Mind the channel! An affordance perspective on how digital voice channels encourage or discourage employee voice

We apply an affordance lens on qualitative data from three case organisations using a digital voice channel providing employees with the opportunity to speak up via answering periodic mini‐surveys and making comments in an anonymous mini‐forum. We find that imbrications of material and social agencies (i.e., the voice channel's features and managerial reactions to voice) in the respective organisational contexts culminate in employees perceiving the channel as either affording or constraining voice, leading to perceived voice outcomes that eventually encourage or discourage them to speak up. Whether voice is encouraged or discouraged partly results from the mere interaction between employees and the digital voice channel independent of managerial reactions. Our findings thus challenge the emphasis on managerial behaviour and reactions to voice in explaining voice behaviour and outcomes in extant literature.     

Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis

The main goal of this research is to provide a comprehensive understanding of the actual progresses in artificial intelligence, with emphasis on chatbots as emerging forms of customer assistance in online retailing. Drawing upon an analysis of the chatbot patents in the past 20 years, our findings show the increasing technology push towards the adoption of new conversational agents based on natural language. Findings also highlight the extent to which the research and development efforts are attempting to improve artificial intelligence systems that characterize chatbots. To this end, technology advancements are mainly focusing on: (i) improving chatbot ability to automatically draw inferences on users starting from multiple data sources, and (ii) using consumers’ knowledge adaptively to provide more customized solutions. Finally, results show the tight relationship between the digital assistants’ analytical skills and their ability to automatically interact with the users.     

Sustainable product innovation and changing consumer behavior: Sustainability affordances as triggers of adoption and usage

This conceptual paper argues that for sustainable product innovation to make a contribution to addressing sustainability issues, we need to understand not only why consumers adopt sustainable products but also what makes them use these in sustainable way. To explain how specific product features can change the ways in which consumers engage with sustainable products in the adoption and usage phase, we draw on affordance theory. Affordances refer to the potential for agentic action of users in relation to a technological object. We develop a conceptual framework that explains how sustainable product innovation can lead to the design of sustainability affordances that stimulate adoption and sustainable usage. The framework shows how three forms of agency—material, firm, and user agency—interact and together influence a product's sustainability affordances that drive adoption and a change in consumer behavior. The framework explains how trade-offs between a product's environmental features and consumer expectations regarding desired functionalities and user experience can be overcome.     

What is digital possession and how to study it: a conversation with Russell Belk,Rebecca Mardon,Giana M. Eckhardt,Varala Maraj,Will Odom,Massimo Airoldi,Alessandro Caliandro,Mike Molesworth and Alessandro Gandini

ABSTRACT

The platformisation of digital consumption, means that increasingly many of the things that we call ours – our messages, photos, music, achievements – are entangled in complex socio-technical arrangements which require ongoing market mediation. In this context, refining our understanding of what digital possessions are and how to study them is vital. This requires refocusing research away from existing comparative analyses between digital and material possessions. To do so, we organised an interdisciplinary roundtable discussion with critical marketers and digital media scholars, consumer researchers, digital sociologists and researchers in Human Computer Interaction (HCI) at the 11^th Interpretive Consumer Research Conference held in Lyon in May 2019. The result of that discussion is this curation of comments which deal with theoretical, methodological and critical issues and a bold agenda for future research.     

Online boundary work tactics: an affordance perspective

Studies of work/life balance have focussed on offline settings, and even though technology is considered as a boundary‐influencing feature, social media have not been the focus. Social technologies challenge the relationship between work and private life in new ways, due to their identified affordances: visibility, persistence, association and editability (Treem and Leonardi, 2012). In this paper, we present the results of a study of social media use (Facebook and Twitter) by employees of non‐governmental organisations (NGOs), and show how, through their affordances, these technologies influence the relationship and boundaries between work and non‐work, increasing visibility and reducing individual privacy. Consequently, we observe boundary work tactics that aim to protect private life from both public and professional scrutiny, in prohibitive, reactive or active ways. Our results call for organisations to develop explicit policies or guidelines for social media use, in both their own interests and those of their employees.     

Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential

Artificial intelligence (AI) and machine learning (ML) may save money and improve the efficiency of business processes, but these technologies can also destroy business value, sometimes with grave consequences. The inability to identify and manage that risk can lead some managers to delay the adoption of these technologies and thus prevent them from realizing their potential. This article proposes a new framework by which to map the components of an AI solution and to identify and manage the value-destruction potential of AI and ML for businesses. We show how the defining characteristics of AI and ML can threaten the integrity of the AI system’s inputs, processes, and outcomes. We then draw from the concepts of value-creation content and value-creation process to show how these risks may hinder value creation or even result in value destruction. Finally, we illustrate the application of our framework with an example of the deployment of an AI-powered chatbot in customer service, and we discuss how to remedy the problems that arise.     

How IT affordances influence customer engagement in live streaming commerce? A dual-stage analysis of PLS-SEM and fsQCA

Engaging customers and building loyalty are crucial for operators who embrace live streaming. While previous research has identified many social and technical factors that assist operators in engaging their customers, few studies have examined customer engagement from a sociotechnical perspective (i.e., IT affordances). To fill the gap, a theoretical model grounded in the stimulus-organism-response (SOR) framework is developed to explain how IT affordances (i.e., metavoicing, visibility, and guidance shopping affordance) influence customer engagement through swift guanxi and perceived enjoyment in live streaming. To test the model, a survey with 371 valid samples was carried out and a dual-stage analysis using partial least squares-structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) was employed. PLS-SEM results reveal that IT affordances in live streaming indirectly affect customer engagement through swift guanxi and perceived enjoyment. Additionally, fsQCA suggests that two different configurations with high or low visibility affordance can lead to the same outcome of high customer engagement. These findings offer insights into how IT affordances can promote customer engagement and assist operators of live streaming commerce (LSC) in developing effective strategies to engage their customers.     

It is not merely a chat: Transforming chatbot affordances into dual identification and loyalty

Artificial intelligence (AI)-powered service chatbot functionality is changing to more effectively connect with customers in the era of digital marketing. As such, an understanding of how to enhance user perceptions and behaviors through interface design has become crucial. Using affordance actualization theory and social identity theory as a theoretical lens, this study explored how chatbot affordances affect consumer brand loyalty. Data were collected from 369 respondents who had at least one conversation with AI chatbots from a predetermined list of banks in Taiwan; the collected data were then analyzed using structural equation modeling. The results revealed that anytime–anyplace connectivity, information association, visibility, and interactivity affordances provided by chatbots positively influenced chatbot exploitation, which in turn affected perceived brand competence and perceived brand warmth. Furthermore, perceived brand competence influenced customer–chatbot identification and customer–brand identification, whereas brand warmth influenced customer–brand identification. Customer–chatbot identification also influenced brand loyalty both indirectly—through customer–brand identification—and directly. These findings are useful for assisting managers in various industries in the application of AI technologies to implement digital transformation strategies and improve customer services.     

Machine learning for enterprises: Applications,algorithm selection,and challenges

Machine learning holds great promise for lowering product and service costs, speeding up business processes, and serving customers better. It is recognized as one of the most important application areas in this era of unprecedented technological development, and its adoption is gaining momentum across almost all industries. In view of this, we offer a brief discussion of categories of machine learning and then present three types of machine-learning usage at enterprises. We then discuss the trade-off between the accuracy and interpretability of machine-learning algorithms, a crucial consideration in selecting the right algorithm for the task at hand. We next outline three cases of machine-learning development in financial services. Finally, we discuss challenges all managers must confront in deploying machine-learning applications.