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The study’s objective is to measure the importance consumers attach to AI-based attributes, namely, chatbots, face recognition, virtual fitting room, smart parking and cashier-free station in retail stores. The study also examines the specific purpose of using these attributes for shopping.

A conjoint experiment was conducted using fractional factorial design. Consumers were given 14 profiles (AI attributes and its levels) to rank according to their visiting preferences.

The results revealed that the retail chatbot was considered the most important attribute, followed by face recognition, virtual fitting room, smart parking system and cashier-free station. Moreover, consumers prefer to use chatbots for in-store shopping assistance over alerts and updates, customer support and feedback. Similarly, consumers wish a face recognition facility for greetings while entering the store over other services. In addition, cluster analyses revealed that customer groups significantly differ in their preferences for AI-based attributes.

The study guides retail managers to invest in AI technologies to provide consumers with a technology-oriented shopping experience.

Our results provide an insight into the receptivity of AI technologies that consumers would like to experience in their favorite retail stores. The present study contributes to the literature by investigating consumer preferences for various AI technologies and their specific uses for shopping.

Omni-channel shopping affords consumers a variety of delivery options to receive products based on their preferred times and locations. By considering consumers' contributions (physical, social and attentive efforts) in co-creating delivery services, this study investigates their preferences for parcel delivery.

A scenario-based questionnaire survey is conducted for data collection in Singapore (n = 483). Furthermore, a multinomial logistic regression is performed to assess consumers' choice mode of delivery among five alternatives, that is attended home delivery, unattended home delivery, automated self-collection locker, attended pickup point and click-and-collect.

Compared to attended home delivery, consumers who choose the alternatives are found to be more willing to contribute physical effort but less interested in responding attentively to informational updates. Efforts required for social interactions discourage consumers from choosing attended deliveries, prompting unattended alternatives (e.g. home delivery and self-collection) as more attractive choices. Additionally, socio-demographic factors and product value also influence consumers' preferences.

This study contributes to the literature by integrating the theoretical concept of consumer logistics into omni-channel studies, providing a new approach to examining consumers' channel behaviour. With detailed profiling that links product value and consumers' socio-demographics to their choice mode of delivery, the authors create practical insight into the optimal design of omni-channel distribution systems that best harness consumers' voluntary contributions.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This study aims to examine the factors influencing consumers’ intentions to use QR code menus in the post-COVID-19 pandemic using the unified theory of acceptance and use of technology and rational choice theory as the theoretical foundations.

Using a survey method, 200 data are collected from consumers who had used QR code menus in the past. Partial least squares structural equation modelling is used to analyse the data.

Our findings show that performance expectancy, effort expectancy, social influence, habit and perceived privacy protection have a significant positive influence on intentions. However, facilitating conditions, hedonic motivation and perceived privacy risk do not have a significant influence on intentions.

This study further extends the work of previous studies by using the unified theory of acceptance and use of technology model, with additional two new predictors, namely perceived privacy protection and perceived privacy risk, to understand consumers’ intentions to use QR code menus.

Research on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined in prior studies, but current research has not gone far enough in examining how AI is framed on social media. This paper aims to fill this gap by examining how people frame the futures of work and intelligent machines when they post on social media.

We investigate public interpretations, assumptions and expectations, referring to framing expressed in social media conversations. We also coded the emotions and attitudes expressed in the text data. A corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments was analyzed using computer-aided textual analysis comprising a BERTopic model and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment.

Different interpretations, assumptions and expectations were found in the conversations. Three subframes were analyzed in detail under the overarching frame of the New World of Work: (1) general impacts of intelligent machines on society, (2) undertaking of tasks (augmentation and substitution) and (3) loss of jobs. The general attitude observed in conversations was slightly positive, and the most common emotion category was curiosity.

Findings from this research can uncover public needs and expectations regarding the future of work with intelligent machines. The findings may also help shape research directions about futures of work. Furthermore, firms, organizations or industries may employ framing methods to analyze customers’ or workers’ responses or even influence the responses. Another contribution of this work is the application of framing theory to interpreting how people conceptualize the future of work with intelligent machines.

