Search results

Chocolate production and cocoa supply chains are rife with social and environmental challenges. Chocolate companies commonly make claims that their products are “sustainable”, giving little guidance on what this means. The aim of this paper is to conduct a scoping review to synthesise the accounting literature related to the chocolate industry and sustainability and develop a research agenda for accounting scholarship.

The scoping review followed Arksey and O’Malley’s (2005) five-stage framework for a scoping review. Nineteen accounting journals were searched for literature on “chocolate OR cocoa AND sustainability” from 2000 to 2023. A total of 171 papers were identified through the search, of which 18 were deemed relevant and included for thematic analysis. The themes are analysed using a conceptual framework on accountability.

Analysis of the relevant literature revealed three distinct perspectives on sustainability in the chocolate industry. These include critique on the problems related to top-down accountability approaches in the chocolate industry; that accountability mechanisms have fallen short in managing sustainability challenges; and that sustainability interventions are driven by profit motives. The themes further reveal a lack of accountability in the industry for marginalised voices.

The scoping review methodology used in this study offers insights into the diverse perspectives on sustainability in the chocolate industry. This research adds valuable knowledge to the field by uncovering nuanced issues around accountability and sustainability and highlighting the need for future research for accountability for sustainable chocolate production.

The purpose of this study is to (1) identify the factors that affect the adoption of virtual reality (VR) for spectator sports, (2) examine the differences in the factors among the four adopter categories (i.e. continuers, discontinuers, potentials and resistors) and (3) determine whether these factors are useful for discriminating among the adopter categories, based on the “diffusion of innovation” and “uses and gratification” theories.

In total, 216 participants were included in the analysis. Logistic regression and multiple analyses of variance were conducted to identify the factors that affect the adoption of VRS and examine the differences in the factors between adopter and non-adopter as well as between the continuers, discontinuers, potentials and resistors.

This study found that actualized innovativeness, complexity, companionship and gender significantly affect user adoption of VR for spectator sports. There were significant differences in the factors among the four adopter categories. The factors were also useful in discriminating between the four adopter categories.

This study highlights how individuals embrace emerging technologies differently based on their adopter category characteristics. From a marketing perspective, the insights gained from this study can inform the development of targeted strategies, campaigns and user experiences for VR spectator sports (VRS). This approach promises new revenue streams for the spectator sport industry and offers solutions to challenges like declining viewership and digital marginalization. It underscores the potential success of VR technology in transforming the spectator sport industry.

Several factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors.

The research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing — the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI).

The research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM) and belief in OSC as the main influencing factors.

Data were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond.

The research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered.

The research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage.

The crypto market is growing quickly, marked by a lack of fundamentals, and the risks are not yet fully comprehended by participants. Our goal is to investigate overconfidence in this market and analyze the role that risk propensity and certain demographics play.

We conducted a survey in Brazil and Portugal, leveraging an online questionnaire disseminated via social media channels to engage a diverse adult population. We collected a total of 826 responses, addressing ethical considerations throughout the process. The data analysis was conducted using SPSS statistical software and logit regression modeling.

Our study reveals that overconfidence is a notable bias that distinguishes individuals who invest in cryptocurrencies from those who do not. Although overconfidence and risk propensity are closely linked, they originate from distinct personal characteristics. Furthermore, our findings indicate that age and market experience positively correlate with overconfidence and negatively correlate with risk propensity. Financial knowledge, interestingly, did not prove to be a significant factor for cryptocurrency investment.

Our research augments the existing literature on overconfidence, delving into this phenomenon in a new subdomain, and in doing so, it enriches our comprehension of the unique and still relatively under-researched cryptomarket. Moreover, we illuminate individual factors that sway the decision to invest in cryptocurrencies and should be considered by market participants.

• (1)

  Pioneering work examining the presence of overconfidence bias among crypto-investors, using a robust data set collected from a binational survey.

• (2)

  Verifies the relations among overconfidence, risk propensity, and demographics.

• (3)

  Examines the influence of age and experience on investment decisions, revealing a positive relationship with overconfidence and a negative correlation with risk propensity.

• (4)

  Logistic regression is used to determine the combined effect of overconfidence, risk propensity, and demographics on the decision to invest in cryptocurrencies.

Pioneering work examining the presence of overconfidence bias among crypto-investors, using a robust data set collected from a binational survey.

Verifies the relations among overconfidence, risk propensity, and demographics.

Examines the influence of age and experience on investment decisions, revealing a positive relationship with overconfidence and a negative correlation with risk propensity.

Logistic regression is used to determine the combined effect of overconfidence, risk propensity, and demographics on the decision to invest in cryptocurrencies.

The study examines the impact of behavioral biases, such as herd behavior, overconfidence and reactions to ESG News, on Socially Responsible Investing (SRI) decisions in the Indian context. Additionally, it explores gender differences in SRI decisions, thereby deepening the understanding of the factors shaping SRI choices and their implications for sustainable finance and gender-inclusive investment strategies.

The study employs Bayesian linear regression to analyze the impact of behavioral biases on SRI decisions among Indian investors since it accommodates uncertainties and integrates prior knowledge into the analysis. Posterior distributions are determined using the Markov chain Monte Carlo technique, ensuring robust and reliable results.

The presence of behavioral biases presents challenges and opportunities in the financial sector, hindering investors’ SRI engagement but offering valuable opportunities for targeted interventions. Peer advice and hot stocks strongly predict SRI engagement, indicating external influences. Investors reacting to extreme ESG events increasingly integrate sustainability into investment decisions. Gender differences reveal a greater inclination of women towards SRI in India.

The sample size was relatively small and restricted to a specific geographic region, which may limit the generalizability of the findings to other areas. While efforts were made to select a diverse sample, the results may represent something different than the broader population. The research focused solely on individual investors and did not consider the perspectives of institutional investors or other stakeholders in the SRI industry.

The study's practical implications are twofold. First, knowing how behavioral biases, such as herd behavior, overconfidence, and reactions to ESG news, affect SRI decisions can help investors and managers make better and more sustainable investment decisions. To reduce biases and encourage responsible investing, strategies might be created. In addition, the discovery of gender differences in SRI decisions, with women showing a stronger propensity, emphasizes the need for targeted marketing and communication strategies to promote more engagement in sustainable finance. These implications provide valuable insights for investors, managers, and policymakers seeking to advance sustainable investment practices.

The study has important social implications. It offers insights into the factors influencing individuals' SRI decisions, contributing to greater awareness and responsible investment practices. The gender disparities found in the study serve as a reminder of the importance of inclusivity in sustainable finance to promote balanced and equitable participation. Addressing these disparities can empower individuals of both genders to contribute to positive social and environmental change. Overall, the study encourages responsible investing and has a beneficial social impact by working towards a more sustainable and socially conscious financial system.

This study addresses a significant research gap by employing Bayesian linear regression method to examine the impact of behavioral biases on SRI decisions thereby offering more meaningful results compared to conventional frequentist estimation. Furthermore, the integration of behavioral finance with sustainable finance offers novel perspectives, contributing to the understanding of investors, investment managers, and policymakers, therefore, catalyzing responsible capital allocation. The study's exploration of gender dynamics adds a new dimension to the existing research on SRI and behavioral finance.