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The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

The purpose of our study is to investigate the impact of corporate culture on sales order backlog.

The authors use regression analysis to examine the relation between corporate culture and the level of sales order backlog, an important leading indicator of firm performance.

Using a large panel sample of US firms for the period of 2003–2021, the authors find a significant and positive relation, suggesting that firms with strong corporate culture have a higher level of sales order backlog.

The study findings contribute to two separate areas of research: corporate culture in management literature and sales order backlog in accounting literature. Prior study has focused on the impact of corporate culture on current firm performance. This study extends prior research by investigating the impact of corporate culture on order backlog, an important leading indicator of future performance.

This study, informed by the Situational Crisis Communication Theory, aims to suggest two primary response strategies that can be used for effective internal crisis communication during a pandemic situation, such as COVID-19. The effect of base response strategies on employees' perceptions of communication quality, leadership and relational outcomes were investigated.

An online survey of full-time employees in the United States was conducted.

The findings showed that for an instructing information strategy, not all types of information were equally associated with positive employee responses in terms of perceived quality of internal communication related to the COVID-19 pandemic and transformational leadership. Specific information that employees need to know in order to safely perform daily tasks, such as organizational protocols and thorough preparation, seem to be the most needed and desired information. Adjusting information was positively associated with employee perceptions of internal communication quality and perceptions of CEO leadership. Employees' perceived quality of internal communication affected by the base crisis response strategies were positively correlated with perceptions of transformational leadership and relational outcomes (i.e. employee trust in the organization, employee perceptions of the organization's commitment to relationships with employees, employee support for organizational decision-making related to COVID-19).

This study presents important theoretical and practical insights through an interdisciplinary approach that applies the theoretical framework and relationship-oriented outcomes of public relations to public health crisis situations.

Examining multilevel effects of financial and social performance of microfinance institutions (MFIs), the authors aim to investigate microfinance mission drift from the trend effect. The authors also seek to move the literature forward by decomposing the performance variance at different levels and examining whether and how much each level of analysis matters.

Growth curve modeling and variance decomposition analysis were conducted using a dataset consisting of 17,953 observations of 2,902 microfinance institutions in 122 countries from 1999 to 2017.

The study's result shows no evidence of mission drift in the microfinance industry. While MFIs improve their economic returns, they also increase the depth of outreach. In addition, firm-level heterogeneity is the dominant effect which explains 44% of the variance in microfinance financial performance (ROA) and 39% of the variance in social performance (Depth of outreach). The country-level is more critical in explaining financial performance (ROA) than social performance (Depth of outreach), accounting for 11 and 32% of the total variance, respectively. In particular, the interplay between the country-level and organizational-category level accounts for 9 and 11% of the total variance in financial performance (ROA) and social performance (Depth of outreach), respectively.

This study’s multilevel analysis of microfinance performances moves the literature forward by responding to the debate on microfinance mission drift and providing a comprehensive overview of both social and financial performance. By focusing on the trend effect, the result of our models shows that MFIs improve both financial and social performance to fulfill dual missions. The microfinance business model becomes sustainable over time. The study's results of country effect and its interaction effect with different organizational categories reveal the prominence of a good policy design on MFI's mission fulfillment.

Using a sample of oil and gas firms in the USA, the study examines the relation between the presence of hedging and annual report readability.

The authors use regression analysis to examine the relation between the presence of hedging and annual report readability.

The authors find that annual reports of firms with the use of hedging are less readable (i.e. difficult to read and understand). The authors also find that the primary results are more pronounced for firms with a higher level of business volatility.

The study contributes to the finance literature on the use and value of hedging and to the accounting literature on the determinants of annual report readability. The Securities and Exchange Commission (SEC) has persistently asked companies to improve the readability of their disclosures to stakeholders (SEC, 1998; 2013, 2014). Hence, the study not only identifies a potential determinant (i.e. hedging) that may influence the level of readability but also supports the current regulatory policy by the SEC, which is encouraging companies to improve readability.

