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This study investigates augmented reality (AR) retailing and attempts to develop a profound understanding of consumer decision-making processes in AR-enabled e-retailing.

The study is grounded in rich informational cues and information processing mechanisms by incorporating the elaboration likelihood model (ELM) and trust transfer theory. This study employs a mixed analytic method that incorporates structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to provide a complete picture of individual information process mechanisms in AR retailing under the tenet of ELM.

The SEM analysis results confirm the relationships between the central and peripheral route factors, information processing outcomes and eventual behavioral intentions. Moreover, all configurations revealed by the fsQCA include both central and peripheral factors. Hence, the dual routes proposed in the ELM are verified by using two distinct analytical approaches.

This study is pioneering in validating and contextualizing ELM theory in AR retailing. In addition, this study offers a methodological paradigm by demonstrating the application of multi-analysis in exploring consumers’ information process mechanisms in AR retailing, which offers a holistic and comprehensive view to understand consumers’ decision-making mechanisms.

Drawing upon the social network theory (SNT), the aim of this study is to examine the impact of value co-creation (VCC) on retailer loyalty (RL) and omni-channel shopping frequency (OCF) from the consumer perspective.

Grounded on SNT, the research focuses on VCC in omni-channel retailing. A paper-based questionnaire was used to examine the role of VCC on OCF and RL, and hypotheses were tested using structural equation modeling (SEM).

The findings supported the impact of VCC on RL, and in turn, the impact of RL on OCF. However, the results of the analysis reveal that RL has no mediator role between VCC and OCF.

The study shows the increasing role of VCC on RL, and the important role of RL on OCF. The findings reveal that RL has a significant role for VCC and OCF separately but does not have a mediator role between VCC and OCF. Moreover, the study extends the knowledge on VCC in omni-channel retailing.

The new research model, which was hypothesized from SNT, was validated. In the omni-channel retailing context, the study revealed the consumer perspective on VCC, and also the linkages between VCC, RL and OCF.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.

This study aims to evaluate the impact of product complexity, product involvement and product diagnosticity on shaping webrooming behavior in emerging fashion retailing, with product knowledge acting as a moderator and information processing, and uncertainty reduction theory contributing as the theoretical foundation.

In total, 371 responses were collected from fashionable consumers who had recently purchased fashion products via a purposive sampling approach, and the data were analyzed using structural equation modeling and PROCESS macro.

The results illustrated that product complexity had a significant impact on product involvement and product diagnosticity, and consumer attitude. Attitude, in turn, had a favorable impact on webrooming behavior. Likewise, product diagnosticity and product involvement had a positive mediating association between product complexity and consumer attitude. Product knowledge significantly moderated the relationship between product complexity, product involvement, and consumer attitude, but it exhibited a negative moderation association between product complexity, product diagnosticity, and attitude.

This study represents a novel research endeavor, shedding light on webrooming from the perspective of product attributes in fashion retailing. It contributes to the growing body of literature on fashion marketing by analyzing the rapidly evolving phenomena of webrooming behavior within the multichannel context of the fashion industry.

This research delves into a comprehensive examination of Amazon’s Vendor Flex (VF) model, seeking to illuminate the intricacies of supply chain innovation through alliances between Amazon and its suppliers. Employing a multiple case study methodology, the study investigates the reduction of transaction costs, the establishment of strategic alliances for supply chain innovation and governance issues within these alliances.

A multiple case study methodology, incorporating personal interviews and triangulation with primary sources, was employed to unravel the dynamics of the VF model.

Results indicate that the VF model aligns with the reduction of transaction costs by leveraging Amazon’s specialized knowledge, although not necessarily through direct knowledge sharing. Amazon suppliers highlight competitive advantages gained through VF, showcasing efficient navigation of peak seasons and a focus on core activities with online retailing integration. The VF alliance represents a collaborative model where Amazon’s technological prowess enables a streamlined and innovative supply chain for online retailing, which resembles a vertical integration process.

This research underscores the potential of strategic alliances to drive innovation by incorporating industry-leading practices. The governance issues within the VF alliance reveal power imbalances, emphasizing the need for managers to govern dynamics, disclose information and build trust in large-scale alliances.

The New Zealand city of Christchurch provides a leading example of post-disaster rebuilding in a Central Business District (CBD) area. In its rebuilding programme, the city has given emphasis to the greening of hospitality and traditional retail space through a combination of development of shared pedestrian spaces (with traffic exclusion and calming) and the integration of greening within the streetscape design. This paper aims to assess whether the development of greened pedestrian areas leads to higher retail spending and, thus, retail rental rates.

This study uses pedestrian movement data collected from several CBD locations, as well as spending data on retail and hospitality, to assess relationships between pedestrian movements and spending. This study explores retail spending in greened pedestrian shared spaces, and explores how this differs from retail spending in traditional street areas within the Christchurch CBD.

Spending patterns are location-related, depending on the characteristics of pedestrian space in the selected area. Greened shared pedestrian areas have the highest spending per measured pedestrian for retail and hospitality, whereas traditional street areas have lower spending for retail and hospitality per measured pedestrian, demonstrating the benefits in redeveloped central city areas.

The scope of smart data continues to develop as a research area within urban studies to develop an open and connected city. This research demonstrates the use of innovative technologies for data collection, use and sharing. The results support commercial benefits of greening and pedestrianisation of retail and hospitality areas for CBDs and providing an example for other cities to follow.

