Can AI benefit individual resilience? The mediation roles of AI routinization and infusion

The recent COVID-19 pandemic has raised concerns about individual resilience in the face of adversity. The abundant research suggested that artificial intelligence (AI) can help organizations handle changes during this challenging period. However, little empirical research has explored whether the presence of AI enhances individual resilience against adversities. Drawing on the reciprocal determinism theory, this study considers the formation of two typical post-adoption behaviors and their subsequent results in individual resilience. The structural equation modeling shows that AI factors (usability and sociability) and personal factors (self-efficacy) determine usage behaviors (routine and infusion use), in turn affecting individual resilience. The OLS results suggest the right half of the U-shaped relationships between infusion use and resilience. Two-step fsQCA offers three configurations resulting in high resilience under the different presence of AI factors and also suggests the roles of user behaviors. The study provides new theoretical enlightenment for the impact of digital service technology on individuals and enriches the existing literature on the usage of digital service technology. The findings provide practical implications for practitioners to design AI products better to improve smart service experience.     

Artificial intelligence as a boundary-crossing object for employee engagement and performance

The study proposes AI-powered tools and applications as boundary-crossing objects to examine how AI performance can affect employees' job engagement, service and job performance. Job security is modelled as a moderator in the boundary-crossing process. Several theories including boundary crossing, goal setting and self-regulation are drawn on to posit these relationships. The study was undertaken with Australia-based full-time employees who had experience with AI-powered tools at work. The results show that AI performance had a significant effect on job engagement, and employee service performance, which were significantly related to job performance appraisal. Job engagement and service performance exhibited significant mediation effects between AI and job performance. The moderation effect exerted by job security was significant in enhancing employees’ job engagement and service performance. The study contributes to service research and human resource management literature. The findings have implications for service marketers and human resource practitioners.     

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.     

Who will lead and who will follow: Identifying Influential Users in Online Social Networks

Along with the explosive growth of the phenomenon Online Social Networks (OSN), identifying influential users in OSN has received a great deal of attention in recent years. However, the development of practical approaches for identifying them is still in its infancy. By means of a structured literature review, the authors analyze and synthesize the publications particularly from two perspectives. From a research perspective, they find that existing approaches mostly build on users’ connectivity and activity but hardly consider further characteristics of influential users. Moreover, they outline two major research streams. It becomes apparent that most marketing-oriented articles draw on real-world data of OSN, while more technology-oriented papers rather have a theoretical approach and mostly evaluate their artifacts by means of formal proofs. The authors find that a stronger collaboration between the scientific Business and Information Systems Engineering (BISE) and Marketing communities could be mutually beneficial. With respect to a practitioner’s perspective, they compile advice on the practical application of approaches for the identification of influential users. It is hoped that the results can stimulate and guide future research.     

Understanding the user satisfaction and loyalty of customer service chatbots

The artificial intelligence (AI) chatbot is emerging as a significant corporate customer-facing application, potentially increasin customer service efficiency while reducing costs. However, little work has sought to assess the quality of service they provide consumers. This study applies the e-service quality by incorporating conversational AI quality to predict users' satisfaction and loyalty to customer service chatbots. The proposed model was empirically evaluated using survey data collected from 219 users responding about their perceptions of customer service chatbots. The findings indicate that AI chatbot service recovery quality and AI chatbot conversational quality significantly influence user satisfaction. On the other hand, core AI chatbot service quality and satisfaction significantly influenced chatbot user loyalty. This study contributes to researchers and practitioners by proposing and evaluating a more comprehensive chatbot e-service quality that combines both fundamental (core service and service recovery qualities) and human-like (conversational quality) aspects of e-service. The results are of value in devising future AI chatbot services and related strategies.     

The Climate for Service

SUMMARY

We develop a framework in which internal employees' diagnoses of their firm's service climate determine their role behavior towards customers and, ultimately, customer satisfaction, loyalty, retention and shareholder value. Elements of the framework include: (1) foun dation issues (fundamental human behavior issues like the presence of necessary resources and the quality of leadership), (2) internal service (the quality of service employees report they receive internally from others), (3) service climate (the degree to which management emphasizes service quality in all of its activities), and (4) customer-focused service behavior. How this research is done is reviewed and research supporting elements of the framework is described. How the approach can be adapted for promoting CLV goals is explicated and answers to some frequently asked questions about change to an organization with a service quality and CLV focus are described.     

