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This study examines the relationship between investors' familiarity bias, including the home bias and endowment bias, and their financial situations, expectations and personal characteristics.

Using the 2019 Survey of Consumer Finances, the authors utilize an ordinary least squares regression to identify the presence of endowment bias and home bias in individual investors' direct stock holdings and use a Heckman selection model to examine determinants of the extent of endowment bias and home bias.

This study finds that investors with higher income and more education, men, non-white investors and people with greater risk tolerance are actually at a greater risk of endowment bias. This study also identifies a profile of investors that are more likely to have a home bias: with less financial sophistication, lower net worth, older, female, more risk-averse, with a positive expectation about the domestic economy and a relatively shorter investment horizon.

This paper is among the first to use US investors' directly reported stock holdings to examine the individual characteristics that are correlated with greater familiarity bias, providing financial professionals with information about how to allocate their limited time in providing education to a variety of clients.

This paper seeks to demonstrate the value of using social media to capture fashion trends, including the popularity of specific features of clothing, in order to improve the speed and accuracy of supply chain response in the era of fast fashion.

This study examines the role that text mining can play to improve trend recognition in the fashion industry. Researchers used n-gram analysis to design a social media trend detection tool referred to here as the Twitter Trend Tool (3Ts). This tool was applied to a Twitter dataset to identify trends whose validity was then checked against Google Trends.

The results suggest that Twitter data are trend representative and can be used to identify the apparel features that are most in demand in near real time.

The 3Ts introduced in this research contributes to the field of fashion analytics by offering a novel method for employing big data from social media to identify consumer preferences in fashion elements and analyzes consumer preferences to improve demand planning.

The 3Ts improves forecasting models and helps inform marketing campaigns in the apparel retail industry, especially in fast fashion.

Research on online user privacy shows that empirical evidence on how privacy literacy relates to users' information privacy empowerment is missing. To fill this gap, this paper investigated the respective influence of two primary dimensions of online privacy literacy – namely declarative and procedural knowledge – on online users' information privacy empowerment.

An empirical analysis is conducted using a dataset collected in Europe. This survey was conducted in 2019 among 27,524 representative respondents of the European population.

The main results show that users' procedural knowledge is positively linked to users' privacy empowerment. The relationship between users' declarative knowledge and users' privacy empowerment is partially supported. While greater awareness about firms and organizations practices in terms of data collections and further uses conditions was found to be significantly associated with increased users' privacy empowerment, unpredictably, results revealed that the awareness about the GDPR and user’s privacy empowerment are negatively associated. The empirical findings reveal also that greater online privacy literacy is associated with heightened users' information privacy empowerment.

While few advanced studies made systematic efforts to measure changes occurred on websites since the GDPR enforcement, it remains unclear, however, how individuals perceive, understand and apply the GDPR rights/guarantees and their likelihood to strengthen users' information privacy control. Therefore, this paper contributes empirically to understanding how online users' privacy literacy shaped by both users' declarative and procedural knowledge is likely to affect users' information privacy empowerment. The study empirically investigates the effectiveness of the GDPR in raising users' information privacy empowerment from user-based perspective. Results stress the importance of greater transparency of data tracking and processing decisions made by online businesses and services to strengthen users' control over information privacy. Study findings also put emphasis on the crucial need for more educational efforts to raise users' awareness about the GDPR rights/guarantees related to data protection. Empirical findings also show that users who are more likely to adopt self-protective approaches to reinforce personal data privacy are more likely to perceive greater control over personal data. A broad implication of this finding for practitioners and E-businesses stresses the need for empowering users with adequate privacy protection tools to ensure more confidential transactions.

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

The purpose of this study is to analyse numerous aspects affecting residential property price in Malaysia against macroeconomics issues such as gross domestic product (GDP), exchange rate, unemployment and wage.

The hedonic pricing model has been adopted as econometric model for this research to investigate the relationship between residential property price against macroeconomics indicator. The data for residential property price and macroeconomic variables were collected from 1991 to 2019. Multiple linear regression had been adopted to find the relationship between the dependent and independent variables.