Digitalisation is perceived as a new process that may add value to firms. Current theoretical understanding assumes it should be part of a firm's strategy to respond to multiple pressures in the business environment. This paper explores the occurrence of digitalisation in a rare context, that of the English agricultural industry in the United Kingdom, a place disproportionality filled with family firms. The general understanding of digitalisation in family firm settings remains embryonic. The authors' explorations make theoretical contributions to research at the intersection of rural entrepreneurship, family business and innovation.

Utilising a purposive, qualitative approach, primary data was collected from multiple interviews with 28 UK family farms, and secondary data from another 164. Interview transcripts were coded using NVivo, along with secondary data from reports, observations and websites.

The authors present empirical evidence illustrating how digitalisation manifests incrementally and radically in different types of family farms. The authors present a model that shows the areas of farming that have, and continue to be, digitalised. This increases analytical precision when identifying digitalisation activities that differ depending on the strategy to either scale or diversify. The authors propose that incremental digitalising occurs to a great extent during a scaling strategy, and that radical digitalising occurs to a smaller extent during diversification strategies in family farms.

This research uses a sample of family-run farms from the UK agricultural sector to explore nuanced elements of digitalisation. It should therefore be explored in other types of family firms located in different sectors and geographies.

This research is important because family farms are under increasing pressure and have limited financial resources to deal with the digitalisation agenda. Therefore, empirical evidence helps other farms in similar situations. The authors found digitalisation investments, that tend to be capital intensive, only matter for scalers and less so for diversifiers. Family farms can use the model presented as a tool to evaluate their farm. The tool helps them define what to do, and ideate the potential activities that might be digitalised, to feed into their wider strategy.

Family firms, in particular farms, are critical to many economies. The general consenses currently assumes all family firms should digitalise, yet the authors' evidence suggests that this is not the case. It is important to create policies that are sensitive to the needs of different types of businesses, in this case between family firm scalers and diversifiers, instead of simply incentivising digitalisation using a blanket approach usually by offering financial aid. Understanding how digitisation can support (or not) family firm resilience and growth in an effective and efficient manner can have significant benefit to individual firms, and across industries.

The proposed model extends theoretical understanding linking strategy, digitalisation activity and innovation in family farms. It shows that digitalisation is a key building block of scaling strategies, maximising digitalisation to increase efficiency. Yet, diversifying family farms minimise digitalisation, whereby they only digitalise a small amount of the farming activity. This empirical evidence contrasts with the wider narrative that farmers are slower at using new technology. This research found that some are slower because it does not align with their strategy. However, sometimes digitalisation aligns with their strategy during external changes, in which case the diversifiers are quick to act.

Perceived risk and benefits have been proposed as crucial elements in consumers’ technology adoption intention. Therefore, this study aims to use the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, with perceived risks and benefits, to explore how they affect consumers’ Buy-Now-Pay-Later (BNPL) behavioural intention.

An online survey was conducted among 537 users of BNPL. Partial least square structural equation modelling analysis was performed to examine the hypothesized relationships.

The study found that perceived benefits, performance expectancy, effort expectancy, habit and social influence increase consumers’ behavioural intention towards BNPL. In contrast, perceived risk reduces the consumer’s intention to use. Additionally, the study revealed that consumers’ intention towards BNPL services positively affects their usage.

The lack of information makes consumers hesitant to use BNPL. Therefore, marketers should optimize facilitating conditions, such as user-friendly interfaces, accessible resources and efficient customer support, which should be the business strategy of BNPL service providers to ensure a seamless user experience.

The research empirically investigates the effect of perceived benefits on consumer behavioural intention. This contribution is crucial because while there was speculation about benefits as a driving force to consumers’ adoption intention, there was a lack of sufficient empirical evidence to support it.

This paper aims to investigate the effect of gamified e-service quality (GE-SQ) on customer value co-creation, relationship quality and purchase intention in e-retailing.

A questionnaire was used to collect data from a sample of 549 e-retailing customers who participated in a gamified online service process. A structural equation modeling approach was adopted to analyze the model.

