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Although adaptation to climate change is a well-researched topic at the individual level and in highly vulnerable industries, its integration into business strategies is poorly researched. In this chapter, we conduct bibliometric analyses on a sample of 368 relevant papers published in business journals to derive descriptive statistics and map the conceptual and intellectual structure of the field. We find an increased interest in adaptation and confirm a strong representation of industry-specific research. We complement the bibliometric analyses with a content analysis focused on emergent themes in the adaptation scholarship. We discuss systemic influences, individual effects, regulations and stakeholders, and exposure as areas likely to attract further scrutiny in future scholarship. For each theme, we derive practical implications for practitioners and policymakers.

The current paper aims to discuss the implementation of Green Lean Six Sigma (GLSS) in the food industry to improve sustainable practices. The focus is more specifically on dairy processors to ascertain the current state of the literature and aid future research direction.

Utilising a systematic literature review (SLR), the paper addresses various terms and different written forms in the literature. The study characterises the current deployment of GLSS in the food industry and explains the reported benefits of this approach.

GLSS, a concept that has yet to be fully explored in the food industry, as in other sectors, holds significant potential to enhance the food industry’s sustainability practices. The dairy sector, a subsector of the food industry known for its high greenhouse gas emissions, is a prime candidate for the application of GLSS. In instances where it has been applied, GLSS has demonstrated its effectiveness in improving sustainability, reducing waste, lowering greenhouse gas emissions and minimising water usage. However, the specific tools used and the model for GLSS implementation are areas that require further study, as they have the potential to revolutionise food industry operations and reduce their environmental impacts.

Benchmarking of this research by the food industry sector and by academics can aid understanding of the practical application of GLSS tools and aid implementation of these practices to evolve the dairy processing sector in the next decade as sustainability champions in the sector.

This study extensively analyses GLSS in the food industry, with a particular focus on dairy processors.

This paper aims to investigate how implying movement in food packaging imagery may affect product liking. Furthermore, the underlying mechanism is investigated by studying the effect of implied motion visuals on design appeal and naturalness perception.

Two packages of pineapple juice were designed in which the implied motion depicted in their imagery was manipulated, and a tasting experiment was conducted in which two samples of the same juice were evaluated.

The results show that the effect of packaging imagery on product liking occurs indirectly through both design appeal and the product naturalness perception. The results of a parallel multiple-mediator analysis show that (1) depicting implied motion made the package be perceived as more appealing, (2) the product corresponding to the package depicting implied motion was perceived as being more natural, and (3) both effects equally contributed to the positive effect of visuals depicting implied motion on product liking.

Overall, these findings widen our understanding of the effects of packaging design on product liking and may help both designers and manufacturers design more appropriate packaging for their products.

The purpose of the study was to explore the purchase intention of online consumers by proposing and validating a model supported by exhaustive reviews from top-rated journals, where digital technology, consumer privacy, consumer engagement and online advertising were the extracted constructs influencing consumer learning on digital platforms and finally evaluating the purchase intention of online consumers.

A questionnaire representing these constructs was then sent to the 470 respondents on LinkedIn, and then designations like business heads, managers and faculty from educational institutions were selected using a stratified sampling technique and, finally, PLS-SEM robust computation standards aided in research model assessment and validation.

Results predicted that the variance explained by individual independent constructs defines consumer privacy as a priority for companies, followed by online advertising, consumer engagement and digital technology while measuring the final purchase intent for online consumption. Also, with dynamism in consumer sentiments and a rapidly changing technological environment, the consumer’s digital behaviour may differ in the coming future in relation to their online purchase intent.

Current research anticipates that the final online purchase intent of consumers has been vividly covered by our independent constructs, but an unexplained R² of 31% still promotes prospects related to the existing research. Furthermore, India has a huge rural population that, with a lack of money, has a complex behavioural mindset due to religious issues.

It is important to note that in a real-time market, a better understanding of the duality of persuasive and smart technology and the evaluation of the performance of social media helps in deciding the final online consumer intent.

The need for digital transformation has become an essential necessity for companies while managing the expectations and needs of the fastest-growing online consumers.

The surge of artificial intelligence (AI) applications and subsequent adoption by consumers and marketers has ignited substantial research exploring the benefits and opportunities of AI. Despite this, little attention has been given to its unintended negative consequences. In this paper, the authors examine both the practitioner and academic sides of ethical AI. In doing so, the authors conduct an extensive review of the AI literature to identify potential issues pertaining to three areas: individual consumers, societal and legal. The authors identify gaps and offer questions to drive future research.