Machines augmenting entrepreneurs: Opportunities (and threats) at the Nexus of artificial intelligence and entrepreneurship

Artificial intelligence (AI) refers to machines that are trained to perform tasks associated with human intelligence, interpret external data, learn from that external data, and use that learning to flexibly adapt to tasks to achieve specific outcomes. This paper briefly explains AI and looks into the future to highlight some of AI's broader and longer-term societal implications. We propose that AI can be combined with entrepreneurship to represent a super tool. Scholars can research the nexus of AI and entrepreneurship to explore the possibilities of this potential AI-entrepreneurship super tool and hopefully direct its use to productive processes and outcomes. We focus on specific entrepreneurship topics that benefit from AI's augmentation potential and acknowledge implications for entrepreneurship's dark side. We hope this paper stimulates future research at the AI-entrepreneurship nexus.Executive summaryArtificial intelligence (AI) refers to machines that are trained to perform tasks associated with human intelligence, interpret external data, learn from that external data, and use that learning to flexibly adapt to tasks to achieve specific outcomes. Machine learning is the most common form of AI and largely relies on supervised learning—when the machine (i.e., AI) is trained with labels applied by humans. Deep learning and adversarial learning involve training on unlabeled data, or when the machine (via its algorithms) clusters data to reveal underlying patterns.AI is simply a tool. Entrepreneurship is also simply a tool. How they are combined and used will determine their impact on humanity. While researchers have independently developed a greater understanding of entrepreneurship and AI, these two streams of research have primarily run in parallel. To indicate the scope of current and future AI, we provide examples of AI (at different levels of development) for four sectors—customer service, financial, healthcare, and tertiary education. Indeed, experts from industry research and consulting firms suggest many AI-related business opportunities for entrepreneurs to pursue.Further, we elaborate on several of these opportunities, including opportunities to (1) capitalize on the “feeling economy,” (2) redistribute occupational skills in the economy, (3) develop and use new governance mechanisms, (4) keep humans in the loop (i.e., humans as part of the decision making process), (5) expand the role of humans in developing AI systems, and (6) expand the purposes of AI as a tool. After discussing the range of business opportunities that experts suggest will prevail in the economy with AI, we discuss how entrepreneurs can use AI as a tool to help them increase their chances of entrepreneurial success. We focus on four up-and-coming areas for entrepreneurship research: a more interaction-based perspective of (potential) entrepreneurial opportunities, a more activities-based micro-foundation approach to entrepreneurial action, a more cognitively hot perspective of entrepreneurial decision making and action, and a more compassionate and prosocial role of entrepreneurial action. As we discuss each topic, we also suggest opportunities to design an AI system (i.e., entrepreneurs as potential AI designers) to help entrepreneurs (i.e., entrepreneurs as AI users).AI is an exciting development in the technology world. How it transforms markets and societies depends in large part on entrepreneurs. Entrepreneurs can use AI to augment their decisions and actions in pursuing potential opportunities for productive gains. Thus, we discuss entrepreneurs' most critical tasks in developing and managing AI and explore some of the dark-side aspects of AI. Scholars also have a role to play in how entrepreneurs use AI, but this role requires the hard work of theory building, theory elaboration, theory testing, and empirical theorizing. We offer some AI topics that we hope future entrepreneurship research will explore. We hope this paper encourages scholars to consider research at the nexus of AI and entrepreneurship.     

Organizing a precarious black box: An actor‐network account of the Atlantic Schools of Business, 1980–2006

There is a growing use of actor‐network theory (ANT) throughout management and organization studies. While earlier ANT research used ethnography to “follow the actors” in the production of organization/knowledge, more recent studies use archival sources to examine developments over time. We extend the latter approach using qualitative social network analysis (SNA) and apply this to a case study of the Atlantic Schools of Business (ASB). Our contribution is two‐fold: first, through an examination of actors in the ASB networking processes over 26 years, we demonstrate how the seemingly stable surface of an organization can hide the precariousness of organizing; second, we reveal the potential fusion of ANT with SNA as a method for dealing with large qualitative datasets over long periods of time. Copyright © 2015 ASAC. Published by John Wiley & Sons, Ltd.     

A Framework for Collaborative Artificial Intelligence in Marketing

We develop a conceptual framework for collaborative artificial intelligence (AI) in marketing, providing systematic guidance for how human marketers and consumers can team up with AI, which has profound implications for retailing, which is the interface between marketers and consumers. Drawing from the multiple intelligences view that AI advances from mechanical, to thinking, to feeling intelligence (based on how difficult for AI to mimic human intelligences), the framework posits that collaboration between AI and HI (human marketers and consumers) can be achieved by 1) recognizing the respective strengths of AI and HI, 2) having lower-level AI augmenting higher-level HI, and 3) moving HI to a higher intelligence level when AI automates the lower level. Implications for marketers, consumers, and researchers are derived. Marketers should optimize the mix and timing of AI-HI marketing team, consumers should understand the complementarity between AI and HI strengths for informed consumption decisions, and researchers can investigate innovative approaches to and boundary conditions of collaborative intelligence.     