The result shows that the GDP has a significant positive impact on residential property price, while exchange rate has no significant impact although it was positive. In addition, the unemployment rate has a significant impact on the residential property price and has a negative relationship. Similar to the wage that shows the negative relationship with residential property prices. Moreover, during the pandemic COVID-19 in Malaysia, this research shows a more transparent view of the relationship between residential property price and the macroeconomic issues of GDP, exchange rate, unemployment and wage.

The findings of this research found that macroeconomics issue cannot be eliminated due to Malaysia is a developing country, and there will always be an issue that will happen, but the issues can be reduced to maximise the advantages, e.g. during COVID-19, the solution to fight against COVID-19 were crucial and weaken the macroeconomics issues.

This paper aims to conduct a systematic review of the literature on the quality of integrated reports (IR) and highlight the gaps in the existing research to provide directions and suggestions for future research.

This study was conducted through a systematic literature review using content analysis based on 40 papers from the Scopus, Web of Science and EBSCOhost databases on IR quality. While reading the full-text papers, the authors found six additional papers referenced by the literature being reviewed that were relevant to IR quality. Thus, there were 46 papers in the final review. The analysis begins with the definition and dimension of IR quality and theoretical lenses. Furthermore, this study outlines constructs or variables used in the previous literature.

The authors found that most studies used the quantitative method (41 papers or 89%). Five papers in the literature used qualitative methods (11%). Most researchers (34 papers or 72%) defined IR quality as consistent with the International Integrated Reporting Council framework, specifically the eight content elements. In particular, with the constructs that make up the quality of the IR, variations between researchers were found. Furthermore, there were some gaps that could be the directions for future research.

The literature that provides academic knowledge about IR quality is still limited, and research on IR is still growing. The literature review conducted by this study can provide an overview of the current research positions on the quality of IR and directions for future research in this area.

This study intends to show corporate executives a framework demonstrating the quality of corporate reporting. It can impact not only investors as a specific stakeholder group but also other stakeholder groups.

To the best of the authors’ knowledge, this study is the first literature review to examine the quality of IR, thus providing a map of current research to suggest directions for future research. Most of the previous literature reviews have been focused on integrated reporting (IR) in general and not quality.

Financial stability has become a focal point for central banks since the global financial crisis. However, the optimal mix between monetary and financial stability policies remains unclear. In this study, the “soft” approach to such policy mix was tested – how often monetary policy (in inflation reports) analyses financial stability issues. This paper aims to discuss the aforementioned objective.

A total of 648 inflation reports published by 11 central banks from post-communist countries in 1998-2019 were reviewed using a text-mining method.

Results show that financial stability topics (mainly cyclical aspects of systemic risk) on average account for only 2%of inflation reports’ content. Although this share has grown somewhat since the global financial crisis (in CZ, HU and PL), it still remains at a low level. Thus, not enough evidence was found on the use of a “soft” policy mix in post-communist countries.

Given the strong interactions between price and financial stability, this paper emphasizes the need to increase the attention of monetary policymakers to financial stability issues.

The study combines two research areas, i.e. monetary policy and modern text mining techniques on a sample of post-communist countries, something which to the best of the authors’ knowledge has not been sufficiently explored in the literature before.

The reason for concern is the rapid decline in loans to small enterprises which is critical to their performance, compared to large businesses following the periods of banking reformations in Nigeria. Thus, the purpose of this paper is to investigate the influence of risk perception on bank lending behaviour to small enterprises. It also investigates the impact of government intervention, consolidation and recapitalization on the relationship between risk perception and bank lending behaviour to small enterprise.

This study empirically analysed (ordinary least square) secondary data obtained from the Central Bank of Nigeria Statistical Bulletins, Annual Statement of Accounts covering the period 1992–2020.

The results show that the absence of government interventions and the presence of banking reformations have statistically negative significant effect on bank lending to small enterprises. The findings challenge the argument that generally assumes risk aversion of banks towards small enterprise lending because of small enterprise’s inability to prove their credit worthiness and consequently constraining access to finance to the sector. Instead, the results and analysis from this study found theoretical support for the variation of bank behaviour in lending to small enterprises depending on the status of wealth of the financial system.