The authors review recent academic literature on AI in marketing journals, and top ethical principles from three top technology developers (Google, IBM and Meta) in conjunction with media reports of negative AI incents. They also identify gaps and opportunities for future research based on this review.

The bibliographic review reveals a small number of academic papers in marketing that focus on ethical considerations for AI adoption. The authors highlight concerns for academic researchers, marketing practitioners and AI developers across three main areas and highlight important issues relating to interactive marketing.

This paper highlights the under-researched negative outcomes of AI adoption. Through an extensive literature review, coupled with current responsible AI principles adopted by major technology companies, this research provides a framework for examining the dark side of AI.

This study aims to explore image-based visual price determinants (image features and visual aesthetic perception) and how image features affect Airbnb listing price on a sharing accommodation platform.

The study uses an SOR model and a hedonic price model to examine the connections between the characteristics of image features, visual aesthetic perception and Airbnb listing prices. The model is then examined by an econometric model using data from Insideairbnb.com.

Empirical results revealed that image features have a significant positive effect on visual aesthetic perception, visual aesthetic perception has a significant positive effect on Airbnb listing price and visual aesthetic perception has a significant mediating effect between image features and Airbnb listing price.

This study contributes to the relationship and effect mechanism among image features, visual aesthetic perception and Airbnb listing price and has some implications for both property operators and the sharing accommodation platform.

本研究探讨了基于图像的视觉价格决定因素（图像特征和视觉美学感知）以及图像特征如何影响共享住宿平台Airbnb价格。

本研究采用SOR模型和hedonic价格模型来检验图像特征特征、视觉美感与Airbnb房源价格之间的关系。然后使用Insideairbnb.com上的数据, 通过计量经济学模型对该模型进行检验。

实证结果显示：1）图像特征对视觉美学感知有显著的正向影响; 2）视觉美学感知对Airbnb价格有显著的正向影响; 3）视觉美学感知在图像特征和Airbnb价格之间有显著的中介效应。

本研究有助于探讨图像特征、视觉美学感知和Airbnb价格之间的关系和影响机制, 对房源经营者和共享住宿平台都有一定的借鉴意义。

Este estudio explora los determinantes visuales del precio basados en las imágenes (características de las imágenes y percepción estética visual) y cómo afectan las características de las imágenes al precio de los anuncios de Airbnb en una plataforma de alojamiento compartido.

El estudio emplea un modelo SOR y un modelo de precios hedónicos para examinar las conexiones entre las características de los rasgos de la imagen, la percepción estética visual y los precios de Airbnb. A continuación, se examina el modelo mediante un modelo econométrico utilizando datos de Insideairbnb.com.

Los resultados empíricos revelan que 1) las características de la imagen tienen un efecto positivo significativo sobre la percepción estética visual, 2) la percepción estética visual tiene un efecto positivo significativo sobre el precio de los anuncios de Airbnb, y 3) la percepción estética visual tiene un efecto mediador significativo entre las características de la imagen y el precio de los anuncios de Airbnb.

Este estudio contribuye al mecanismo de relación y efecto entre las características de la imagen, la percepción estética visual y el precio del anuncio de Airbnb, y tiene algunas implicaciones tanto para los operadores inmobiliarios como para la plataforma de alojamiento compartido.

This study investigates the finance-investment nexus in sub-Saharan Africa using data from 41 countries spanning the period from 2000 to 2022. The central question addressed is whether there is a “too little” or “too much” finance problem in the region.

This study employs a system-generalised method of moments (GMM) approach to analyse the association between finance and private investment. Additionally, a dynamic threshold regression model is used to uncover potential nonlinearities in this relationship.

Initially, the study identifies a negative correlation between increased finance and private investment. However, further analysis using the dynamic threshold regression model reveals a critical threshold level of finance. Specifically, the threshold is found to be 6.52% of domestic credit to the private sector and 23.18% using the financial development index. Below this threshold, finance negatively impacts private investment, while surpassing this threshold leads to positive growth in private investment. These findings indicate an issue of “too little” finance in the finance and private investment nexus in sub-Saharan Africa. The results are robust across different model specifications.

The implications of this study highlight the importance of identifying critical thresholds for financing to enhance investment expenditures in the region.

This study contributes to the literature by uncovering nonlinearities in the finance-investment nexus in sub-Saharan Africa. The identification of critical thresholds provides valuable insights for policymakers, emphasising the need to strengthen the financial sector in countries operating below these thresholds to promote private investment and economic growth.

Fueled by the soaring popularity of the digital medium, consumers are increasingly relying on dynamic images to inform their decisions. However, little is known about how changes in the presentation of movement impacts these decisions. The purpose of this paper is to document whether and how movement speed–a fundamental characteristic of dynamic images in the digital medium–influences consumers' risk judgments and subsequent decisions.

