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The accounting literature suggests that the use of accounting standards with greater quality promotes the financial reporting quality and enhances accountability. This study aims to investigate the effect of the International Public Sector Accounting Standards (IPSAS) adoption, by official development assistance (ODA) beneficiary countries, on the reported level of their perceived corruption.

We investigate a sample of ODA beneficiary countries (168 country-year observations) facing rising levels of corruption. We apply a panel regression analysis for these countries during the period from 2015 to 2018.

The findings suggest that the IPSAS’ adoption can significantly influence the level of perceived corruption and implement important evidence about promoting transparency factor for underdeveloped countries.

This study contributes to the accounting literature by examining the theoretical and empirical insights about the impact of the of IPSAS’ adoption on the level of corruption, which can be considered as a new area of accounting literature and a useful signal for stakeholders in countries seeking adequate solutions to combat and fight corruption activities.

The purpose of this paper is to propose a method for statistical inference on data from power law distributions in order to explain behavior and social phenomena associated with web‐based social spaces such as discussion forums, question‐and‐answer sites, web 2.0 applications and the like.

The paper starts by highlighting the importance of explaining behavior in social networks. Next, the power law nature of social interactions is described and a hypothetical example is used to explain why analyzing sub‐sets of data might misrepresent the relationship between variables having power law distributions. Analysis requires the use of the complete distribution. The paper proposes logarithmic transformation prior to correlation and regression analysis and shows why it works using the hypothetical example and field data retrieved from Microsoft's Netscan project.

The hypothetical example emphasizes the importance of analyzing complete datasets harvested from social spaces. The Netscan example shows the importance of the logarithmic transformation for enabling the development of a predictive regression model based on the power law distributed data. Specifically, it shows that the number of new and returning participants are the main predictors of discussion forum activity.

This paper offers a useful analysis tool for anyone interested in social aspects of the Internet as well as corporate intra‐net systems, knowledge management systems or other systems that support social interaction such as cellular phones and mobile devices. It also explains how to avoid errors by paying attention to assumptions and range restriction issues.

The purpose of this paper is to explore the most significant determinants of capital structure of manufacturing firms in India and to investigate whether the capital structure models derived from foreign research provide convincing explanations for capital structure decisions of Indian firms by using multiple regression model.

Different conditional theories of capital structure like trade off theory, pecking order theory and agency theory are reviewed to formulate testable propositions concerning determinants of capital structure of manufacturing firms. Multiple regression model and correlation matrix have been used as statistical tools to investigate the most significant determinants of capital structure of manufacturing firms in India with the help of SPSS Software for a sample of top 100 manufacturing firms listed in BSE.

The results suggest that variables like asset composition, business risk and return on assets are positively related to debt ratio whereas firm size and debt service capacity are negatively related to debt ratio. The asset composition, business risk and return on assets appear to be significant determinants of capital structure, while firm size and debt service capacity are insignificant determinants.

The findings of this study are consistent with predictions of trade off, pecking order and agency theory of finance which helps in understanding financing behaviour of firms in India.

This study has laid some ground work to explore the determinants of capital structure of Indian firms upon which a more detailed evaluation could be based. Furthermore, empirical findings should help corporate managers to make optimal capital structure decisions.

To the authors’ knowledge, this study is the first that explores the most significant determinants of capital structure of manufacturing firms in India by using the most recent data. Moreover, this study also confirms that same factors affect the capital structure decisions of firms in developing countries as identified for firms in developed economies.

This study aims to investigate the relationship between management characteristics including management capability, management entrenchment, agency costs and overconfidence and firm performance in companies listed on the Tehran Stock Exchange market.

The research population includes 125 companies after applying systematic elimination sampling method during 2010-2016. The primary measure for companies’ performance is return on assets and Demerjian et al. (2012a) model is used to measure managerial characteristics.

The results indicated that two management characteristics, namely, management capability and overconfidence are positively associated with firm performance and improve the level of performance. Agency costs did not have any significant effect on firm performance and management entrenchment leads to deterioration in firm performance.

The paper focuses on managerial characteristics and firm performance, which the results may very helpful to companies and investors to hiring managers with specific characteristics. Moreover, the results may give strength to further studies.

The purpose of this paper is to examine the possibility of creating hedge funds “clones” using liquid exchange traded instruments.

Authors analyze the performance of fixed weight and extended Kalman filter generated clone portfolios (EKF) for 14 hedge fund strategies from February 2004 to September 2009. EKF approach does not indeed impose any normality constraints on the error terms which allow the filter to find the optimal recursive process by itself. Such models could adjust even faster to sudden shifts in market conditions vs a standard Kalman filter.

For five strategies out of 14, this work finds that EKF clones outperform their corresponding indices. Thus, for certain strategies, the possibility of cloning hedge fund returns is indeed real. Results should be however considered with caution.

This paper suggests that the most important benefits of clones are to serve as benchmarks and to help investors to better understand the various risk factors that impact hedge fund returns.