The empirical evidence confirmed that GE-SQ encouraged customer value co-creation behavior and relationship quality. Customer value co-creation and relationship quality led to higher purchase intention. Also, value co-creation and relationship quality partially mediated the relationship between GE-SQ and purchase intention.

The findings increase our knowledge of GE-SQ and its behavioral consequences. Moreover, the study proposes and validates a theoretical framework based on GE-SQ, value co-creation and customer relationship quality. This study provides insight into using gamification as a practical tool in the e-retailing industry.

Recent library and information science literature suggests transgender and nonbinary populations are not treated, served and represented on an equal or equitable basis as cisgender populations are in libraries. This article aims to assess the prevalence of bias and inclusion efforts in Illinois libraries.

This quantitative, cross-sectional, descriptive study utilizes a critical queer theory lens and includes a Likert scale survey with a demographic question on gender identity to measure four constructs and determine if there is a relationship between gender identity and bias, inclusion efforts, and knowledge of transgender and nonbinary user needs.

Results suggest respondents' biases reinforce structural cisgenderism in Illinois libraries and may account for the unequal conditions trans and nonbinary populations experience. Additionally, there is a correlation between cisgender-identifying Illinois LIS professionals and biased attitudes and behaviors, use of inclusive practices, and knowledge of transgender and nonbinary user needs.

This study contributes quantitative data, analysis and practical implications to a body of predominantly qualitative library literature on transgender and gender diverse experiences in libraries.

The purpose of this paper is to systematically analyze existing studies related to the adoption of smart technologies in cruise tourism services, particularly robots, artificial intelligence, service automation and virtual reality. More specifically, the authors intend to highlight the current state of research on this topic, present the findings within a conceptual framework and propose a research agenda.

The relevant literature was extracted using two major electronic databases, web of science (WoS) and Scopus. The authors identified 31 articles from high-quality journals and used a systematic review and the VOSviewer software to analyze them.

Since 2014, there has been an increase in the number of studies related to smart technologies in cruise tourism services. At first, researchers focused on Royal Caribbean’s robotic bartender arm, whereas other technologies such as digital signage, self-service options, facial recognition and virtual culinary experiences received less attention. However, the interest in exploring these last smart technologies has grown significantly since 2019. The adoption of RAISA in the cruise tourism service (ASCT) framework was proposed, identifying five major domains: cruise robotic technology, technology innovation, cruise passengers’ engagement behavior, cruise passengers’ technology readiness and privacy perception and knowledge expertise. These domains provide valuable guidance for future research in this field.

To the best of the authors’ knowledge, this is the first study to systematically analyze literature on the adoption of new technologies in cruise tourism services, specifically focusing on the major technologies available on cruise ships.

本文的主要目标是系统地分析与邮轮旅游服务中智能技术采用（RAISA）相关的现有研究, 特别是关于机器人、人工智能、服务自动化和虚拟现实等方面。具体而言, 我们意图突出此主题的当前研究现状, 将研究结果呈现在一个概念框架中, 并提出一个研究议程。

我们使用了两个主要的电子数据库 WoS 和 Scopus 提取相关文献。我们从高质量期刊中找到了 31 篇文章, 并采用系统综述和 VOSviewer 软件对它们进行了分析。

自 2014 年以来, 与邮轮旅游服务中智能技术相关的研究数量不断增加。最初, 研究人员着重关注皇家加勒比邮轮公司的机器人调酒师臂, 而其他技术, 如数字标识、自助选项、面部识别和虚拟烹饪体验等, 得到的关注较少。然而, 自 2019 年以来, 探索这些智能技术的兴趣显著增长。提出了 ASCT 框架, 识别了五个主要领域：邮轮机器人技术; 技术创新; 邮轮旅客参与行为; 邮轮旅客技术准备度; 以及隐私感知和专业知识。这些领域为该领域的未来研究提供了有价值的指导。

这是第一篇系统分析邮轮旅游服务中新技术采用文献的研究, 特别聚焦于邮轮上的主要技术。