A key lesson from this study for government concerned about promoting performance of the small enterprise sector is that regulating and enforcing lending requirements on access to debt financing of the sector is necessary if constraints in access debt finance is to be eliminated. Second, while strategies such as bank consolidation, recapitalization may help strengthen and make financially robust the banking system; it places the banks in a gain position where losses looms to them than gain.

This study challenges the argument that generally assumes risk aversion of banks towards small enterprise lending as a result of inability to prove their credit worthiness and consequently constraining access to finance to the sector. Instead, the results and analysis from this study reveal a variation in lending to small enterprises and suggests that the position of the bank in relation to a reference point influences how risk is perceived by the bank and thus impacts on their risk decision-making behaviour.

Measuring shared value (SV) created in innovation ecosystems (IEs) is increasingly relevant but complex, given the multidimensional and multiactor nature of both concepts, which challenges traditional performance measurement systems (PMSs). Moving from this gap, the authors propose an integrated approach to extend the balanced scorecard (BSC) for measuring and monitoring SV creation at IE level.

The proposed approach combines the most recent contributions on PMS in IEs and SV to define perspectives and dimensions that are better suited to deal with the nature of both IEs and SV. The approach is also applied to the real case (Alpha) of an Italian IE through a step wise method. Starting from the IE vision, the authors identify in the strategy map the specific objectives related to each perspective/dimension combination and then associate a performance indicator with each objective.

The resulting SV BSC is composed of indicators interconnected along different perspectives and dimensions. The application of the approach to the real case proves its feasibility and highlights characteristics, advantages and disadvantages of the SV BSC when used at IE level. The authors also provide guidelines for its application to other IEs.

The study contributes to the research on PMS by introducing and applying to a real case an integrated approach to assess SV in IEs, overcoming the shortcomings of PMS framed for single firms. It can be of interest for both researchers in the field of ecosystems value creation and practitioners managing or promoting such complex structures.

Mean absolute percentage error (MAPE) is the primary forecast evaluation metric in hospitality and tourism research; however its main shortcoming is that it is asymmetric. The asymmetry occurs due to over or under forecasts that introduce bias into forecast evaluation. This study aims to explore the nature of asymmetry and designs a new measure, one that reduces the asymmetric properties while maintaining MAPE’s scale-free and intuitive interpretation characteristics.

The study proposes and tests a new forecasting accuracy measure for hospitality revenue management (RM). A computer simulation is used to assess and demonstrate the problem of asymmetry when forecasting with MAPE, and the new measures’ (MSapeMER, that is, Mean of Selectively applied Absolute Percentage Error or Magnitude of Error Relative to the estimate) ability to reduce it. The MSapeMER’s effectiveness is empirically validated by using a large set of hotel forecasts.

The study demonstrates the ability of the MSapeMER to reduce the asymmetry bias generated by MAPE. Furthermore, this study demonstrates that MSapeMER is more effective than previous attempts to correct for asymmetry bias. The results show via simulation and empirical investigation that the error metric is more stable and less swayed by the presence of over and under forecasts.

It is recommended that hospitality RM researchers and professionals adopt MSapeMER when using MAPE to evaluate forecasting performance. The MSapeMER removes the potential bias that MAPE invites due to its calculation and presence of over and under forecasts. Therefore, forecasting evaluations may be less affected by the presence of over and under forecasts and their ability to bias forecasting results.

Hospitality RM should adopt this measure when MAPE is used, to reduce biased decisions driven by the “asymmetry of MAPE.”

The MAPE error metric exhibits an asymmetry problem, and this paper proposes a more effective solution to reduce biased results with two major methodological contributions. It is first to systematically study the characteristics of MAPE’s asymmetry, while proposing and testing a measure that considerably reduces the amount of asymmetry. This is a critical contribution because MAPE is the primary forecasting metric in hospitality and tourism studies. The second methodological contribution is a procedure developed to “quantify” the asymmetry. The approach is demonstrated and allows future research to compare asymmetric characteristics among various accuracy measures.