Search results

The transition to omnichannel retail is the recognized future of retail, which uses digital technologies (e.g. augmented reality shopping assistants) to enhance the customer shopping experience. However, retailers struggle with the implementation of such technologies in brick-and-mortar stores. Against this background, the present study investigates the impact of a smartphone-based augmented reality shopping assistant application, which uses personalized recommendations and explainable artificial intelligence features on customer shopping experiences.

The authors follow a design science research approach to develop a shopping assistant application artifact, evaluated by means of an online experiment (n = 252), providing both qualitative and quantitative data.

Results indicate a positive impact of the augmented reality shopping assistant application on customers' perception of brick-and-mortar shopping experiences. Based on the empirical insights this study also identifies possible improvements of the artifact.

This study's assessment is limited to an online evaluation approach. Therefore, future studies should test actual usage of the technology in brick-and-mortar stores. Contrary to the suggestions of established theories (i.e. technology acceptance model, uses and gratification theory), this study shows that an increase of shopping experience does not always convert into an increase in the intention to purchase or to visit a brick-and-mortar store. Additionally, this study provides novel design principles and ideas for crafting augmented reality shopping assistant applications that can be used by future researchers to create advanced versions of such applications.

This paper demonstrates that a shopping assistant artifact provides a good opportunity to enhance users' shopping experience on their path-to-purchase, as it can support customers by providing rich information (e.g. explainable recommendations) for decision-making along the customer shopping journey.

This paper shows that smartphone-based augmented reality shopping assistant applications have the potential to increase the competitive power of brick-and-mortar retailers.

Artificial intelligence (AI) is shifting the way of how customers interact with organisations by blending both physical and digital environments, thereby creating a new paradigm of customer-organisation relationship. The new relationship boundary driven by AI principally challenges as well as creates opportunities for relationship marketing theories and practices. The main objective of this chapter is to present a framework named ‘physical-digital space’ to demonstrate how AI can merge the physical and digital world. To broaden the understanding, this chapter uses the lens of customer experience in relationship marketing. The framework extends the existing understanding and provides managerial implications on how an organisation can develop strategies so that a customer consciously or subconsciously develops a positive relationship with the organisation.

Traditional sets of attributes characterizing shopping centres need to be updated to relate to new specific consumers' needs and choices, to ensure the survival of shopping centres. To this end, this chapter revisits shopping centres’ attributes in the light of consumers' choices of actual centres, taking into account the recent increasing role of technologies, leisure activities and changes in consumer behaviour. In doing so, we aim to improve perceptions of modernity and help to regenerate (or at least mitigate the decline of) shopping centres. Specifically, the new set of attributes include appearance (external appearance), convenience, entertainment and leisure activities, memorable experiences, green place and policy, image (modern image), price, service, size and technology.

Purpose: The purpose of this chapter is to review and critically evaluate robots, artificial intelligence and service automation (RAISA) applications in the restaurant industry to educate professors, graduate students, and industry professionals.

Design/methodology/approach: This chapter is a survey of applications of RAISA in restaurants. The chapter is based on the review of professional and peer-reviewed academic literature, and the industry insight section was prepared based on a 50-minute interview with Mr. Juan Higueros, Chief Operations Officer of Bear Robotics.

Findings: Various case studies presented in this chapter illustrate numerous possibilities for automation: from automating a specific function to complete automation of the front of the house (e.g., Eatsa) or back of the house (e.g., Spyce robotic kitchen). The restaurant industry has already adopted chatbots; voice-activated and biometric technologies; robots as hosts, food runners, chefs, and bartenders; tableside ordering; conveyors; and robotic food delivery.

Practical implications: The chapter presents professors and students with a detailed overview of RAISA in the restaurant industry that will be useful for educational and research purposes. Restaurant owners and managers may also benefit from reading this chapter as they will learn about the current state of technology and opportunities for RAISA implementation.

Originality/value: To the best of the authors’ knowledge, this chapter presents the first systematic and in-depth review of RAISA technologies in the restaurant industry.

This study investigates the determinants of effective human and artificial intelligence (AI) relationship-building strategies for brands. It explores the antecedents and consequences of consumers' interactant satisfaction with communication and identifies ways to enhance consumer purchase intention via AI chatbot promotion.

Microsoft Xiaoice served as the focal AI chatbot, and 331 valid samples were obtained. A two-stage structural equation modeling-artificial neural network approach was adopted to verify the proposed theoretical model.

