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This study develops the idea of smart retailing, exemplified in innovative, technology-enriched retail services as part of service-oriented strategies. In particular, the purpose of this paper is to provide a new integrated framework to understand the emerging retail scenario based on the smart usage of technologies to improve retail service and develop innovation management strategies. This framework will provide a comprehensive understanding the basic forms of smart retailing as the current competitive scenario.

As a viewpoint, this paper employs an interdisciplinary approach, drawing upon the actual challenges in retailing, to propose a new perspective, the smart retailing one, to describe the new competitive scenario and formulates an emerging research agenda.

The present paper contributes to research on innovation and technology management for retailing by examining the key dimensions of smart retailing, which aims to enhancing retail service quality and retailers’ performance.

The paper clearly explains how current retailing is moving to a smart perspective, and how retail management should be adapted to successfully perform in the current service-dominant logic scenario, as consequence of the increasing consumer involvement in service co-production and the rapid growth of digital technologies.

• Explain what a smart consumer is through Generation Z views

• Understanding the nature of decision-making process with the use of smart technologies

Explain what a smart consumer is through Generation Z views

Understanding the nature of decision-making process with the use of smart technologies

Previous studies have considered customers' psychological responses to intelligent retail technology adoption, but have not considered how technology integration systems could promote the relationship between retailers and consumers. Based on the Stimulus–Organism–Response framework, this paper proposes a customer engagement model in a fully intelligent retail environment. The concept of the quality of intelligent experience is constructed from the perspective of customer experience, and the effect of the mechanism of smart retail on the customer engagement relationship is discussed.

Using two surveys, this study analyzes 201 (in study 1) and 321 (in study 2) questionnaires by using structural equation model in partial least square software.

The analysis shows that the human–machine interaction, intelligent systems and the product content of the quality intelligent experience significantly impact customer engagement on smart retail.

This research was designed for general retail products, without distinguishing between different product types. Thus, it did not consider the moderating effect of product types.

The findings enrich the intelligent retail technology field and provide operable guidance to help smart retailers improve customer relations.

This paper proposes a customer engagement model to describe how technology integration systems promote the relationship between retailers and consumers.

Recently, smart retail technology has emerged as an innovative technology that can improve consumer motivation and behavior in smart stores. Although prior studies have investigated factors influencing the adoption of smart retail technology, to the authors’ knowledge, no previous work has investigated the determinants of purchase intentions. The ultimate goal for retailers should be shopping, not technology adoption. However, traditional brick-and-mortar stores and theories focus on investing in utilitarian factors to attract customers. This study proposes that hedonic motivation should also play an important role, as new technologies may arouse customer curiosity and increase pleasant experiences. Therefore, the purpose of this study is to explore utilitarian and hedonic motivations that promote customers' purchase; intentions in smart stores. Specifically, the authors address the research questions: (1) What are the constituents of utilitarian motivation? (2) What are the constituents of hedonic motivation? (3) What are the factors that influence customers' purchase intentions? By answering the questions, the findings help retailers understand how to motivate customers to make purchases in smart stores.

To investigate consumer motivation and purchase intentions, the customers who made purchases in smart stores were invited to participate in the questionnaire survey. This study collected 307 data in smart retail settings. Partial least squares (PLS) software was used to assess the reliability, validity and the paths and significance of all hypotheses.

The results show that perceived ease of use directly and indirectly influences purchase intentions through utilitarian and hedonic motivations. Utilitarian motivation is a formative second-order construct comprised of merchandise price, merchandise quality, location convenience, speed of shopping and product recommendation. Hedonic motivation is a reflective second-order construct composed of control, curiosity, joy, focused immersion and temporal dissociation. The findings provide insights into the successful implementation of smart retail technology and offer retailers to better understand consumer motivation and purchase intentions in smart stores.

