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This research explores anticipated long-term change in the retail and services marketplace, directly arising as a result of the Covid-19 pandemic.

A series of 20 in-depth interviews were conducted with retail and service stakeholders (executives, suppliers and thought-leaders) from across Asia-Pacific (New Zealand and Australia), the United Kingdom, Europe and the United States.

We identify six guiding principles for long-term change in the retail and services sector required to guide future business development and practice, including embedding new ways of working, rethinking the role and purpose of physical space, prioritizing digital elements, integrating employees in community, building agile supply and planning for future turbulence.

The Covid-19 pandemic is different from prior disruptive experiences in that it was a sudden shock to business and was collectively experienced by firms, workers and consumers across the globe. This research provides a view of decision-makers’ sensemaking and anticipated changes impacting the future retail and services marketplace.

Although saving for retirement is critical, many people are reluctant to contribute money now in anticipation of a future event, namely their retirement. This study aims to investigate the interaction effect between message frame and message appeal on individual preparedness for retirement, specifically in terms of an individual’s willingness to contribute to retirement savings.

Across three studies, the authors show that message frames with a gain-orientation are more persuasive when self-oriented rather than oriented towards socially distant others. In Study 1, the authors use a 2 (gain-loss message frame) × 3 (self, close-other, distant-other message appeal) experimental design with ad copy manipulating the scenarios. In Study 2, the authors replicate and extend Study 1 to validate the findings. In Study 3, the authors overlay life-stage to determine how consumers differ in terms of their willingness to contribute to retirement.

Across the studies the authors find that message frame and message appeal influence consumer preparedness for retirement. The authors investigate these effects in terms of willingness to contribute to retirement savings (Studies 1–3) as well as satisfaction with retirement savings, anticipated retirement enjoyment, positive emotions and negative emotions (Studies 1 and 2).

While a substantive amount of research has been devoted to retirement planning, gaps remain as to the efficacy of communication message strategies on influencing retirement preparedness. This research seeks to contribute towards this literature stream by uncovering the ways in which individuals can be encouraged to contribute towards retirement planning.

This paper aims to examine how consumers respond to social media influencers that are created through artificial intelligence (AI) and compares effects to traditional (human) influencers.

Across two empirical studies, the authors examine the efficacy of AI social media influencers. With Study 1, the authors establish baseline effects for AI influencers and investigate how social-psychological distance impacts consumer perceptions. The authors also investigate the role of an influencer’s agency – being autonomous or externally managed – to test the boundaries of the results and determine the interactive effects between influencer type and influencer agency. Study 2 acts as an extension and validation of Study 1, whereby the authors provide generalisability and overlay the role of need for uniqueness as a moderated mediator.

The authors show that there are similarities and differences in the ways in which consumers view AI and human influencers. Importantly, the authors find no difference in terms of intention to follow or personalisation. This suggests that consumers are equally open to follow an AI or human influencer, and they perceive the level of personalisation provided by either influencer type as similar. Furthermore, while an AI influencer is generally perceived as having lower source trust, they are more likely to evoke word-of-mouth intentions. In understanding these effects, the authors show that social distance mediates the relationship between influencer type and the outcomes the authors investigate. Results also show that AI influencers can have a greater effect on consumers who have a high need for uniqueness. Finally, the authors find that a lack of influencer agency has a detrimental effect.

The studies investigate consumers’ general response to AI influencers within the context of Instagram, however, future research might examine consumers’ response to posts promoting specific products across a variety of category contexts and within different social media platforms.

The authors find that in some ways, an AI influencer can be as effective as a human influencer. Indeed, the authors suggest that there may be a spill-over effect from consumer experiences with other AI recommendation systems, meaning that consumers are open to AI influencer recommendations. However, the authors find consistent evidence that AI influencers are trusted less than traditional influencers, hence the authors caution brands from rushing to replace human influencers with their AI counterparts.

This paper offers novel insight into the increasingly prominent phenomenon of the AI influencer. Specifically, it takes initial steps towards developing understanding as to how consumers respond to AI influencers and contrast these effects with human influencers.

Brands are increasingly considering the use of chatbots to supplement, or even replace, humans in service interactions. Like humans, chatbots can follow certain service scripts in their encounters, which can subsequently determine the customer experience. Service scripts are verbal prescriptions that seek to standardize customer service interactions. However, while the role of service scripts is well documented, despite the increasing use of chatbots as a service mechanism, less is known about the effect, on consumers, of different service scripts presented during chatbot service encounters.

An experimental scenario was developed to test the research hypotheses. Respondents were randomly allocated to scenarios representing a 2 (service interaction: human, chatbot) × 2 (service script: education, entertainment) design. A total of 262 US consumers constituted the final sample for the study.

The findings indicate that when employing an education script, a significant positive effect occurs for human service agents (compared to chatbots) in terms of both satisfaction and purchase intention. These effects are fully mediated by emotion and rapport, showing that the bonds developed through the close proximity to a human service agent elicit emotion and develop rapport, which in turn influence service outcomes. However, this result is present only when an educational script is used.

This paper contributes to the emerging service marketing literature on the use of digital services, in particular chatbots, in service interactions. We show that differences occur in key outcomes dependent on the type of service script employed (education or entertainment). For managers, this study indicates that chatbot interactions can be tailored (in script delivered) in order to maximize emotion and rapport and subsequently consumer purchase intention and satisfaction.

This paper aims to develop a novel and generalizable machine-learning based method of measuring established marketing constructs through passive analysis of consumer-generated textual data. The authors term this method scale-directed text analysis.