Three experimental studies investigate the impact of movement speed displayed in the digital medium, focusing on different risk-laden domains including health (pilot study), gambling (Study 1) and stock market decisions (Study 2).

The authors find that faster movement speed displayed in the digital medium elevates consumers’ feelings of risk and elicits cautionary actions in response. The authors reveal a mechanism for this effect, showing that faster movement reduces feelings of control over outcomes, which predicts greater feelings of risk.

Future work could expand upon these findings by systematically examining whether certain individuals are more susceptible to movement speed effects in the digital medium. Research could also investigate whether different ways of experiencing movement speed (e.g. physical movement) similarly influence risk judgments and whether movement speed can have positive connotations outside of risky domains.

The authors offer important insights to marketing practitioners and public policymakers seeking to guide consumers’ judgments and decisions in risk-laden contexts through the digital medium.

By showing how movement speed alters judgments in risk-laden contexts, the authors contribute to literature on risk perception and the growing body of literature examining how moving images shape consumers’ behaviors.

Service robots offering low-contact services are trending in the hospitality industry. Nevertheless, there is limited empirical research on consumer perspectives. Using the stimulus-organism-response (SOR) model, this study aims to investigate consumer acceptance of service robots and explores the relationships among stimuli (coolness and affinity), organisms (utilitarian and hedonic value) and responses (behavioral intention).

This study uses a purposive sampling method and conducts a questionnaire survey in restaurants with service robots in Taiwan. A total of 554 valid responses are analyzed, and confirmatory factor analysis and structural equation modeling are performed.

The findings reveal that coolness and affinity positively influence utilitarian and hedonic values, whereas utilitarian and hedonic values positively affect behavioral intention. Additionally, these values mediate how coolness and affinity impact behavioral intentions, showing that coolness more significantly influences hedonic value, while affinity has a greater effect on utilitarian value.

As a theoretical basis, the SOR model examines the relationships among stimulus, organism and response. This study comprehensively investigates the factors influencing consumer perception toward service robots, testing the applicability of this theory in the food and beverage industry and expanding its practical value. Additionally, the author introduces the “fit” concept, indicating that, for maximum effect, coolness pairs best with hedonic value and affinity with utilitarian value.

提供低接触服务的服务机器人在酒店业中越来越受欢迎。然而, 关于消费者视角的实证研究仍然有限。本研究采用刺激-有机体-反应（SOR）模型, 调查消费者对服务机器人的接受度, 并探讨刺激（酷炫度和亲和力）、有机体（实用价值和享乐价值）和反应（行为意图）之间的关系。

本研究采用目的性抽样方法, 在台湾有服务机器人的餐厅进行问卷调查。共收集到554份有效问卷, 并进行了确认性因子分析和结构方程模型分析。

研究结果表明, 酷炫度和亲和力对实用价值和享乐价值有正向影响, 而实用价值和享乐价值对行为意图有正向影响。此外, 这些价值在酷炫度和亲和力对行为意图的影响中起中介作用, 表明酷炫度对享乐价值的影响更显著, 而亲和力对实用价值的影响更大。

本研究以SOR模型为理论基础, 检验了刺激、有机体和反应之间的关系。我们全面调查了影响消费者对服务机器人感知的因素, 测试了该理论在餐饮业中的适用性, 并扩展了其实际价值。此外, 我们引入了“匹配”概念, 指出为了达到最大效果, 酷炫度最好与享乐价值搭配, 而亲和力最好与实用价值搭配。

Financial institutions actively seek to leverage the capabilities of artificial intelligence (AI) across diverse operations in the field. Especially, the adoption of AI advisors has a significant impact on trading and investing in the stock market. The purpose of this paper is to test whether AI advisors are less preferred compared to human advisors for investing and whether this algorithm aversion diminishes for trading.

The four hypotheses regarding the direct and indirect relationships between variables are tested in five experiments that collect data from Prolific.

The results of the five experiments reveal that, for investing, consumers are less likely to use AI advisors in comparison to human advisors. However, this reluctance to AI advisors decreases for trading. The author identifies the perceived importance of careful decision-making for investing and trading as the psychological mechanism. Specifically, the greater emphasis on careful decision-making in investing, as compared to trading, leads to consumers’ tendency to avoid AI advisors.

This research is the first to investigate whether algorithm aversion varies based on whether one’s approach to the stock market is investing or trading. Furthermore, it contributes to the literature on carefulness by exploring the interaction between a stock market approach and the lay belief that algorithms lack the capability to deliberate carefully.