documents have further categorized information data via NVivo. In addition, 22 students and 9 instructors were invited for a semi-structured interview to further supplement this study and confirm its findings. This study finds that attitudes towards digitalization in the study area can be divided into three correlated attitude layers. Teaching attitudes and educational attitudes are the first level, and the second level is the digital platform attitude, technology use attitude, and resource attitude. Furthermore, network attitude, service attitude, and development attitude are the third level. Thus, through the analysis, this study suggests higher education institutions should make improvements in digitalized teaching, education quality via innovation, technological development, resources use, and development via creating a better digital platform or environment is essential for genuinely promoting the HESD.

Digital Mixed Content Analysis on Digital Platform Social Data

The explosion of platform social data as digital secondary data, collectible through sophisticated and automatized query systems or algorithms, makes it possible to accumulate huge amounts of dense and miscellaneous data. The challenge for social researchers becomes how to extract meaning and not only trends in a quantitative as well as in a qualitative manner. Through the application of a digital mixed content analysis perspective to data analysis, in this contribution, the author will present the potentiality of a hybrid digitalized approach to social content. This perspective should be seen as an applied example of organizing a framework to guide the application of integrated methods of content analysis (quantitative and qualitative) but also integrated objects of analysis (individuals, relationships, and digital actions) on digital platform social data and to address their varied nature.