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The diversity of perspectives means that one can find many factors and models of proenvironmental behavior. However, they typically suffer from limitations and varying degrees of validity in specific contexts, suggesting that today the prime goal should be to learn and improve the models which have been already developed. In this study, the authors build on the model for predicting proenvironmental behavior developed by Oreg and Katz-Gerro (2006), namely one of the most comprehensive cross-national proenvironmental behavior models and one of the few not to be limited to either a local or single-country context or specific proenvironmental behavior.

By using the statistical matching technique, the authors merged data from two existing databases without common identifiers – the International Social Survey Program (environmental module) and the European Social Survey (Round 5). The resulting multinational data concerning 9,710 observations enabled a replication with extensions of Oreg and Katz-Gerro's (2006) proenvironmental behavior model that incorporates newly added Schwartz's theory of human values. To achieve the study's main objective, that is, to present improvements to the original model of proenvironmental behavior, the authors used structural equation modeling (SEM) procedures to estimate four competing models in the R program.

This study implies that Schwartz's individually measured motivational types of values (benevolence [BE], universalism [UN], self-direction [SD]) are predictors of people's proenvironmental behavior, while his conceptualization of post-materialism yields a better model fit than Inglehart's country-level post-materialism scores. The results also corroborate previous findings that post-materialist values can stimulate proenvironmental behaviors through attitudes, perceived behavioral control and intentions. The present study reveals that proenvironmental attitudes did not change substantially in the 10-year period, even though the world's environmental and sustainability challenges have largely increased. Surprisingly, the mean value of several of the perceived threat variables even decreased.

The authors externally validate one of the most comprehensive proenvironmental behavior models by reproducing it using new multinational large-sample data with nearly 10,000 observations collected 10 years later. The most significant addition to the original model introduced in the current study is the inclusion of Schwartz's motivational types of values, which are measured at the individual level, namely BE, UN and SD. The authors also extend the model by adding proenvironmental behavior measures and group the construct into three latent variables: saving natural resources, green purchasing and environmental activism.

This study examines the adoption drivers of scale-appropriate mechanization in Nepal's maize-based farming systems. The authors also assess the contribution of scale-appropriate mechanization to the United Nations Sustainable Development Goals (SDGs) of zero hunger (SDG2) and no poverty (SDG1).

Propensity score matching and doubly robust inverse probability-weighted regression adjusted methods were applied to estimate the effects of mini-tiller adoption. These methods control the biases that arise from observed heterogeneities between mini-tillers users and nonusers.

The study findings show that farm size, labor shortages, draft animal scarcity, market proximity, household assets and household heads' educational level influence the adoption of mechanization in Nepal. Mechanized farms exhibited enhanced maize productivity, profits and household food self-sufficiency. Reduced depth and severity of poverty were also observed. Nevertheless, these effects were not uniform; very small farms (≤0.41 ha) facing acute labor shortages benefited the most.

The study results suggest that policymakers in developing nations like Nepal may wish to expand their emphasis on scale-appropriate mechanization to improve farm productivity and household food security, reduce poverty and contribute to the SDGs.

This first-of-its-kind study establishes the causal effects between scale-appropriate farm mechanization and SDG1 (no poverty) and SDG2 (zero hunger) in a developing nation.

Catastrophic supply chain disruptions can significantly damage the operational and financial performance of firms. While a growing body of literature on supply network structures has studied what influences supply networks' vulnerability to supply chain disruptions and capability to recover from them, it remains unclear how supply network structures change after major supply chain disruptions. We aim to provide an understanding of how these changes occur.

Using a natural experiment approach and supply network data from Factset, this study investigates how firms' supply network structures change after experiencing the catastrophic supply chain disruptions caused by the 2011 Tohoku earthquake and tsunami in Japan. We capture post-earthquake supply network changes using the measures of degree centrality and ego network density.

The results of the analysis suggest that compared to unaffected firms, the affected firms experience changes in their supply network structures tending toward lower complexity measured by in-degree centrality, out-degree centrality and ego network density.

This study contributes to social network theory and the complex adaptive supply network literature by providing empirical evidence of structural changes in supply networks after catastrophic supply chain disruptions. A managerial contribution is made by providing a reflection on why these changes might be occurring and alert firms to the challenges of managing complexity in their supply networks.

This paper investigates the stock market reaction to three different events related to the UEFA Champions League – the announcements of draws, odds and match results. The aim of the paper is to test whether these events are informative for stock market operators, i.e. whether they produce abnormal returns.

Applying the event study methodology, the authors investigate the stock market reaction before (at two events: the draw date and on the release of betting odds) and after the matches of 11 listed soccer teams in the period 2003–2019. The authors also conduct OLS regression analyses in order to disentangle the impact of firm specific variables and match characteristics on cumulative abnormal returns.

This paper finds that match outcomes affect the stock market performance of listed teams, while the announcements of draws and odds do not. More specifically, the market does not consider match outcomes involving wins and ties as informative events, while it penalizes losing teams. Moreover, investor reactions to events related to the UCL competition depend more on match characteristics than on company specific variables.