Regarding the IQ (intelligence quotient) and EQ (emotional quotient) of AI chatbots, the multi-dimensional social support model helps explain consumers' interactant satisfaction with communication, which facilitates affective attachment and purchase intention. The results also show that chatbots should emphasize emotional and esteem social support more than informational support.

Brands should focus more on AI chatbots' emotional and empathetic responses than functional aspects when designing dialogue content for human–AI interactions. Well-designed AI chatbots can help marketers develop effective brand promotion strategies.

This research enriches the human–AI interaction literature by adopting a multi-dimensional social support theoretical lens that can enhance the interactant satisfaction with communication, affective attachment and purchase intention of AI chatbot users.

In an increasingly technology-driven retail landscape, retailers face the challenge of making the most effective decisions regarding the selection and use of innovative technology. Although previous research provides insights into the added value of technology, it does not directly guide retailers in overviewing and selecting technology that supports their sales operations. This chapter contributes to the field of retail technology studies by introducing a sales-oriented model intended to assist retailers in inventorying available technologies and making decisions regarding the selection and use of these technologies for their physical stores. The model uses an updated version of the seven steps of selling as a foundation and, in line with the resource life cycle, decision support system and self-service technology literature streams, proposes applying technology in such a way that it supports the stages of the retailer's sales process. This chapter concludes with a discussion of practical guidelines for applying the model.

This paper aims to study the development of radio frequency identification (RFID) technology through an analysis of patents filed with and issued by the US Patent and Trademark Office. A close analysis of these clusters reveals the patent development strategies of two competing factions of RFID technology developers. This paper provides an analysis of the patents along with insights into the contents of the patents held by these two groups.

The analysis is based on Intermec Technologies and the RFID Patent Pool, the two major players in this domain. The comparison of Intermec Technologies and RFID Patent Pool is conducted using meta‐data analysis and patent content clustering. The methodology and approach includes data pre‐processing, key phrase extraction using term frequency‐inverse document frequency, ontology construction, key phrase correlation measurement, patent technology clustering and patent document clustering. Clusters are derived using the K‐means approach and a prototype Legal Knowledge Management Platform.

The findings support a strong link between intellectual property and competitive advantage – specifically Intermec Technologies, which have not joined the RFID Patent Pool. The patent search results show that Intermec Technologies hold basic RFID patents in the early stages of technology development, which has placed the company in a dominant position.

The features of each cluster clearly depict the niches and specialties of companies and provide a historical framework of RFID technology development.

The RFID patent analysis shows that if a company holds crucial patents in the early stages of a developing technology which relate to the fundamental key aspects of the technology, then the company will be more likely to maintain a leading and dominant position in that industry segment (i.e. RFID in this study).

This research uses patent content cluster analysis to explain the rationale behind an alliance strategy decision.

The main objective of this chapter is broadening the understanding of anthropomorphic artificial intelligence (AI) (e.g. avatars, humanoid robots, chatbots) in both physical and digital environments. The chapter strives to demonstrate how organisations can curate relationship marketing and enhance customer experience by employing anthropomorphic AI. To achieve this, the chapter extends existing understanding in three ways. First, it explains the interconnectivity between relationship marketing and customer experience. Second, it presents anthropomorphic AI along with its different characteristics and technologies. Third, it offers some real-life uses cases and examples of such AI drawing from practical insights into five selected industries. Overall, the chapter provides some food of thoughts concerning the successful application and deployment of anthropomorphic AI in marketing practices.

This study aims to examine the positive impact of showrooming on the fashion retail business by examining the interrelationship between deal-seeking on mobile devices and digital coupon redemption intention on mobile shopping intention.

Purposive sampling was used to obtain data from 496 fashion apparel customers using the database of an online survey collection platform. Stimulus organism response (S-O-R) theory was used to examine the influence of showrooming on showroomers' mobile shopping intentions.

The findings suggest price consciousness is negatively related to showrooming and product involvement is positively related. In addition, showrooming affects the intention to redeem digital coupons and mobile deal-seeking. The intention to redeem digital coupons boosted mobile deal-seeking behavior. The impact of mobile deal-seeking on showroomers' mobile purchase intention is significant.

This research focused on fashion product consumers and generalization of the findings may be limited. The literature on positive effect of showrooming phenomenon on brick-and-mortar stores are scarce further extensive research may provide substantial generalization.

This demonstrates how showroomers may be successfully enticed to make purchases on the Brick-and-Mortar (B&M) store's online channel.

This study provides insights on navigating the showroomers into online channel customers.