The method first develops a dictionary of words related to specific dimensions of a construct that is used to assess textual data from any source for a specific meaning. The method explicitly recognizes both specific words and the strength of their underlying sentiment.

Results calculated using this new approach are statistically equivalent to responses to traditional marketing scale items. These results demonstrate the validity of the authors’ methodology and show its potential to complement traditional survey approaches to assessing marketing constructs.

The method we outline relies on machine learning and thus requires either large volumes of text or a large number of cases. Results are reliable only at the aggregate level.

The method detail provides a means of less intrusive data collection such as through scraped social media postings. Alternatively, it also provides a means of analyzing data collected through more naturalistic methods such as open-response forms or even spoken language, both likely to increase response rates.

Scale-directed text analysis goes beyond traditional methods of conducting simple sentiment analysis and word frequency or percentage counts. It combines the richness of traditional textual and sentiment analysis with the theoretical structure and analytical rigor provided by traditional marketing scales, all in an automatic process.

For consumers, cross-channel behaviour is increasingly prevalent. Such behaviour involves consumers actively engaging in (and deriving benefit) from one channel during a product search but switching to another channel when making a purchase. Drawing on multi-attribute utility theory, this study proposes a cross-channel behaviour typology consisting of three key aspects: channel choice behaviour, functional and economic outcomes and consumer-specific psychographic and demographic variables.

Segmentation analysis conducted via latent class analysis (LCA) was performed on a sample of 400 US consumers collected via an online survey.

Cross-channel behaviour is not always intentional. We identify a specific segment of consumers that most often engage in unplanned, rather than intentional, cross-channel switching. We find that of all shoppers that engage in cross-channel behaviour, a fifth (20%) are forced to switch channels at the point of purchase.

Cross-channel behaviour can be mitigated by retailers via a deep understanding of the driving factors of different configurations of showrooming and webrooming.

In contrast with existing conceptualisations, this study suggests that cross-channel behaviour often stems from consumers being “forced” by factors outside of their control, but within the retailers' control. This research presents a nuanced approach to decompose consumer cross-channel behaviour from the consumer perspective as planned, forced or opportunistic.

This paper investigates the degree to which self-selection explains the apparent higher purchase value of research shoppers.

An online survey was administered to 594 retail shoppers. The purchase value of research shoppers and single-channel shoppers was compared before and after propensity score matching to account for self-selection effects.

Prior to matching, research shoppers spend significantly more than single-channel shoppers. This difference persists after accounting for self-selection but is reduced by 25%. The impact of self-selection differs across product categories and channels, with the online channel most likely to lead to higher purchase value.

The findings build on existing literature on the value of omni-channel retail strategies and provide insights for retailers to determine the likely impact of encouraging research shopping among their customers.

The research provides important insights into the role that self-selection plays in the value of multi-channel shoppers, and the likely value to retailers of omni-channel strategies.

Periods of economic downturn can severely affect the performance of firms in general, and retailers in particular. However, all retailers are not equally affected by recessionary periods, with some viewing this time as an opportunity to invest and establish competitive advantage and others cutting back and waiting for the recession to pass. In the light of the current economic downturn, the purpose of this paper is to investigate retailers' strategic response to the downturn, and assess how these responses differ across different organisations in terms of size, geographic location, and sector of operation.

A total of 50 (25 Australian, 25 New Zealand) managers associated with the retail sector (ranging from chief executive officers to store owners) were invited to participate in face‐to‐face interviews, resulting in 28 in‐depth interviews (15 Australian, 13 New Zealand) across a wide range of retail categories. These exploratory data were combined with a review of the literature to explore retailer responses to the current economic downturn.

This paper suggests that there are growth opportunities for retailers during times of economic downturn. Specifically, this paper suggests that large retailers see this as an opportunity to invest in green and sustainable business practices as a means to aid in the recovery of the recession in terms of reducing costs and beyond in terms of developing competitive advantage. Other strategies include: personalised offerings, differentiation via service, realignment of offer to changing consumer value(s), and reduced costs and investment.

The focus of this paper is medium to large sized organisations and, as such, its generalisability to small retailers may be somewhat limited.

Past research reveals that not all the retailers are equally affected by recessionary periods. Given this, the value of this paper lies in providing insight into strategic response to an economic downturn, and providing a foundation for examining these strategies and their link to retail performance via quantitative research.

The purpose of this paper is to understand how consumers may be segmented with respect to their reactions to social network marketing.

Consumers are segmented on the basis of attitudes toward social network marketing and the association among psychological, economic, and socio-demographic covariates are explored using data from 883 consumers and latent-class analysis.

A total of five segments are identified – Passive, Talkers, Hesitant, Active, and Averse – along with significant covariates, such as information search, convenience, entertainment, age and gender that predict membership.

Evidence was found of two segments that are highly impacted by social network marketing in terms of brand engagement, purchase intention and WOM referral intention. The most engaged – the Active – representing approximately 10 percent, is most open to interacting with brands in social networks, likely to make a purchase as a result of the campaign, and likely to spread WOM. The second group – Talkers – representing 28 percent, is also high on all outcomes, but not impacted as greatly in terms of purchase intentions.

There is a sizable share of the market (38 percent) that can be positively impacted through social network marketing. This paper reinforces that it is problematic to collapse all users of social networks.

The paper offers a more nuanced understanding of how consumers engage with social media by focusing on how consumers engage with social network marketing and by employing three segmentation bases: brand engagement, purchase intention, and WOM.