Chatbots or me? Consumers’ switching between human agents and conversational agents

With the application of artificial intelligence (AI) technology in organizational frontlines, customers' service experiences have begun to shift from interactions with service personnel to those with technology. However, only a few studies have explored customers' behavioral switch from human-mediated services to technology-mediated ones with regard to the application of AI in frontline services. Based on the push–pull mooring framework, this study explored the determinants that affect consumers’ behavioral switch from using human agents to using AI-based conversational agents. Data collected from 441 users of banking services were analyzed using structural equation modeling. The findings reveal that both push effects—namely, low empathy and low adaptability—and pull effects, including anytime/anywhere connectivity, association, visibility, and personalization, have positive influences on switching behavior. Finally, in addition to having a direct influence on switching behavior, frequency of service use positively moderated the relationship between pull effects and switching behavior.     

Transforming the Customer Experience Through New Technologies

New technologies such as Internet of Things (IoT), Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), virtual assistants, chatbots, and robots, which are typically powered by Artificial Intelligence (AI), are dramatically transforming the customer experience. In this paper, we offer a fresh typology of new technologies powered by AI and propose a new framework for understanding the role of new technologies on the customer/shopper journey. Specifically, we discuss the impact and implications of these technologies on each broad stage of the shopping journey (pre-transaction, transaction, and post-transaction) and advance a new conceptualization for managing these new AI technologies along customer experience dimensions to create experiential value. We discuss future research ideas emanating from our framework and outline interdisciplinary research avenues.     

Engaging and retaining customers with AI and employee service

Artificial intelligence (AI) permeates in service organisations as a tool to enhance operational efficiency and improve customer experience. Reports show that most consumers prefer human interactions with service employees. Drawing on this observation, the current study examines how customers' service experiences with employees and AI influence customer engagement and loyalty. Customers’ emotional intelligence is proposed as a moderator between service experience and customer engagement. The study was conducted with hotel customers in Australia. The results show that whilst both service experience with employees and AI are significantly related to customer engagement and loyalty, only certain dimensions make significant unique variances in the outcome variables. The findings indicate that customers prefer employee service. These service experiences also have significant partial mediation effects on customer loyalty. Emotional intelligence has a significant moderation effect on customer engagement. Discussion of these findings and implications derived from this study concludes this paper.     

Impact of Technology-Based Self-Service on Employees, Customers, and Sales in the Korean Services Market

For service providers, efficient production and delivery is becoming increasingly important. If customers can play the role of partial employees when participating in the service production and delivery, service providers can reduce the workload and achieve higher productivity. This study’s purpose is to investigate the impact of technology-based self-service (TBSS) which is designed for improvement of operational efficiency of service organizations on employees, customers, and sales, especially focused on South Korea’s service market. In-depth interviews with managers of large service providers and a questionnaire survey of employees were used as the research method. Our findings indicate that technology-based self-service positively influences employee satisfaction, but it provides disadvantages in sales, and in customer satisfaction when the services fail. Moreover, some interesting results were identified. We present the details of the statistical results and the implications found from the study.     

Social anxiety as a moderator in consumer willingness to accept AI assistants based on utilitarian and hedonic values

This study develops a comprehensive research model to explain user willingness to accept AI assistants, and the acceptance path pertaining to this process. User data was used to test how the advantages of AI assistant (accuracy, responsiveness, compatibility, anthropomorphism, & affinity) influence consumer utilitarian and hedonic value, and explore how their willingness to accept AI assistants is affected by their value perceptions. This research also examines whether social anxiety moderates the relationship between AI assistant advantages and utilitarian/hedonic value. The study reveals that AI assistant advantages are important factors affecting the utilitarian/hedonic value perceived by users, which further influence user willingness to accept AI assistants. The relationships between AI assistant advantages and utilitarian and hedonic value are affected differently by social anxiety. Marketers and managers in the AI context can refer to the study methods to help improve AI assistants and develop more effective marketing strategies for product promotion.     

Affective Computing in Marketing: Practical Implications and Research Opportunities Afforded by Emotionally Intelligent Machines

After years of using AI to perform cognitive tasks, marketing practitioners can now use it to perform tasks that require emotional intelligence. This advancement is made possible by the rise of affective computing, which develops AI and machines capable of detecting and responding to human emotions. From market research, to customer service, to product innovation, the practice of marketing will likely be transformed by the rise of affective computing, as preliminary evidence from the field suggests. In this Idea Corner, we discuss this transformation and identify the research opportunities that it offers.

