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.     

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.     

Customer Experience Creation: Determinants, Dynamics and Management Strategies

Retailers, such as Starbucks and Victoria's Secret, aim to provide customers a great experience across channels. In this paper we provide an overview of the existing literature on customer experience and expand on it to examine the creation of a customer experience from a holistic perspective. We propose a conceptual model, in which we discuss the determinants of customer experience. We explicitly take a dynamic view, in which we argue that prior customer experiences will influence future customer experiences. We discuss the importance of the social environment, self-service technologies and the store brand. Customer experience management is also approached from a strategic perspective by focusing on issues such as how and to what extent an experience-based business can create growth. In each of these areas, we identify and discuss important issues worthy of further research.     

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.     

Virtual and augmented reality: Advancing research in consumer marketing

Virtual reality (VR) and augmented reality (AR) technologies are having a profound impact on a variety of marketing practices and are attracting increasing attention from marketing researchers. In this article, we review developments in VR/AR applications and research in the area of consumer marketing. We propose a conceptual framework for VR/AR research in consumer marketing that centers around consumer experiences provided by VR/AR applications along the customer journey and the effectiveness of such applications, and delve into the key concepts and components of the framework. Next, we provide a comprehensive overview of VR/AR applications in current practices and extant research on VR/AR in consumer marketing. Finally, based on this framework, we offer an outlook for future developments of VR/AR technologies and applications, discuss managerial implications, and prescribe directions for research on consumer marketing.     

What (de) motivates customers to use AI-powered conversational agents for shopping? The extended behavioral reasoning perspective

Artificial Intelligence (AI)-powered conversational agents have become ubiquitous tools in the digital transformation of conventional customer-company interactions. Despite the widespread implementation of Artificial Intelligence (AI)-powered conversational agents, there is still a limited understanding of how customers use and resist these technologies for shopping. To address this gap, this study investigates factors that influence the usage and resistance of AI-based conversational agents for shopping using the extended behavioral reasoning theory (BRT) and partial least squares-based structural equation modeling (PLS-SEM). To test the proposed framework, this study conducted two empirical studies in South Korea. Study 1 focused on text-based chatbots with a sample of 232 participants, while Study 2 examined voice-based chatbots with a sample of 234 participants. The results of the both studies mainly supported the hypotheses driven by the extended BRT. Theoretically, this study contributes by offering a comprehensive understanding of customer motivation, attitudes, and behavioral intentions toward the use of AI-powered conversational agents in shopping. Managerially, this study provides important insights for retail managers and developers of AI-powered conversational agents for shopping. By understanding the factors that drive customer usage and resistance, managers, and developers can better design and deploy these innovative technologies to enhance the customer experience and improve business outcomes.     

Evolution of retail formats: Past,present, and future

In this paper, the authors review current literature on retail formats and propose a new customer-centric framework for retailers to focus on as they continue to innovate and evolve. Specifically, they review the literature on how formats compare in their attributes and compete with each other; the role of customer behavior in format choice; and developments in multichannel and omnichannel retailing. They propose a framework for retail formats suggesting two paths – either reduce friction in the customer journey or enhance customer experience. They discuss the challenges faced by offline (physical store-first) and online (digital-first) retailers and elaborate on strategies each type of retailer is pursuing to address these challenges. Finally, they offer directions for future research in this domain. They conclude by calling for newer digital-first and physical-first players to continue coming up with different customer-centric formats, which they predict will slowly morph into integrated retailers, leaving space for newer players to enter the market and hence keep the wheel of retailing spinning.     

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.     

Siri,Alexa, and other digital assistants: a study of customer satisfaction with artificial intelligence applications

ABSTRACT

Digital assistants (e.g., Apple’s Siri, Amazon’s Alexa, Google’s Google Assistant) are highly complex and advanced artificial intelligence (AI) based technologies. Individuals can use digital assistants to perform basic personal tasks as well as for more advanced capabilities. Yet, the functional and topical use of a digital assistant tends to vary by individual. This study reflects the contextual experiences of the respondents. At present, there is little empirical evidence of customer satisfaction with digital assistants. PLS-SEM was used to analyse 244 survey responses to examine this research gap. The results confirmed that expectations and confirmation of expectations have a positive and significant relationship on customer satisfaction with digital assistants. This study provides evidence that customer expectations are being satisfied through the digital assistant interaction experience. As firms integrate digital assistants into their operations, they must help customers properly define what to expect from the firm’s interactive experience.     

