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Despite the benefits of international diversification, the home equity bias phenomenon is well documented in the portfolio choice literature. The purpose of this paper is to investigate whether the same investment behavior applies to domestic socially responsible investments (SRIs) where ethical screenings should be the selection criteria.

The authors apply the model by Coval and Moskowitz (1999), Grinblatt and Keloharju (2001) and Agarwal and Hauswald (2010) to uncover the effect of distance relative to screenings on SRI domestic portfolio choice. For the first time, the authors test the robustness of distance effect by using time bias, which is the travel time between the fund manager and the company’s headquarter.

The authors find that SRIs exhibit a strong preference for locally headquartered firms. After controlling for screening activity and other fund characteristics, the authors still find a strong distance bias in SRI fund portfolio decision-making. The authors find that this bias is mostly observed in SRI fund with social screening and that fund holding characteristics determine the propensity of fund managers to invest locally. The results suggest that the local bias puzzle exists in SRI.

This study provides avenue for future research to examine whether the same local bias is found in SRI investment in other countries where they have different characteristics and behavior. Also, the evidence that local bias exists in SRI investment may need further analysis as to whether this is conflicting with the objectives of SRI, which focus more on ethical beliefs.

The results suggest that many local firms in the same city currently held by an SRI fund will not be held by this fund if it is in another city. The implications of the findings are that geographic proximity, along with ethical screenings, is an important dimension to how SRI fund invests.

This study is the first that examines local bias in SRI funds by using portfolio holding data.

This paper aims to analyze how judgment bias (optimism vs pessimism) and temporal distance influence self-control decisions. This research also analyzes the mediating role of perceived control on judgment bias and temporal distance.

Three studies (one laboratory and two online experiments) analyze how judgment bias and temporal distance influence self-control decisions on consumers’ willingness to pay.

The findings uncover an important boundary condition of temporal distance on self-control decisions. In contrast to previous research, the findings indicate that individuals exposed to optimism (vs pessimism) bias display more self-control in the future and make choices that are more indulgent in the present. The findings also reveal that perceived control mediates the effects of judgment bias and temporal distance.

The findings help managers to adapt short- and long-term marketing efforts, based on consumers’ momentary judgment biases and on their chronic judgment bias orientation.

This research contributes to the literature on self-control and temporal distance, showing that judgment bias reverses previous research findings on self-control decisions.

The purpose of this paper is twofold to examine the factors that contribute to local bias of venture capital in China and to explore the relationship between local bias and performance of venture capital institutions.

Local bias was measured in line with the model developed by Cumming and Dai (2010). Regression techniques were performed for our long-term cross-sectional data to analyse the potential determinants of local bias. This is followed by the Probit model to test the relationship between local preference and successful exit.

The overall finding indicated that local bias in China increased over time. The stiff competition among venture capital institutions reduced local bias, but the enhanced innovation capabilities of a particular geographical area amplified local bias because of the knowledge spillover effect. Finally, the results suggested that venture capital institutions with less local bias enjoy a greater likelihood of making successful exits.

This study used successful venture capital exit as a proxy for venture capital institution’s performance because of the unavailability of information such as internal rate of return. Future research should try to adopt other way of measuring venture capital institution’s performance.

This study sheds light on the various possible causes of local bias that the policymakers need to be aware of. Despite the rapid rise of China’s venture capital market in recent years, venture capital institutions have yet to make inroads into the local high-tech industry. This study implies to the policymakers that to reverse this trend, they should formulate policies that foster the long-term performance of venture capital institutions, mitigate the severity of local bias and raise the competitiveness of the Chinese venture capital market.

Because of data limitations, there is currently lack of prior empirical research on local bias of Chinese venture capital institutions based on large-scale data. This study intends to fill the gap.

Top-management-teams (TMTs) and chief executive officers (CEOs) dealing with internationalization are naturally predisposed to deal with space, so they will consult “spatial knowledge.” The purpose of this paper is to offer a conceptual description of spatial knowledge used by TMTs/CEOs and to describe how the use of spatial knowledge can be triggered and the resulting biases that arise from it. The description of spatial knowledge is also discussed in relation to core international business (IB) theories/models.

This is a conceptual study.

TMTs/CEOs use spatial knowledge for internationalization decisions. This spatial knowledge is “declarative” because it involves knowledge of places and associated characteristics or attributes, “configurational” because it involves knowledge of various types of relative positions and proximities between places and “procedural” because it involves knowledge of how to structure transactions, operate or organize interdependencies between locations. Additionally, TMTs/CEOs individually have spatial knowledge that is uniquely distorted. Then, finally, when TMTs/CEOs consult spatial knowledge to identify international opportunities or solutions, their search process may entail distance and directional biases as a result of their spatial knowledge.

