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This study is based on the heuristic-systematic model (HSM) to dynamically examine the effect of review variance on sales and the boundary conditions that mitigate this effect.

Based on the theoretical domain of HSM, a conceptual model is proposed that analyzes the nonlinear relationship between review variance and sales and the interaction and motivation factors that moderate these relationships. Review data from websites targeting the film industry in the USA and South Korea (Korea) were collected to empirically analyze the authors' hypothesis, and panel regression analysis was used for confirmation.

Moderated by interactive and motivational factors, review variance exhibits an inverse-U-shaped relationship with review variance. Specifically, as an interaction factor, review valence and owned social media (OSM) resulted in positive interaction effects, and as a motivation factor, the number of alternatives exhibited a positive interaction effect with review variance. The effect of review variance was less pronounced in the USA than in Korea.

The study outcomes reveal a nonlinear relationship between review variance and sales, thus supporting the contradictory findings of previous studies. This study contributes to the literature by using the HSM as a theoretical framework to verify various HSM mechanisms using online review data. This exploratory study also contributes to the international marketing literature by showing that the effects of review variance vary across cultures.

The aim of this paper is to discuss a selection of policy strategies, regional initiatives and market approaches to uncover the realities of twenty-first-century building energy performance. A position that market-based approaches, human influence and policy interventions are part of an ecosystem of building energy performance is presented.

An exploratory search of secondary sources spanning the last three decades was conducted. Both peer-reviewed and grey literature were included to capture a broader understanding of the discourse in literature. Research questions guided the literature search, and a data extraction tool was designed to categorise the literature. The primary limitation of this study is that only a few applications could be discussed in a condensed format.

Several challenges about the current status quo of building energy performance were identified and summarised as follows. (1) Inconsistencies in measurement and verification protocols, (2) Impacts of market approaches, (3) National policy priorities that are at variance with regional targets and (4) Ambiguous reporting on environmental impacts of energy efficiency (EE) technologies.

The practical implications of the findings in this paper for practice and research are that as part of the building energy performance ecosystem, national responses through government interventions must become adaptive to keep up with the fast-paced energy sector and social trends. Simultaneously, before market-based approaches overcome the messiness of socio-economic dynamics, institutional conditions and cultural nuances, they ought to transparently address environmental impacts and the infringement of several SDGs before they can become viable solutions to building energy performance.

This paper presents building energy performance as an ecosystem comprising human influence, market-based approaches and policy interventions which form interdependent parts of the whole. However, evidence in the literature shows that these aspects are usually investigated separately. By presenting them as an ecosystem, this paper contributes to the discourse by advocating the need to re-align building energy performance to socio-economic-political dynamics and contextually viable solutions.

This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings.

This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics.

Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting.

While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK.

This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system.

This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.

The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used.

The research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results.

The built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices.

The NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market.

The authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

When financial statements are public, the choice between alternative reporting regimes constitutes a signal that addresses external stakeholders. Generally, the choice of more complex regimes acts as a complement of firms' transparency. However, in the absence of audits, opportunistic behaviors could be incentivized. This study aims to test whether SMEs' choice between alternative accounting regimes is associated with earnings quality.

Drawing on the literature about accounting choices and earnings quality, this study investigates whether the same conclusions are confirmed for SMEs. Using a sample of 4,054 Italian companies and 12,114 observations, it compared four earnings quality proxies of a group of companies that opted for the “Full” rules and those of a subsample of the population of companies that applied the Simplified rules.

The results suggest that the signaling power of accounting rules' choice could lead to wrong conclusions for SMEs. Indeed, a positive relationship emerged (H1) between the choice of the “Full” rules and income smoothing behaviors, while the same choice appears to reduce the probability to disclose SPOS. Moreover, the results suggest that opportunistic behaviors are more frequent for firms that have settled in a “non-cooperative” social environment (H2).

This study could foster research on financial reporting quality in private firms.

Comparing the quality of financial statements drawn up according to two alternative accounting regimes could provide useful suggestions for both users and regulators.

The results contribute to the limited literature on the implications of differential reporting. Finally, it enriches the literature about heterogeneity in accounting quality within private firms.

User acceptance is a necessary precondition to implementing self-driving buses as a solution to public transport challenges. Focusing on potential users in a real-life setting, this paper aims to analyze the factors that affect their willingness to use public autonomous shuttles (PASs) as well as their word-of-mouth (WOM) intentions.

Grounded on Unified Theory of Acceptance and Use of Technology (UTAUT2), the study was carried out on a sample of 318 potential users in a real-life setting. The hypothesized relationships were tested using partial least squares structural equation modeling (PLS-SEM).

The study reveals that performance expectancy, facilitating conditions, hedonic motivation and trust are significant predictors of PAS usage intention, which is, in turn, related to WOM communication. Additionally, the factors that impact the intention to use a PAS are found to exert an indirect effect on WOM, mediated by usage intention.