The purpose of the study is to investigate the impact of asset redeployability on the level of corporate cash holdings.

The authors use regression analysis to examine the relation between asset redeployability and corporate cash holdings.

Using a large panel sample of US public firms from 1990 to 2020, the authors find a significant positive relation between asset redeployability and cash, which suggests that firms with more redeployable assets hold more cash.

The authors contribute to a growing literature in accounting and finance that investigates the impact of asset redeployability on firm characteristics and also contribute to the literature on the determinants of cash holdings.

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 privacy calculus based on a single stakeholder failed to explain users' co-owned information disclosure owing to the uniqueness of co-owned information. Drawing on collective privacy calculus theory and impression management theory, this study attempts to explore the co-owned information disclosure of social network platform users from a collective perspective rather than an individual perspective.

Drawing on collective privacy calculus theory and impression management theory, this study explores the co-owned information disclosure of social network platform users from a collective perspective rather than an individual perspective based on a survey of 740 respondents.

This study finds that self-presentation and others presentation directly positively affect users' co-owned information disclosure. Also, self-presentation, others presentation and relationship presentation indirectly positively affect users' co-owned information disclosure via relationship support. Furthermore, personal privacy concern, others' privacy concern and relationship privacy concern indirectly negatively affect users' co-owned information disclosure via relationship risk.

The findings develop the theory of collective privacy calculus and impression management, which offer insights into the design of the collective privacy protection function of social network platform service providers.

The turbulent phase of COVID-19 has caused uncertainty as governments fail to develop coherent strategies for cutting emissions and are struggling to match the rhetoric of sustainable activities with actions (Barbier & Burgess, 2020; Cawthorn, Kennaugh, & Ferreira, 2021). In the recent past, firms have failed in their plans to decarbonise their key sectors such as the retail sector in the United Kingdom So far, retailers' commitment to achieving net zero emissions has been an important pledge but delivery is nowhere closer to their promises (Henriques, 2020). The firms' climate targets are not going to be met by magic as serious action is needed to fulfil the promises.

Fossil fuels have led to a drastic increase in carbon emissions in the world over the last decade. Firms championing cleaner energy and low carbon technologies are needed to cut emissions. Renewable energy sources such as wind energy can help reducing the dependency of fossil fuels (Boretti, 2020; Ebhota & Jen, 2020). Wind is an indirect form of solar energy which can provide environment-friendly option in uncertain times and can provide long-term sustainability of global economy. Solar energy technologies have the potential to decrease climate change through energy-related emissions (Li, Dai, & Cui, 2020). Increasing energy demand has initiated a focus on using hydrogen from water as a substitute for oil and fossil fuels (Boretti, 2020).

The first part of the chapter discusses theoretical perspectives of sustainable development and environmental performance with regards to three main issues: energy, water and carbon emissions, whereas the later part highlights the importance of solar technology as a low-polluted alternative to fossil fuels in the retail sector. Sustainable development of energy, water and environmental precautions such as reducing carbon emissions are of interest to wider branches of industries including retail, energy and water sector, governmental policymakers, researchers, educators and society. The purpose of this chapter is to increase the debate of the key issues of sustainable development regarding environment, energy and water in the modern times.

Joon Y. Park is one of the pioneers in developing nonlinear cointegrating regression. Since his initial work with Phillips (Park & Phillips, 2001) in the area, the past two decades have witnessed a surge of interest in modeling nonlinear nonstationarity in macroeconomic and financial time series, including parametric, nonparametric and semiparametric specifications of such models. These developments have provided a framework of econometric estimation and inference for a wide class of nonlinear, nonstationary relationships. In honor of Joon Y. Park, this chapter contributes to this area by exploring nonparametric estimation of functional-coefficient cointegrating regression models where the structural equation errors are serially dependent and the regressor is endogenous. The self-normalized local kernel and local linear estimators are shown to be asymptotic normal and to be pivotal upon an estimation of co-variances. Our new results improve those of Cai et al. (2009) and open up inference by conventional nonparametric method to a wide class of potentially nonlinear cointegrated relations.