This study is the first to examine how consumer motivation influences purchase intentions in smart stores. This study posits and verifies the extended hedonic system acceptance model (HSAM) to explain consumer motivation for shopping in smart retail settings. This study also models the original first-order utilitarian and hedonic constructs as second-order formative and reflective constructs, respectively. Utilitarian motivation regarding functional benefits is developed based on the 5Ps of marketing and situational factors, while hedonic motivation regarding pleasant experiences is proposed based on cognitive absorption.

This study aims to examine the influence of the derived attributes of embedded artificial intelligence-mobile smart speech recognition (AI-MSSR) technology, namely perceived usefulness, perceived ease of use (PEOU) and perceived enjoyment (PE) on consumer purchase intention (PI) through the chain relationships of attitudes to AI and consumer smart experience, with the moderating effect of consumer innovativeness and Generation (Gen) X and Gen Y in fashion retail.

The study employed a quantitative survey strategy, drawing a sample of 836 respondents from Sri Lanka and India representing Gen X and Gen Y. The data analysis was carried out using smart partial least squares structural equation modelling (PLS-SEM).

The findings show a positive relationship between the perceived attributes of MSSR and consumer PI via attitudes towards AI (AAI) and smart consumer experiences. In addition, consumer innovativeness and Generations X and Y have a moderating impact on the aforementioned relationship. The theoretical and managerial implications of the study are discussed with a note on the research limitations and further research directions.

To multiply the effects of embedded AI-MSSR and consumer PI in fashion retail marketing, managers can develop strategies that strengthen the links between awareness, knowledge of the derived attributes of embedded AI-MSSR and PI by encouraging innovative consumers, especially Gen Y consumers, to engage with embedded AI-MSSR.

This study advances the literature on embedded AI-MSSR and consumer PI in fashion retail marketing by providing an integrated view of the technology acceptance model (TAM), the diffusion of innovation (DOI) theory and the generational cohort perspective in predicting PI.

This study aims to identify the antecedent factors influencing consumer attitudes and patronage intentions toward an intelligent unmanned convenience store (IUCVS) in Taiwan. The IUCVS is a new smart service that offers customers a novel shopping experience, given that it avoids queues and physical contacts with cashiers. However, studies discussing IUCVS remain scant owing to its brief history.

This research develops a synergistic model combining original unified theory of acceptance and use of technology (UTAUT) constructs with perceived risk and value to test differences between unexperienced and experienced customers’ attitudes and patronage intentions toward IUCVSs. Data collected from 268 experienced and 156 unexperienced consumers were tested against the proposed research model using partial least squares (PLS) structural equation modeling and multi-group analysis (PLS-MGA).

In line with expectations, three UTAUT variables (i.e. performance, effort expectancy and social influence) and perceived value significantly and positively influence consumer attitudes toward IUCVSs. This research confirms the significant and negative direct effect of perceived risk on consumers’ patronage intentions toward IUCVSs. Furthermore, the PLS-MGA results unveil that a significant difference exist in the effects of perceived convenience value on attitudes toward IUCVS between consumers who had experience of using self-service machines and those who have not.

This research successfully fills the research gap by offering a synergistic model for evaluating consumers’ attitudes and patronage intentions toward a new smart service. Several important theoretical and practical implications are provided to help retail managers develop service strategies.

This research aims to propose the development of a model that identifies, in essential services, the determining factors affecting the technological advances offered by different smart technologies in supermarket retail channels that influence citizens' quality of life, amidst the coronavirus disease 2019 (COVID-19) pandemic.

The data were collected using a cross-sectional questionnaire survey (n = 469). The authors applied the structural equation modeling (SEM) technique to test the hypotheses, along with the partial least squares (PLS) method for estimating latent variables and combining with the necessary condition analysis (NCA) method.

According to the results of the NCA method, the results were adequate, and more attention should be paid to the quality of life construct after finding the bottleneck point of 50%. In this sense, adaptive resilience was characterized as the main necessary predictor construct for quality of life. In addition, Generation Z and Millennials have the highest frequency of use in all smart technologies, with “assisted purchase” being the most widely used.