The study enriches the ongoing debate about the impact of soccer team results on stock market performance in several ways: using the widest time span ever adopted in this area; focusing on UCL, which is the most important soccer competition played by private clubs; disentangling for the first time the effects of draws, odds release and sporting outcome on stock returns of listed soccer clubs.

The article takes its point of departure in the current labor market debate on immigration policy and attempts to explore the way private labor market mediators involved in the integration of immigrants contribute to reproducing cultural stereotypes reminiscent of colonial ideas.

Findings are based on respondent-driven interviews with nine labor market mediators (seven women and three men) from eight private labor market agencies in Sweden.

The findings showed that the private labor market mediators put their trust in the neoliberal system and rely on the colonial discourses when convincing immigrants to assimilate into the dominant culture. At the same time, the findings also show that there is no evidence that the assimilating activities they offer will lead to long-term employment.

Given that migration across national borders has become a common phenomenon, the author suggests a call for critical reflections on the taken-for-granted notions of both self and others and the influences colonial discourses have on integration.

The author would suggest a shift of focus from immigrants' cultural adjustment to society's and employers' responsibilities and readiness to adjust to prevailing conditions.

The study contributes to the labor market literature and the diversity management literature by adding insights from public labor market mediators' experiences of the help they offer immigrants to enter the labor market. It shows how mediators make use of dominating discourses to secure cultural hegemony.

In this article, the authors draw-upon an extended unified theory of acceptance and use of technology (UTAUT) and propose a research model involving performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC) and competitive pressure (CP) as potential salient factors explaining the adoption of digitalization in European SMEs. The authors also postulate that there may be cross-cultural differences, thereby leading us to include the country as a moderator in the model.

The authors validate this model with a cross-cultural sample involving 188 owner-managers from the Czech Republic and Slovakia and through the partial least square structural equation modeling (PLS-SEM) techniques as well as multi-group analysis.

The results using the study’s global dataset indicate that PE, FC and CP significantly affect owner-managers intentions toward digitalization in SMEs. The authors’ application of the multi-group analysis also suggests that although the two countries differ in digitalization adoption intention, the differences are statistically insignificant. In the conclusion, the authors highlight several implications these findings have for theory and practice.

The authors recommend that the providers of emerging digital technologies should improve on the performance features of those technologies and ensure they are relevant to the SMEs. By doing so, the adoption of digitalization will grow, because owner-managers of SMEs will have the confidence that adopting such technologies will improve their operations. Second, SMEs are required to provide adequate organizational and technical infrastructure to support digitalization adoption.

Aside from being among the few attempts to extend the explanatory power of UTAUT with PE, EE, FC and CP in investigating digitalization adoption in SMEs context, this study also validates its model with rigorous methodological approach as well as three datasets (global, Czech Republic and Slovakia) thereby strengthening the validity of the results.

This paper tests whether Bayesian A/B testing yields better decisions that traditional Neyman-Pearson hypothesis testing. It proposes a model and tests it using a large, multiyear Google Analytics (GA) dataset.

This paper is an empirical study. Competing A/B testing models were used to analyze a large, multiyear dataset of GA dataset for a firm that relies entirely on their website and online transactions for customer engagement and sales.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the intellectual property fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits. Frequentist A/B testing identified fraud in bounce rate at 5% significance, and bounces at 10% significance, but was unable to ascertain fraud at the standard significance cutoffs for scientific studies.

None within the scope of the research plan.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the IP fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits.

Bayesian A/B testing can derive economically meaningful statistics, whereas frequentist A/B testing only provide p-value’s whose meaning may be hard to grasp, and where misuse is widespread and has been a major topic in metascience. While misuse of p-values in scholarly articles may simply be grist for academic debate, the uncertainty surrounding the meaning of p-values in business analytics actually can cost firms money.

There is very little empirical research in e-commerce that uses Bayesian A/B testing. Almost all corporate testing is done via frequentist Neyman-Pearson methods.

This paper aims to explore how parasocial relationships with Korean celebrity endorsers on social media result in brand credibility and loyalty.

The participants were identified through a purposive sampling approach, and they were composed of consumers who purchased Korean-celebrity-endorsed products and services of a telecommunications company. The hypothesized relationships were gauged using a predictive approach as a research design via partial least squares (PLS) path modeling.

The findings show that all hypothesized relationships are supported. In particular, social media interaction was found to have a substantial, positive and significant effect on self-disclosure. Moreover, self-disclosure has a considerably significant and direct effect on parasocial relationships and was found to indirectly affect the link between social media interactions and parasocial relationships. The results further reveal that social media interactions and parasocial relationships predict source trustworthiness, leading to brand credibility and loyalty.

To the best of the authors’ knowledge, the present undertaking is the only study that examined how parasocial relationships on social media are built when foreign celebrities, in this case, the well-known Korean popular group BTS, endorse telecommunications products and services.

La presente investigación explora cómo las relaciones parasociales con celebridades coreanas en las redes sociales generan credibilidad de marca y lealtad.