How to create a realistic customer journey map

Although many articles discuss customer journey mapping (CJM), both academics and practitioners still question the best ways to model the consumer decision journey. We contend that most customer journey maps are critically flawed. They assume all customers of a particular organization experience the same organizational touchpoints and view these touchpoints as equally important. Furthermore, management lacks an understanding of how to use CJM as a cross-functional, strategic tool that promotes service innovation. This article proposes a solution to the unwieldy complexity of CJM by linking customer research to the CJM process and by showing managers how to develop a customer journey map that improves a customer's experience at each touchpoint. Using the case of an actual retail mall, we show that common CJM assumptions about the equal importance of all touchpoints are fundamentally wrong, and how easy it is for retail managers and strategic planners to make incorrect judgements about customer experience. This article demonstrates through a case study how customer research helped a mall's strategic management team understand which touchpoints were more or less critical to customer experience. It also shows key strategic initiatives at each touchpoint, resulting in cross-functional input aimed to advance service innovation at the mall.     

Supermarket self-checkout service quality,customer satisfaction,and loyalty: Empirical evidence from an emerging market

Supermarket shoppers around the world are increasingly encountering and using self-service technologies (SSTs) during their shopping process. The SSTs are mainly offered to reduce retailer costs and enhance customer's experience. Among the many different SSTs available, self-checkout systems (SCS) have become an extremely popular choice of supermarkets around the world. Although some of the main motivations of the supermarkets for offering SCSs are cost cutting, speed, and convenience, supermarkets are also assuming that these services would enhance customer experience, satisfaction, and ultimately loyalty. However, empirical evidence is needed to better understand customer expectations of SCS service quality and how technology based service quality impacts retail patronage. Therefore, the purpose of this research is to examine the service quality of supermarket/grocery store SCSs and its impact on customer satisfaction and loyalty in an emerging market, namely Turkey. Using the SSTQUAL scale (Lin and Hsieh, 2011), data (n=275) for the study is collected from shoppers who had just completed going through the self-checkout counter in a large supermarket chain. The results of this study show that SCS service quality positively influences loyalty through the customer satisfaction path. Managerial and research implications of the findings are discussed.     

Reversing the Logic: The Path to Profitability through Relationship Marketing

Many firms have experienced greater success through implementing relationship marketing strategies. This is achieved by gaining knowledge about their own customers through database marketing and about the general marketplace through marketing research. Over time, this has led firms to adopt a general framework which we call the conventional path to profitability. This conventional framework suggests that new product innovation leads to acquisition, acquisition combined with a rich experience leads to satisfaction, satisfaction leads to loyalty and customer retention, and loyalty/retention leads to profitability. However, we show that some of the links in the framework are weak based on both academic research and marketplace realities. Consequently, we reverse the logic of the conventional path to profitability. We introduce a new approach that starts the customer relationship management strategy with customer profitability and the notion that different customers should be rewarded and satisfied differently. In addition, we outline a strategy that relationship marketing firms can implement, leading to higher levels of customer profitability and offer directions for future research.     

How do firms handle variability in customer experience? A dynamic approach to better understanding customer retention

Firms invest in customer experience in the expectation that these investments will ultimately provide positive financial returns. In practice, however, customers are continuously exposed by the changes occurring in their personal perceptions of customer experience and market surrounding. Using a unique and comprehensive dataset containing customer-level and market-level information for a sample of 13,761 customers in the telecom market, we empirically test the proposed framework by applying multilevel modeling techniques. The results offer novel insights into the effects of customer experience, its variability (customer level), and market turbulence (market level) on customer retention, including the moderating effect of relationship age.     

Customer-based execution strategy in a global digital economy

Multinational corporations (MNCs) are continuing to invest more in expanding into new markets around the world. These firms are faced with determining the optimal go-to-market strategy in these heterogeneous new markets to attract and retain profitable customers. This paper provides an organizing framework to help firms develop profitable customer-level strategies across countries in the digital environment. We start by providing a summary of the marketing literature on a customer-based execution strategy. Next, we discuss how the evolving digital landscape is affecting firms’ relationships with customers and describe some of the current digital product and process innovations in the marketplace. We discuss boundary conditions for how these digital product and process innovations might affect profitable customer strategies in a global context. In addition, we discuss implementation challenges that MNCs will likely face in deploying these customer-level strategies and other stakeholders (outside of customers) that will likely play a role in the execution of these customer-level strategies. Finally, we summarize set of research questions to guide future research on customer-level strategies in a global digital context.     