This is the first paper to introduce the notion of “spatial knowledge” to the research on TMT/CEO experiences and internationalization and IB research in general.

Hybrid systems as the next generation of intelligent applications within the field of mass appraisal and valuation are investigated. Motivated by the obvious limitations of paradigms that are being used in isolation or as stand‐alone techniques such as multiple regression analysis, artificial neural networks and expert systems. Clearly, there are distinct advantages in integrating two or more information processing systems that would address some of the discrete problems of individual techniques. Examines first, the strategic development of mass appraisal approaches which have traditionally been based on “stand‐alone” techniques; second, the potential application of an intelligent hybrid system. Highlights possible solutions by investigating various hybrid systems that may be developed incorporating a nearest neighbour algorithm (k‐NN). The enhancements are aimed at two major deficiencies in traditional distance metrics; user dependence for attribute weights and biases in the distance metric towards matching categorical variables in the retrieval of neighbours. Solutions include statistical techniques: mean, coefficient of variation and significant mean. Data mining paradigms based on a loosely coupled neural network or alternatively a tight coupling with genetic algorithms are used to discover attribute weights. The hybrid architectures developed are applied to a property data set and their performance measured based on their predictive value as well as perspicuity. Concludes by considering the application and the relevance of these techniques within the field of computer assisted mass appraisal.

While some studies have found that cognitive biases are detrimental to entrepreneurial performance, others have conjectured that cognitive biases may stimulate entrepreneurial action. This study uses a typology of availability and representative heuristics to examine how two patterns of biases affect entrepreneurial performance. Drawing on ideas from cognitive science, this study predicts that various levels of biases in each pattern stimulate entrepreneurial behavior and performance.

A profile-deviation approach was employed to analyze data from 253 entrepreneurs and zero-truncated Poisson regression and the zero-truncated negative binomial regression to test hypotheses.

This study finds some positive associations between a particular level of cognitive biases in each of the two patterns and entrepreneurial behavior and performance. Results show that the patterns of biases often stimulate and never hurt entrepreneurial behavior and performance. The opposite holds for a lack of cognitive biases, which hurts and never stimulates entrepreneurial behavior and performance.

This study examines patterns of cognitive biases of entrepreneurs instead of single biases. The study broadens the perspective on the heuristics and cognitive biases of entrepreneurs by examining patterns of biases emanating from the availability and the representativeness heuristic that make a difference for entrepreneurial behavior and performance. The study also brings the “great rationality debate” closer to the entrepreneurship field by showing that a normative rule based on statistics and probability theory does not benefit entrepreneurial behavior and performance.

Technology is sometimes used to support assessments of academic research in the form of automatically generated bibliometrics for reviewers to consult during their evaluations or by replacing some or all human judgements. With artificial intelligence (AI), there is increasing scope to use technology to assist research assessment processes in new ways. Since transparency and fairness are widely considered important for research assessment and AI introduces new issues, this review investigates their implications.

This article reviews and briefly summarises transparency and fairness concerns in general terms and through the issues that they raise for various types of Technology Assisted Research Assessment (TARA).

Whilst TARA can have varying levels of problems with both transparency and bias, in most contexts it is unclear whether it worsens the transparency and bias problems that are inherent in peer review.

This is the first analysis that focuses on algorithmic bias and transparency issues for technology assisted research assessment.

The scope of this research is to investigate performing loan efficiency for fifty European banks during the period 2008–2017.

The study is structured as a two-stage analysis of performing loan efficiency and its driving factors. In the first stage of the proposed methodology “Data Envelopment Analysis” is used to estimate performing loan efficiency for each bank included in the sample. A bootstrap statistical procedure enhances the findings. In the second stage, the impact of other factors on the efficiency scores of loan performance using tobit regression is investigated.

The results are consistent with the findings of the individual banks' financial analyses. According to the findings of DEA implementation, the evaluated banks may enhance their cost efficiency by 39% on average. In addition, the results indicate that loan efficiency performance improves after 2015, coinciding with the business cycle's upward trend. The tobit regression is employed in the second stage to examine the influence of bank-related and macroeconomic factors on banks' loan management efficiency. According to the findings of the tobit regression, three factors, namely the capital adequacy ratio, GDP per capita and managerial inefficiency, have a substantial influence on performing loan efficiency.

This research investigates the effectiveness of European economic policy in protecting the European banking system from the consequences of the sovereign debt crisis in several euro area members. The results highlight the distance of the Eurozone from the level of the ‘optimal currency area’.