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This study integrates previous research on the intention to use Airbnb to determine which antecedents provide a parsimonious explanation.

Meta-analyses based on 61 samples estimate how 8 antecedents are associated with the intention to use Airbnb. Subsequent analyses utilize meta-analyses to estimate a regression model to simultaneously estimate the relationship between the antecedents and the intention to use Airbnb. Relative weight analysis then determined each antecedent’s utility.

A parsimonious model with only four antecedents (hedonic motivation, price value, effort expectancy and social influence) was nearly as predictive as the full eight-antecedent model. Ten moderating variables were examined, but none were deemed to consistently influence the relationships between the antecedents and the intention to use Airbnb.

Relatively few measures (i.e. four) effectively explain customers’ intentions to use Airbnb. When these measures cannot be readily influenced, alternatives are also presented. Implications for the travel industry are considered and straightforward approaches to increasing users are presented.

This is the first integrative review of customers’ intentions to use Airbnb. We integrate what is currently known about customers’ intentions to use Airbnb and then provide a robust model for Airbnb use intentions that both researchers and practitioners can utilize.

Although there has been a surge in the adoption of building information modelling (BIM) in the construction industry, the small and medium-sized enterprises (SMEs) are still struggling and perceive its adoption as risky. The SMEs in developing economies are especially on the disadvantaged side of the digital divide. Extant studies have focused on large firms and there are scanty studies on the influence of the external environments on BIM adoption in SMEs. Thus, this study espouses institutional theory (INT) to examine the influence of coercive, mimetic, and normative pressures on BIM awareness and adoption in SMEs.

A quantitative approach was employed, and data were collected from the Nigerian construction SMEs via an empirical questionnaire survey using a sequential stratified and convenient sampling method. Hypothesized relationships between the coercive, mimetic, and normative pressure and BIM in SMEs were empirically tested using the partial least squares structural equation modelling (PLS-SEM) technique and the model was validated with the “PLSpredict” procedure.

The results revealed that coercive and mimetic pressures significantly influence BIM adoption in SMEs while normative pressures have the strongest influence on BIM in SMEs. Also, BIM awareness is an important predictor of BIM adoption. The findings also shed light on the influence of firmographics on BIM awareness and adoption in Nigerian SMEs.

The study empirically validates the applicability of INT and highlights that BIM adoption is not only influenced by internal responses to the need for efficiency but also by external pressures. It implies a clear need for intentional isomorphic pressures in driving BIM adoption in SMEs. The study employs the INT to explain a phenomenon that has not been theoretically explored in the context of SMEs in developing economies. Lastly, the study provided valuable insights into driving BIM adoption, together with the effective practical implications for implementation and potential research areas for further studies.

Online employee reviews have emerged as a crucial information source for business managers to evaluate employee behavior and firm performance. The purpose of this paper is to test the relationship between employee personality traits, derived from online employee reviews and job satisfaction and turnover behavior at the individual level.

The authors apply text-mining techniques to extract personality traits from online employee reviews on Indeed.com based on the Big Five theory. They also apply a machine learning classification algorithm to demonstrate that incorporating personality traits can significantly enhance employee turnover prediction accuracy.

Personality traits such as agreeableness, conscientiousness and openness are positively associated with job satisfaction, while extraversion and neuroticism are negatively related to job satisfaction. Moreover, the impact of personality traits on overall job satisfaction is stronger for former employees than for current employees. Personality traits are significantly linked to employee turnover behavior, with a one-unit increase in the neuroticism score raising the probability of an employee becoming a former employee by 0.6%.

These findings have implications for firm managers looking to gain insights into employee online review behavior and improve firm performance. Online employee review websites are recommended to include the identified personality traits.

This study identifies employee personality traits from automated analysis of employee-generated data and verifies their relationship with employee satisfaction and employee turnover, providing new insights into the development of human resources in the era of big data.

Public–private partnerships (PPPs) are notoriously prone to disputes among stakeholders, some of which may unduly jeopardize contract performance. Contract disputes arising in Iran are often due to inefficiency of PPP concession agreements and practice. This study presents a causal-predictive model of the root causes and preventive measures for inter-organization disputes to enhance the likelihood of achieving desirable performance in PPP projects.

A theoretical “causal-predictive” model was developed with fourteen hypotheses based on extant literature and contractual agency theory, which resulted in the creation of a pool of 110 published items. Data were obtained from a questionnaire survey with 75 valid responses, completed by 4 stratified groups of Iranian PPP experts. Partial least square structural equation modeling (PLS-SEM) was used for validating the proposed model via a case study.

Results reveal that the main three factors of PPP desirable performance are as follows: on-time project completion, high quality of activities/products and services for public satisfaction. Further, the most influential factors of the lifecycle problems, construction stage, and preferred risk allocation included risk misallocation, improper payment mechanism and failure to facilitate a timely approval process.

For researchers, the findings contribute to the theory of contractual agency; specifically, how different influences among the model's elements lead to better PPP performance. In practical terms, proposed outcome-based strategies will inform PPP stakeholders to avoid dispute occurrence and thus improve the time, quality and services of projects.