This study includes practical insights for transport decision-makers on PAS service design, marketing campaigns and WOM monitoring.

While extant research focuses on passengers who have tried autonomous shuttles in experimental settings, this article adopts the perspective of potential users who have no previous experience with these vehicles and identifies the link between usage intention and WOM communication in a real-life traffic environment.

若要引入自動駕駛巴士來解決公共交通的問題和挑戰,一個必不可少的先決條件是得到用戶的認可。本研究透過重點分析活在真實生活環境中的潛在用戶,來探討影響他們使用公共自動交通工具的意願和口碑動機的各個因素。

本研究以延伸整合型科技接受模式為基礎,對一個涵蓋處身於真實生活環境中318名潛在用戶的樣本進行分析和探討。研究人員以偏最小平方法的結構方程模型 (PLS-SEM), 去測試各個被假設的關聯。

研究結果顯示,績效期望、有利條件、享樂動機和信任均明顯能夠預測人們使用公共自動交通工具的意願,而人們使用公共自動交通工具的意願又反過來與口碑溝通有所相關。另外,研究人員發現,影響人們使用公共自動交通工具意願的各個因素,對口碑會產生間接的影響,而使用意願是會起著調節作用的。

現存的學術研究均聚焦分析那些曾於實驗設置下坐過自動交通工具的人士,而本研究卻採用從未坐過自動交通工具人士的角度來進行分析與探討,並且找出了於實際的交通環境裡、使用意願與口碑溝通之間的關聯。

本研究提供的啟示,對有關公共自動交通工具服務設計、市場營銷活動和口碑監督工作的運輸決策者來說頗具實務意義。

This study examines how the strong emphasis placed on the purposes of budgeting, referring to a comprehensive focus on budgeting, is related to top managers' education and tenure while controlling for their functional positions in their respective firms and ages, as well as several company-specific predictors (information quality, firm size, information technology, importance of profit and strategy).

Survey data were collected from senior managers of large manufacturing firms in Finland and Sweden.

The results suggest that academic business education is positively associated with a comprehensive focus on budgeting, but tenure as well as functional position in the company (Chief Financial Officer (CFO) or not) and age are not. Overall, the company-specific control variables in general and information quality in particular are shown to have greater explanatory power than the top management characteristics analyzed.

This study identifies several empirically supported factors that seem to contribute to a comprehensive focus on budgeting. The effects of information quality, business education, the importance of profit and firm size could be considered in future research.

Academic business education matters more than the other top management characteristics analyzed. If organizations want to make comprehensive use of budgets, they should employ business graduates and be mindful of company-specific variables.

This study is the first to address a comprehensive focus on budgeting and some of its determinants. Future research could investigate a broader set of such determinants in different contexts.

The study evaluates the effects of governance and other regulatory structures on the development of financial institutions in the subregion of sub-Saharan Africa (SSA).

Data for the analyses were compiled from relevant sources from 1996 to 2019 from a sample of 36 countries in the subregion. Empirical analyses were carried out using the Prais-Winsten panel corrected standard errors panel estimation technique augmented by pooled ordinary least squares with Driscoll and Kraay (1998) standard errors model.

Findings from the study suggest that governance and institutional quality index, as well as individual governance and regulatory variables, have positive effect on the development of financial institutions among economies in SSA. Further empirical estimates show that output growth volatility has negative moderating impact on the relationship between effective governance, control of corruption, rule of law, regulatory quality, voice and accountability, and development of financial institutions. Additionally, the results show that during periods of heightened macroeconomic risk, financial institutions could benefit from improved governance and effective regulatory structures.

Compared to related studies that have reviewed the discourse on financial institutions, this study rather focuses on how governance structures and institutions influence development of financial institutions instead of the impact of financial institution on the broader economy. The authors further augment this interaction by examining how the relationship in question may be moderated by macroeconomic shocks.

The purpose of this study is to investigate the impact of family motivation (FM) on promotive voice behavior (VBPm) and knowledge hiding (KH). The study uses moral disengagement (MD) role as a mediator to see how FM shapes moral engagement leading to participate in promotive voice and knowledge sharing.

The hypothesized model was tested using partial least squares structural equation modeling. The authors used convenience sampling and collected data in two phases. The authors have a final sample of 257 faculty members for analysis, with an overall response rate of 42.8%.

Study findings reveal a negative relationship between FM with MD and a positive relationship with VBPm. The relationship between FM and (KHKH results did not show the expected effects. At the same time, mediation of MD between FM and voice behavior and FM and (KHKH show the expected results.

The study finds that family factors have practical consequences for companies in recognizing the value of familial elements in cultivating employee voice and engagement behaviors. Since family is a powerful motivation to work, it provides valuable insights for HRM strategies and organizational studies to encourage employee voice and moral engagement in the workplace. The study is one of the few studies investigating the impact of FM on promotive voice and KH and enhancing the knowledge of mediating role of MD.