In today’s complex and globalised business setting, Australian clothing retailers are ever more vulnerable to supply chain disruptions. Supply chain resilience reduces the effect of a disruption, which permits the members of a supply chain to respond aptly to disruptive events. This study, hence, aims to uncover the details of how the small and medium-sized enterprises (SMEs) of clothing retailers in Australia build supply chain resilience and what are the major issues experienced by the SMEs while building resilience.

This study is carried out using a descriptive qualitative research design, and data are collected from semi-structured interviews with key informants from managerial levels within the Australian clothing retailers’ businesses.

This study identifies five enablers, namely, collaboration, multi-sourcing, visibility, flexibility and information systems, that the SMEs of clothing retailers mostly consider to achieve resilience in the supply chain. This study also finds that SMEs’ capabilities, cost and financing, lack of managerial autonomy and the inability to create redundancy are the key impediments hindering SMEs from attaining the expected level of resilience.

To the best of the author’s knowledge, this study contributes to the body of knowledge by being one of the first empirical studies to explore the SMEs of clothing retailers’ supply chain resilience in the Australian business context, which can add valuable insights for academics and practitioners in guiding supply chain design decisions for the SMEs in other sectors.

This research examines the effects of physical proximity (close vs distant) of retailers’ private label brands (PLBs) relative to national brands (NBs) and brand display orientation (horizontal [brands occupy the same shelf] vs vertical [brands occupy different shelves]) on consumers’ PLB quality perceptions and PLB evaluations.

Two experiments involving real brands in different product categories tested the hypotheses.

A PLB positioned close (vs distant) to a NB is evaluated more favorably and this effect is mediated by increased PLB quality perceptions, but only in a horizontal brand display. In a vertical brand display, a PLB positioned close (vs distant) to a NB is evaluated less favorably and this effect is mediated by decreased PLB quality perceptions.

The findings suggest that to enhance consumers’ PLB quality perceptions and evaluations, PLBs be positioned next to (rather than on separate shelves) and close to (rather than distant from) NBs in the same product category.

Although the literature suggests that the best shelf position for PLBs is close to NBs, there is a lack of empirical research on the effects of relative shelf positioning on consumers’ quality perceptions and subsequent PLB evaluations. This research finds that both physical proximity and brand display orientation play an important role.

The rise of artificial intelligence (AI) applications has driven enterprises to provide many intelligent services to consumers. For instance, customers can use chatbots to make relevant inquiries and seek solutions to their problems. Despite the development of customer service chatbots years ago, they require significant improvements for market recognition. Many customers have reported negative experiences with customer service chatbots, contributing to resistance toward their use. Therefore, this study adopts the innovation resistance theory (IRT) perspective to understand customers’ resistance to using chatbots. It aims to integrate customers’ negative emotions into a predictive behavior model and examine users’ functional and psychological barriers.

In this study, we collected data from 419 valid individuals and used structural equation modeling to analyze the relationships between resistance factors and negative emotions.

The results confirmed that barrier factors affect negative emotions and amplify chatbot resistance influence. We discovered that value and risk barriers directly influence consumer use. Moreover, both functional and psychological barriers positively impact negative emotions.

This study adopts the innovation resistance theory perspective to understand customer resistance to using chatbots, integrates customer negative emotions to construct a predictive behavior model and explores users’ functional and psychological barriers. It can help in developing online customer service chatbots for e-commerce.

Amidst escalating sustainability challenges, product and brand managers face a pressing need to foster responsible consumption and marketing strategies. Guided by the theory of planned behavior, this paper aims to explore consumers’ motivation to purchase second-hand clothing, a type of product that contributes to Sustainable Development Goal (SDG) 12 on Responsible Consumption and Production by democratizing the brand, extending the life-cycle of the product, promoting a circular economy, while reducing economic costs for consumers and environmental costs for companies.

A two-stage study was conducted: 20 consumers were initially interviewed to identify the salient beliefs about second-hand clothing, and following that, a survey was conducted with 449 consumers to statistically analyze consumers’ motivation to purchase second-hand clothing. The data were analyzed using partial least squares-structural equation modeling (PLS-SEM) and necessary condition analysis (NCA).

From a “should-have” perspective (PLS-SEM), the study reveals that behavioral beliefs, injunctive normative beliefs, descriptive normative beliefs and control beliefs positively shape attitudes, injunctive norms, descriptive norms and perceived behavioral control toward second-hand clothing, whereas attitudes, injunctive norms, moral norms and perceived behavioral control positively influence consumers’ purchases of second-hand clothing. From a “must-have” perspective (NCA), the study shows that behavioral beliefs, injunctive normative beliefs and descriptive normative beliefs are necessary conditions to positively shape attitudes, injunctive norms and descriptive norms toward second-hand clothing, whereas attitudes, injunctive norms and perceived behavioral control are necessary conditions to stimulate second-hand clothing purchases.

The study offers a deep dive into consumers’ motivation to purchase second-hand clothing using a multimethod approach that enables not only the elicitation of salient beliefs (through interviews) but also the empirical examination of these beliefs alongside varying subjective norms in motivating consumers to purchase second-hand clothing (via survey). Given that beliefs are deeply rooted, the rigorous unfolding and validation of consumers’ beliefs about second-hand clothing, including the “should-haves” versus the “must-haves,” provide finer-grained insights that product and brand managers can strategically use to encourage consumers to purchase second-hand clothing.