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AI-based chatbots are revamping employee communication in organizations. This paper examines the adoption of AI-based employee experience chatbots by employees.

The proposed model is developed using behavioral reasoning theory and empirically validated by surveying 1,130 employees and data was analyzed with PLS-SEM.

This research presents the “reasons for” and “reasons against” for the acceptance of AI-based employee experience chatbots. The “reasons for” are – personalization, interactivity, perceived intelligence and perceived anthropomorphism and the “reasons against” are perceived risk, language barrier and technological anxiety. It is found that “reasons for” have a positive association with attitude and adoption intention and “reasons against” have a negative association. Employees' values for openness to change are positively associated with “reasons for” and do not affect attitude and “reasons against”.

This is the first study exploring employees' attitude and adoption intention toward AI-based EEX chatbots using behavioral reasoning theory.

Plastic pollution is a major issue that plagues modern society. Scholars are interested in comprehending consumers’ behavioural actions to address plastic pollution. This study aims to delve into the determinants of consumers’ engagement with social media as a medium to address plastic pollution.

A conceptual model is developed that extends the behavioural reasoning theory (BRT). Using a quantitative approach, 476 responses underwent structural equation modelling analysis.

Results indicate that “reasons for” positively correlate with attitude and intention towards socially responsible engagement. Contrarily, “Reasons against” demonstrated a positive association with socially responsible engagement intention. Attitudes favouring socially responsible engagement correlate positively with the underlying intention. The moderation analysis underscores the positive relation of social return on social media with consumers’ attitude and their “reasons for” leaning towards socially responsible engagement intention. Notably, a positive connection was established between socially responsible engagement intention and the trifecta of consumption, contribution and content creation behaviours.

By enhancing the BRT, this research sheds light on novel perspectives regarding consumers’ engagement on social media platforms. Distinctively, it is among the handful of studies probing the influence of behavioural intention across diverse behavioural outcomes. The insights gained from this study, grounded in empirical evidence from an emerging market, are poised to guide policymakers, governmental agencies and industry practitioners in formulating effective strategies to combat plastic pollution. Additionally, the study can assist in achieving the UN sustainable development goals (SDGs), specifically SGD 12, SGD 13, SDG 14 and SGD 17.

Driven by Davis’s (1989) technology acceptance model (TAM) and Westaby’s (2005) behavioral reasoning theory (BRT), the purpose of this study is to develop and test a conceptual model and examine consumers’ acceptance of artificial intelligence (AI) chatbots for apparel shopping.

Data from 353 eligible US respondents was collected through a self-administered questionnaire distributed on Amazon Mechanical Turk, an online panel. Confirmatory factor analysis and path analysis were used to test all hypothesized relationships using the structural equation model.

The results show that optimism and relative advantage of “reasons for” dimensions have a positive and significant influence on perceived ease of use (PEU), while innovativeness and relative advantage have a positive and significant influence on perceived usefulness (PUF). Discomfort and insecurity have no significant impact on PEU and PUF. However, complexity has a negative and significant impact on PEU but not on PUF. Additionally, PEU has a positive influence on PUF. Both PEU and PUF have a positive and significant influence on consumers’ attitudes toward using AI chatbots, which, in turn, affects the intention to use AI chatbots for apparel shopping. Overall, this study identifies that optimism, innovativeness and relative advantage are enablers and good reasons to adopt AI chatbots. Complexity is a prohibitor, making it the only reason against adopting AI chatbots for apparel shopping.

This study contributes to the literature by integrating TAM and BRT to develop a research model to understand what “reasons for” and “reasons against” factors are enablers or prohibitors that significantly impact consumers’ attitude and intention to use AI chatbots for apparel shopping through PEU and PUF.

This study aims to systematically review various behavioral biases that impact an investor’s decision-making process. The prime objective of this paper is to thematically explore the behavioral bias literature and propose a comprehensive framework that can elucidate a more reasonable explanation of changes in financial markets and investors’ behavior.

Systematic literature review (SLR) methodology is applied to a portfolio of 71 peer-reviewed articles collected from different electronic databases between 2007 and 2021. Content analysis of the extant literature is performed to identify the research themes and existing gaps in the literature.