     

Service Business Development in Small- to Medium-Sized Equipment Manufacturers

Purpose: This article explores service business development by small- and medium-sized equipment manufacturers (SMEMs). It focuses on underlying dynamic and operational capabilities in service business development.

Design/Methodology/Approach: The research design is based on case study research with nine companies from Germany, Italy, Sweden, and Switzerland.

Findings: The findings are twofold. First, the authors elaborate the phases and capabilities necessary for service business development. Second, they argue that these phases and capabilities depend on sales channels (direct sales versus indirect sales through distributors) and customer structures (a limited number of strategic customers versus many end-customers). SMEMs selling directly to a limited number of strategic customers develop organizational capabilities through four phases: (1) consolidation of service offerings, (2) job enlargements in organizational functions, (3) job enlargement in the key account teams, and (4) orchestration of partners to widen the solutions offered to customers. SMEMs selling indirectly through distributors to many customers develop organizational capabilities through the following four phases: (1) rearranging collaboration with distributors, (2) enlarging the service competencies of distributors, (3) modifying distributors into subsidiaries, and (4) enlarging jobs in the sales function of the subsidiaries.

Research Limitations/Implications: The research limitations are due mainly to the intrinsic nature of qualitative research.

Practical Implications: Managers can obtain guidance for service business development from the phases and capabilities described in the paper.

Originality/Value: The study offers a comprehensive framework for assisting researchers in conceptualizing service business development and operationalizing capabilities. The results provide testable propositions that can be used to guide future research.     

“Alexafying” shoppers: The examination of Amazon's captive relationship strategy

The virtual assistants' market is drastically growing and is expected to reach $2.1 billion by 2020. Nonetheless, the quick expansion and high penetration of e-retailers’ AI ecosystem into the shopper's journey is still under-researched in the extant literature. Amazon's Alexa in particular has been fast proliferating into the customer's journey, favoring the development of captive audiences given this new ambient environment. Through a mixed methodology using both qualitative and quantitative approaches, this study examines Amazon's captive relationship strategy on shoppers, brands and competing retailers. The research findings show that Amazon's AI relationship strategy with its customers is based on forming a multi-faceted identity for the AI that would later on facilitate a captive situation that would lead to an addictive relationship. This study is amongst the first to examine the rapid development of e-retailers’ AI ecosystem into the shopper's journey. Taking the pioneering case of Amazon's Alexa powered devices, this research presents a working framework upon which scholars and practitioners alike could base their future studies and strategies on in the fast-growing field of interactive voice assistants and AI led conversations.     

Analysing marketing customization strategies in Australasian firms: a framework for services and goods exporters

Previous studies suggest that services and goods marketers can share internationalization and market entry frameworks. Very little research has investigated any similarity of marketing customization frameworks of services and goods exporters. This study has proposed a common customization framework, which was examined using the experiences of 101 services and goods firms operating in a highly different environment. It was found that the marketing environment–marketing strategy framework for service and good exporters was mostly similar, but that the extent of the influence of selected factors was stronger for service firms. The framework of marketing strategy–performance was confirmed to be mostly similar. The results of this study suggest that, after internationalization and market entry mode theories, a common marketing customization framework can probably be identified across selected services and goods sectors.     

Digital Analytics: Modeling for Insights and New Methods

Firms are increasingly turning towards new-age technologies such as artificial intelligence (AI), the internet of things (IoT), blockchain, and drones, among others, to assist in interacting with their customers. Further, with the prominence of personalization and customer engagement as the go-to customer management strategies, it is essential for firms to understand how to integrate new-age technologies into their existing practices to aid seamlessly in the generation of actionable insights. Towards this end, this study proposes an organizing framework to understand how firms can use digital analytics, within the changing technology landscape, to generate consumer insights. The proposed framework begins by recognizing the forces that are external to the firm then lead to the generation of specific capabilities by the firm. Further, the firms capabilities can lead to the generation of insights for decision-making that can be data-driven and/or analytics-driven. Finally, the proposed framework identifies the creation of value-based outcomes for firms and customers resulting from the insights generated. Additionally, we identify moderators that influence: (a) the impact of external forces on the development of firm capabilities, and (b) the creation of insights and subsequent firm outcomes. This study also identifies questions for future research that combines the inclusion of new-age technologies, generation of strategic insights, and the achievement of established firm outcomes.