Finally, the effect of the pandemic changed the consumption routine with supermarkets, not being a mere option but a necessity in the context of a smart city.

As a result, the proposed model was consistent, showing that all direct and indirect SEM paths were validated, highlighting data security and privacy and resilience issues. In addition, the NCA method complemented the procedures performed in the SEM phase.

In this digitalization age, smart technologies are on the cusp of changing all business sector including retailing. Today’s consumers desire to shop in a smart store where the physical products on display are connected to the internet world. This study aims to propose a model for investigating Generation Z (Gen-Z) consumers’ expectations towards the smart retail technology (SRT) in the Malaysian context through the application of the stimulus-organism-response framework.

By using a purposive sampling technique, a self-administered questionnaire was conducted. A total of 220 usable responses were collected from Gen-Z consumers who had experience in using SRT. The data were analysed using partial least square structural equation modelling (PLS-SEM).

This study underscores the role of perceived usefulness, perceived enjoyment and perceived value on Gen-Z consumers’ attitude and word-of-mouth (WOM). Expectations on usefulness, enjoyment and value provide some ideas on Gen-Z consumers’ value who put a high emphasis on functional value, fun value and value for money when adopting the SRT. On the other hand, the result also demonstrates the mediating role of consumers’ attitude in connecting perceived value and perceived enjoyment on WOM towards SRT.

The findings of this study provide beneficial insights for a retailer who desire to pursue a smart-related acquisition strategy. Retailers are recommended to embrace on the following three key features: innovative solution (high usefulness), reliable benefits (high perceived value) and fun experience (high enjoyment) to provide Gen-Z consumers with compelling experiences. Additionally, retailers are suggested to acknowledge the importance of managing consumers’ attitude in driving positive WOM evaluations.

This paper responds to Ting et al.’s (2018) call in uncovering values and lifestyles of generation in details. In Malaysia, retailers ought to incorporate of the high level of usefulness, value and enjoyment when designing their SRT to constitute the Gen-Z consumers’ expectations. Moreover, similar to Ting’s conclusion, this cohort members are very much into technologies and they tend to embrace SRT in their lifestyles.

Digitalization is revolutionizing the retail sector as today's consumers prefer a seamlessly integrated, fluid and irritation-free shopping experience enhanced with artificial intelligence (AI)-powered technologies. Literature highlights gaps in the understanding of the shopping experience in an omnichannel context, involving aesthetic, cognitive and affective experience dimensions. This research highlights the direct effects and the mechanism triggered in the presence of such device.

A sample of 259 consumers was interviewed at the point of sale. Data have been collected after a shopping experience in two concept stores belonging to the same fashion brand: (1) not equipped with AI-powered technology and (2) equipped with these tools. The measurement scales were validated through ANCOVA analysis and causal relationship analysis with structural equation modeling.

The results show that the presence of an in-store AI-powered technologies in a connected store generates a higher aesthetic reaction when visiting the store, a higher absorption when shopping through the flow and a higher intent to purchase. The authors further investigate the underlying mechanism triggered by the presence of this technology, which enables the authors to outline their consequences regarding purchase intention.

The study, conducted within an actual connected store in France, explores the impact of AI technology in connected retail environments on consumer responses. It is an early research in this field, shedding light on a rarely explored area. The authors’ research addresses a significant gap, providing insights into AI-driven retail experiences.

This chapter provides an overview of technology management to support retailing, before and during the COVID-19 pandemic. Specifically, it focuses on the technologies developed and in use before the pandemic, the ones further developed as response to the pandemic, while the final part of the chapter proposes a new technology implementation process (cycle) to support retailers in introducing new technology. In particular, the process in based on seven main activities: (1) Technology need recognition; (2) Technology screening; (3) Initial development and testing; (4) Business analysis; (5) Technology development; (6) Market acceptance testing; and (7) Technology adoption, while monitoring and learning actions should occur constantly throughout the process to evaluate the benefit of the technology at each stage (or to discard for further investment).