The social significance of AI in retail on customer experience and shopping practices

This paper draws on practice-informed, ethnographic research to develop an understanding of the novel social consequences and opportunities afforded from consumers' interactions with AI digital humans as part of the in-store shopping experience. We reveal and interrogate consumers’ experiences with AI digital humans in an exploratory study undertaken during the launch phase of an in-store kiosk digital store greeter in a flagship store of a large national technology and appliance chain. Our findings contribute to understanding the social significance of AI in retail on customer experience (CX) and the managerial implications of consumers interactions with AI digital humans are described and discussed.     

Understanding Programmatic Creative: The Role of AI

In this article, we expand the concept of programmatic advertising to include programmatic creative as a vital component. While artificial intelligence (AI) has already automated the media buying process, the advertising creative process still requires extensive human efforts. Such discrepancy calls for AI to transform the advertising creative process. We provide a framework for understanding and investigating programmatic creative by drawing evidence from the advertising industry in China. We specifically discuss how big data and machine learning algorithms underpin programmatic advertising. We argue that AI will integrate programmatic buying and programmatic creative in the future. We also discuss the technological, regulatory, and legal challenges faced by programmatic creative. We argue that new theories and methods are needed to conduct research in this area and provide guidance for the advertising industry.     

Customers’ service-related engagement,experience, and behavioral intent: Moderating role of age

Though customer engagement (CE) and experience are important research priorities, empirically derived insight into these concepts and their associations remains scarce. We therefore investigate the impact of CE on experience and behavioral intent, which we explore in the tourism context. We also envisage customer age to moderate the proposed associations, thereby further contributing to the CE literature. To investigate these issues, we collected data from key tourism-sites in Jammu/Kashmir, Northern India. Using structural equational modeling, we find that CE dimensions exert differential effects on customer experience. The findings also reveal a stronger, significant effect of younger customers' (up to 39) cognitive engagement on experience, whereas a negligible effect is attained for older customers. While the paths from affective/behavioral engagement to experience are significant across all age groups, the association's strength rises with customer age. Moreover, the effect of customer experience on behavioral intention intensifies as customers get older. We conclude by outlining key implications that arise from our analyses, followed by avenues for further research.     

The impact of new technologies on customer satisfaction and business to business customer relationships: Evidence from the soft drinks industry

Sales technology has been touted as a primary tool for enhancing customer relationship management (CRM) and thus improving overall customer satisfaction. The marketing, IT, and practitioner literatures make numerous references to the impact of CRM on business orientation and performance. However, according to Richard et al. (2007), not many studies have investigated customer relationship technology adopted by a firm and the impact on relationship development. The aim of this paper is to examine the relative impact of new technologies on improving customer relations and overall customer satisfaction within the sales industry. New technologies have revolutionised the workplace and have become an integral part of organisations throughout the world (Forster, 2000). Driving this revolution are two powerful new forces, cyberspace and computing power. This article defines three key terms which include CRM, customer satisfaction and new technologies and evaluates the impact of these emerging technological developments on relationships and customer satisfaction in a sales force/customer context. Primary research was conducted using two focus groups consisting of nine participants from Britvic. The analysis reveals that technology is both beneficial and essential within the sales force industry for both the sales representative and their customers and provides an insight into the impact of technology on the sales force/customer relationship. The paper concludes with reference to new technologies and the future.     

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.

     

The influence of business strategy on new product activity: The role of market orientation

In this paper, we propose that business strategy influences new product activity both directly and indirectly via its influence on market orientation. Accordingly, we develop a framework linking firms' relative emphasis on cost leadership, product differentiation and focus strategies to firms' customer and competitor orientation as well as their new product development and introduction activity. We use this framework to develop a simultaneous equations model that is tested on survey data from 175 Dutch firms of varying size and across different industries in the manufacturing sector. The surprising findings are that a greater emphasis on a focus strategy results in a decreased emphasis on customer orientation and that competitor orientation has a negative direct influence on new product activity and an indirect positive effect via customer orientation. We discuss the implications of these findings for theory and practice.