The authors compare sentiment level with sentiment shock from different angles to determine which measure better captures the relationship between sentiment and stock returns.

This paper examines the relationship between investor sentiment and contemporaneous stock returns. It also proposes a model of systems science to explain the empirical findings.

The authors find that sentiment shock has a higher explanatory power on stock returns than sentiment itself, and sentiment shock beta exhibits a much higher statistical significance than sentiment beta. Compared with sentiment level, sentiment shock has a more robust linkage to the market factors and the sentiment shock is more responsive to stock returns.

This is the first study to compare sentiment level and sentiment shock. It concludes that sentiment shock is a better indicator of the relationship between investor sentiment and contemporary stock returns.

This study investigates the efficiency of value relevance and faithful representation when stock market price derivates from its firm value to the investigated IT companies listed in FTSE Taiwan 50. The empirical investigation reveals one financial indicators: Return on equity (ROE) has explanatory ability among seven financial indicators, earnings per share (EPS), book value (BV), dividend yield (Div.), price–earnings ratio (P/E), ROE, return on assets (ROA), and return on operating asset (ROOA) to both sampled companies, United Microelectronics Corporation, UMC, (2303) and Taiwan Semiconductor Manufacturing Company Limited, TSMC, (2330). Furthermore, the empirical results indicate that the higher order moments, skewness and kurtosis, of price deviation do not provide a reliable prediction or explanatory power for stock price trends.

Microenterprises in emerging economies are known to operate in turbulent and resource-scarce environments. We test our hypothesis that a more comprehensive “Integrated Capital-Based Model” (ICBM) is needed when explaining the performance of microenterprises in such an environment. The model combines traditionally researched financial, human and social capital with more recently emphasized psychological and cognitive capital, providing greater explanatory power than models using only the traditional types of capital.

We use a pooled linear regression to analyze an existing survey of more than 900 independent business owners who were interviewed seven times between 2008 and 2012 in the Accra and Tema marketplaces in Ghana. We measure the performance of microenterprises using three dependent variables (revenue, profits, and productivity). We contrast the explanatory power of ICBM models against the more traditional models.

The ICBM has significantly higher levels of explanatory power over the traditional models in examining the performance of these microenterprises. These results highlight the importance of psychological and cognitive capital in emerging economies.

We advocate for a more comprehensive view of capital as shown in our ICBM. However, the data were gathered only in an urban setting, which limits the generalizability to rural parts of emerging economies.

These findings suggest the utility of government and appropriate agencies finding ways to enhance the level of psychological and cognitive capital of microenterprise owners.

This paper's originality stems from hypothesizing and empirically confirming the higher predictive efficacy of ICBM against more traditionally researched capital sources.

The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is general market-wide sentiments, while the second is biased approaches toward specific assets.

To achieve the goal, the authors conducted a multi-step analysis of stock returns and constructed complex sentiment indices that reflect the optimism or pessimism of stock market participants. The authors used panel regression with fixed effects and a sample of the US stock market to improve the explanatory power of the three-factor models.

The analysis showed that both market-level and stock-level sentiments have significant contributions, although they are not equal. The impact of stock-level sentiments is more profound than market-level sentiments, suggesting that neglecting the stock-level sentiment proxies in asset valuation models may lead to severe deficiencies.

In contrast to previous studies, the authors propose that investor sentiments should be measured using a multi-level factor approach rather than a single-factor approach. The authors identified two distinct levels of investor sentiment: general market-wide sentiments and individual stock-specific sentiments.

Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams.

This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology.

Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other.

The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations.

The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team.

The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams.

The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.

Although the value effect is comprehensively investigated in developed markets, the number of studies examining the Vietnamese stock market is limited. Hence, the first aim of this research is to provide empirical evidence regarding returns on value and growth stocks in Vietnam. The second aim is to explain abnormal returns on Vietnamese growth and value stocks using both risk-based and behavioral points of view.

From the risk-based explanation, the Capital Asset Pricing Model (CAPM), Fama–French three- and five-factor models are estimated. From the behavioral explanation, to construct the mispricing factor, this paper relies on the method of Rhodes-Kropf et al. (2005), one of the most popular mispricing estimations in the financial literature with numerous citations (Jaffe et al., 2020).

While the CAPM and Fama–French multifactor models cannot capture returns on growth and value stocks, a three-factor model with the mispricing factor has done an excellent job in explaining their returns. Three out of four Fama–French mimic factors do not contain additional information on expected returns. Their risk premiums are also statistically insignificant according to the Fama–MacBeth second-stage regression. By contrast, both robustness tests prove the explanatory power of a three-factor model with mispricing. Taken together, mispricing plays an essential role in explaining returns on Vietnamese growth and value stocks, consistent with the behavioral point of view.

There are several value-enhancing aspects in the field of market finance. First, this paper contributes to the literature of value effect in emerging markets. While the evidence of value effect is obvious in numerous developed as well as international markets, both growth and value effects are discovered in Vietnam. Second, the explanatory power of Fama–French multifactor models is evaluated in the Vietnamese context. Finally, to the best of the author's knowledge, this is the first paper that incorporates the mispricing estimation of Rhodes-Kropf et al. (2005) into the asset pricing model in Vietnam.