Los participantes se identificaron mediante un muestreo intencional y estaban compuestos por consumidores que compraban productos y servicios de una empresa de telecomunicaciones avalados por famosos coreanos. Las relaciones hipotetizadas se midieron utilizando un enfoque predictivo como diseño de investigación mediante un modelo de mínimos cuadrados parciales (PLS).

Los resultados muestran que todas las relaciones hipotetizadas se confirman. En particular, la interacción con los medios sociales tiene un efecto sustancial, positivo y significativo en la autodivulgación. Además, la autodivulgación tiene un efecto considerablemente significativo y directo en las relaciones parasociales y se descubrió que afecta indirectamente al vínculo entre las interacciones en los medios sociales y las relaciones parasociales. Los resultados revelan además que las interacciones en los medios sociales y las relaciones parasociales predicen la fiabilidad de la fuente, lo que conduce a la credibilidad de la marca y a la lealtad.

El presente trabajo es el único estudio que examina cómo se construyen las relaciones parasociales en los medios sociales cuando celebridades extranjeras, en este caso, el conocido grupo popular coreano BTS, promocionan productos y servicios de telecomunicaciones.

本研究探讨了在社交媒体上与韩国名人的寄生关系如何建立品牌可信度和忠诚度。

通过目的性抽样确定参与者, 包括购买韩国名人代言的电信公司产品和服务的消费者。研究设计使用偏最小二乘法（PLS）模型对假设关系进行预测测量。

研究结果表明, 所有假设关系都得到了证实。特别是, 社交媒体互动对自我披露具有实质性的、积极的和显著的影响。此外, 自我披露对寄生关系也有明显的直接影响, 并被发现间接影响社交媒体互动与寄生关系之间的联系。研究结果进一步揭示了社会化媒体互动和寄生关系能够预测来源的可信度, 从而提高品牌可信度和忠诚度。

研究结果表明, 所有假设的关系都得到了证实。特别是, 社交媒体互动对自我披露具有实质性的、积极的和显著的影响。此外, 自我披露对寄生关系也有明显的直接影响, 并被发现间接影响社交媒体互动和寄生关系之间的联系。研究结果进一步揭示了社会化媒体互动和寄生关系能够预测来源的可信度, 从而提高品牌可信度和忠诚度。

本文是唯一一篇研究外国名人在社交媒体上推广电信产品和服务时如何建立寄生社会关系的研究。

The purpose of this study is to introduce a matching function approach to analyze matching in financial reporting.

The matching function is first analyzed analytically. It is specified as a multiplicative Cobb-Douglas-type function of three categories of expenses (labor expense, material expense and depreciation). The specified matching function is solved by the generalized reduced gradient method (GRG) for 10-year time series from 8,226 Finnish firms. The coefficient of determination of the logarithmic model (CODL) is compared with the linear revenue-expense correlation coefficient (REC) that is generally used in previous studies.

Empirical evidence showed that REC is outperformed by CODL. CODL was found independent of or weakly negatively dependent on the matching elasticity of labor expense, positively dependent on the material expense elasticity and negatively dependent on depreciation elasticity. Therefore, the differences in matching accuracy between industries emphasizing different expense categories are significant.

The matching function is a general approach to assess the matching accuracy but it is in this study specified multiplicatively for three categories of expenses. Moreover, only one algorithm is tested in the empirical estimation of the function. The analysis is concentrated on ten-year time-series of a limited sample of Finnish firms.

The matching function approach provides a large set of important information for considering the matching process in practice. It can prove a useful method also to accounting standard-setters and other specialists such as managers, consultants and auditors.

This study is the first study to apply the new matching function approach.

This paper explores the evidence of a long-run co-movement between aggregate unemployment insurance spending and the labor force participation rate in the USA. The unemployment insurance (UI) program tends to expand during an economic downturn and contract during an expansion. UI may incentivize unemployment and may also facilitate better matching in the labor market. Statistical evidence of the presence of a co-movement will thus shed new light on their dynamics.

This research applies time-series econometric approach using monthly data from 1959:1 to 2020:3 to test threshold cointegration and estimate a threshold vector error-correction (TVEC) model. The estimates from the TVEC model investigating the nature of short-run dynamics.

The Enders and Siklos (2001) test find evidence of threshold cointegration between the two indicating the presence of long-run co-movement. The estimates from the TVEC model investigating the nature of short-run dynamics find evidence that the growth in aggregate UI spending and the growth in labor force participation rate adjust simultaneously to maintain the long-run co-movement above the threshold in the short run. The author also observes the same short-run dynamics for the growth in aggregate UI spending and the growth in the labor force participation rate for females.

This model is bi-variate by construction and does not address causality.

The author argues that the UI program positively impacts the female labor market outcomes, for example, better matching. This finding may explain the upward trend in the labor force participation rate for females in the USA.

The research findings may justify the transfer programs for minority and immigrants.

This is first research that analyzes the UI programs impact on the labor force participation using a macroeconometric approach. To the best of the author's knowledge, this is the first study in this genre.