This research identifies publication trends of the behavioral biases literature and uncovers 24 different biases that impact individual investors’ decision-making. Through thematic analysis, an attribute–consequence–impact framework is proposed that explains different biases leading to individual investors’ irrationality. The study further proposes directions for future research by applying the theory–characteristics–context–methodology framework.

The results of this research will help scholars and practitioners in understanding the existence of various behavioral biases and assist them in identifying potential strategies which can evade the negative effects of these biases. The findings will further help the financial service providers to understand these biases and improve the landscape of financial services.

The essence of the current paper is the application of the SLR method on 24 biases in the area of behavioral finance. To the best of the authors’ knowledge, this study is the first attempt of its kind which provides a methodical and comprehensive compilation of both cognitive and emotional behavioral biases that affect the individual investor’s decision-making.

Microfinance programs across the countries are designed on the self-help and peer pressure model, aim at microentrepreneurship development. Despite of significant studies on microfinance-supported microentrepreneurship (MSM), not a single literature examines it from the systems thinking. In addition to that, the extant literature did not look MSM from the behavioral perspectives. To address the above gaps, the present study aims to examine self-help group (SHG)-based microfinance programs from the systems approach using the Stimulus-Organism-Behavior-Consequence (SOBC) model.

Information gathered from 786 women SHG members from four states of India through a structured interview schedule. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were conducted to process data. Additional statistical tests were performed to test the reliability and validity.

It was found that the “positive stimulus” (social intermediation, financial intermediation and business development services) positively impacted; and “negative stimulus” (intermediation accountability, and intermediation assumption) negatively impact, to “motive” (attitude, subjective norms, and perceived control) for micro-entrepreneurship in the SHG-based microfinance. Further, “motive” positively predicted “behavioral intention”; the “behavioral intention” positively determined “consequences” of micro-entrepreneurship. Intermediation as stimuli acted as “input”; the motive and behavioral intention acted as the “process”, and the consequence acted as the “output” in the SHG-based microentrepreneurship system.

To the best of the author's knowledge, this paper is the first one to examine the behavioral systems of microentrepreneurship programs through the Stimulus-Organism-Behavior-Consequence (SOBC) model.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2022-0801

This study aims to identify, review and synthesize existing literature on key theories, drivers and barriers affecting consumer adoption or resistance to artificial intelligence (AI) in the hospitality sector.

This study aims to conduct a complete literature review of the accrued knowledge generated so far on AI in the hospitality sector. To attain the overall objectives of this study, we used the systematic literature review (SLR) method. This method systematically handles the diversity of knowledge in a specific topic to answer precise research questions. It also generates new visions through a synthesis of the literature, to identify the knowledge gaps, set the new directions for the future researcher and provide sufficient guidance to inform the policy and practice.

The findings of this study are presented in three sections, as follows: descriptive analysis, content analysis and synthesized framework. The findings highlighted the state-of-the-art mapping of the existing research in terms of publication frequency over time and across publication outlets, key theories, methods and geographies. In addition, literature on consumer adoption (or resistance) of AI in hospitality is content analyzed to highlight key drivers and barriers. Moreover, this review critically evaluates extant literature and sets future agendas by postulating specific research questions for further knowledge development in this field of study.

The SLR focused on consumer adoption or resistance to use AI in hospitality literature. The future researcher may include additional streams to get better results.

The study findings will help multiple stakeholders to understand the underlying causes of customer resistance or barriers to the intention to use/adopt AI services in the hotel sector. Furthermore, study results will allow them to better analyze the relationship between customer barriers, intents or consumer decision behaviors.

First, this study provides a comprehensive synthesis of the literature on the consumer adoption or resistance of AI in hospitality. This study categorizes the existing diversified literature in two main themes – drivers and barriers – to present a simplistic picture of the existing literature. Second, the review highlights the gaps and limitations in existing research and provides guidance for future scholars. Third, the key contribution of this review is the development of a unified framework on the consumer adoption or resistance of AI in the hospitality sector. That is, this study puts forward the behavioral reasoning theory framework and suggests that future research using this lens will immensely contribute to existing literature. Finally, this study facilitates the practitioners to understand the key motivating and hindering factors affecting the adoption and resistance behavior.

Manufacturing companies continue to encounter a diverse set of obstacles while embracing sustainable development goals. Accordingly, the purpose of this study is to explore critical sustainable development-related barriers to flexible packaging manufacturing companies in the New Zealand context.

Drawing on a qualitative multiple case studies approach, the authors collected data from the New Zealand flexible packaging industry. Semistructured interviews were conducted with the senior corporate managers in two large flexible packaging companies. Following the thematic analysis approach, the authors analyzed the information collected from the participants and synthesized our findings under the key dimensions of internal and external barriers to sustainable development.

The findings revealed that internal barriers to sustainable flexible packaging are linked to economic, operational and technical issues. Conversely, external barriers include global crises and disruption, customer behavior and preferences and institutional and infrastructural-related aspects. Based on the analysis of empirical findings, the authors further identified the underlying reasons for sustainable flexible packaging barriers and recommended guidelines that could assist corporate managers and policymakers in addressing obstacles inhibiting the flexible packaging industry from adopting sustainable business practices.

The authors argue that this study is one of the early studies to consider inhibiting factors to incorporate sustainable development into the New Zealand flexible packaging industry context. Building on a range of theoretical perspectives, the authors extend the current body of knowledge seeking to advance the sustainable development agenda in the New Zealand flexible packaging industry and offer recommended pathways fostering sustainable development in a distinctive manufacturing context.

The zero-waste lifestyle (ZWL) is considered a reasonable step towards controlling waste generation and minimizing the consequences of human activities on the environment. The main aim of this study is to examine the behavioral antecedents of ZWL.

The study draws on the theoretical underpinnings of the theory of planned behavior (TPB) and the norm activation model (NAM) to develop a conceptual framework to understand the antecedents to ZWL. A cross-sectional survey among 349 randomly-selected consumers provided data analyzed with the partial least square-structural equation modeling (PLS-SEM) methodology.

The results demonstrate that personal norms, attitude, subjective norms and perceived behavioral control positively influenced the intention to adopt ZWL. Additionally, the study showed that the awareness of consequences influenced personal norms, attitudes and subjective norms. However, the study identified an intention–behavior gap in adopting ZWL.

This study serves as a pioneering exploration of the behavioral factors that impact the adoption of ZWL. Additionally, the paper endeavors to elucidate the underlying reasons behind the intention–behavior gap within this particular context. Consequently, the study offers substantial theoretical and practical implications aimed at promoting and fostering greater adoption of ZWL practices.

The purpose of this study is to examine the trends and developments of subscription business models (SBMs) over the past two decades.

The study extracted 469 documents (articles and reviews) from the Scopus database during 2000–2022 and analysed 132 documents (articles and reviews). A bibliometric methodology of scientific mapping was employed, including a cluster analysis based on the bibliographic coupling of documents. Content analysis was also conducted to reveal emerging trends in SBMs.

The study revealed six emerging themes in SBMs related to consumer behaviour, digital advertising, online news media, journal publications, circular economy and sustainability strategies.

The results of this study provide new and unique insights into the development and trends of SBMs over the past two decades and offer guidance for future researchers to investigate further the phenomenon of SBMs in emerging areas.

This study aims to explore spectators' awareness of and attitudes toward sponsoring firms as well as to propose a model predicting their purchase and word-of-mouth (WOM) intentions toward the sponsors of a major sporting event during a pandemic crisis.

A quantitative method was used to collect the data, with 1,259 questionnaires being effectively collected and analyzed using SPSS and AMOS. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), path analysis and structural equation modeling (SEM) were used to analyze the data.

Factors such as sports involvement, sincerity, social media use, beliefs about sponsorship, sponsor's image, Covid-19 beliefs and emotions, awareness of and attitude toward sponsors were found to significantly predict spectators' purchase and WOM intentions.

The results support the structural equation model, providing a multidisciplinary conceptual framework that highlights the significance of comprehending the role of significant factors in sponsorship efficiency during the Covid-19 crisis. The proposed framework adds to the knowledge corpus of this field.