Rather than using fixed‐weight and rolling windows approaches (as Hasanhodzic and Lo), this work considers an extended version of the Kalman filter, a computational algorithm that better captures the time changing dynamics of hedge fund returns. Also, in order to be practical, this research considers investable factors and that the models themselves could not be constant over time.

Practitioners often face challenges in model development when establishing a relationship between the input and output variables and their optimization and control. The purpose of this paper is to demonstrate, with the help of a real life case example, the procedure for model development between a key process output variable, called the multi-stage flash evaporator efficiency, and the associated input process variables and their optimization using appropriate statistical and analytical techniques.

This paper uses a case study approach showing how multiple regression methodology has been put into practice. The case study was executed in a leading Indian viscose fiber plant.

The desired settings of the relevant process parameters for achieving improved efficiency have been established by appropriately using the tools and techniques from the Lean Six Sigma tool kit. The process efficiency, as measured by M3 of water evaporated per ton of steam, has improved from 3.28 to 3.48 resulting in satisfactory performance.

This paper will be valuable to many practitioners of Six Sigma/Lean Six Sigma and researchers in terms of understanding the systematic application of quality and optimization tools in a real world situation.

This paper aims to attempt to empirically investigate the impact of privatization on the performance of central public sector enterprises in India. Further attempt is made to explore whether privatization is a necessary or sufficient condition for improvement of performance of central public sector enterprises.

The scope of the study is limited to financial and operating performance analysis of 206 central public sector enterprises in India. Multiple regression analysis has been used to determine the magnitude and direction of relationship between dependent and independent variables and identify variables other than privatization which affects performance.

The study found that financial and operational performance of firms has improved significantly due to privatization. Further, firm-specific factors and other parallel reforms adopted by enterprises have significantly influenced their performance. The established regression model is highly significant with F-ratio of 31.825 at 99% significance level. The degree of explanation of the model is robust with adjusted R² at 0.956 implying that only 4.40% of explanation in the dependent variable cannot be explained by designated independent/explanatory variables.

The study would be useful to public policymakers to reach to a policy view on whether further disinvestment/privatization of central public sector enterprises need to be continued, and if so, then to what extent and direction.

This study aims to identify various enablers and the inter-relationships among them in adopting social sustainability measures in the supply chain. Social sustainability in the supply chain has received growing attention in the recent years, due to growing awareness on equity, health and safety, education, child and bonded labor and ethical practices in corporates.

Various enablers and their contextual relationships were identified. The enablers were classified based on dependence and driving power (DP) with the help of MICMAC analysis. In addition to this, a structural model of the enablers to the social sustainability problem has been put forward using the interpretive structural modeling technique.

In this study, 14 relevant enablers were identified from literature review and subsequent discussions with experts from academia and the industry. Competitive pressure leads at the bottom of the digraph with high DP, followed by customers ' requirements, financial liquidity and social concern. Social sustainability awareness was found to be at the last level with less DP.

Enablers were developed based on literature survey and expert opinions. Hence, the model is not statistically validated. This model also does not quantify the adverse effect of each of the variables on social sustainability practices in the supply chain.

The development of a hierarchy not only helps the supply chain managers to understand the enablers better, based on their importance, but also helps in decision making in the supply chain, which in turn enables the corporations to be competitive.

Findings of this article will help the corporations to be more socially sustainable by understanding the various enablers and their contextual relationships in the supply chain.

The structured social sustainability model helps supply chain managers and experts to understand interdependence of the enablers. This also helps in identifying different enablers with different degree of importance, which will be very much useful in adopting social sustainability measures in the supply chain.

The purpose of the study is to investigate loyalty building and the creation of affectionate bonds in the consumer‐firm dyad.

The study relies on face‐to‐face personal interviews in the context of grocery store retailing.

The results identify the significant predictors of consumer‐firm emotional attachment to be firm trust, trust in employees, likeability of service personnel and likeability of co‐consumers, shopping enjoyment, self‐expressiveness, place dependence, and place identity. Consumers' self‐enrichment, self‐gratification and self‐enablement likely influence emotional attachment, which in turn is a strong predictor of behavioral loyalty and word of mouth. Attachment anxiety appears to multiply the effects of emotional attachment on behavioral loyalty and word of mouth.

The cross‐sectional nature of the study precludes definitive conclusions concerning causality between the constructs utilized. The data come from the supermarket retail channel, limiting the generalizibility of the results.

As the results suggest that the consumer's self‐enrichment seems to be the most important factor in determining emotional attachment, managers should incorporate the notion of emotional attachment into strategic performance management systems.

The study incorporates the notion of consumer heterogeneity into the relationship anxiety construct, arguing in favor of a non‐additive consumer‐firm emotional attachment nomological network.

The purpose of this paper is to analyze the influence of job satisfaction on hostage employee loyalty in Isfahan Province Gas Company (IPGC).

The statistical population of this study included the formal recruited employees of IPGC out of which, 212 employees have been selected using a stratified random sampling method. A questionnaire has been developed and used for data collection regarding job satisfaction and employee loyalty. In this study along with the other studies of the authors, employees of IPGC were classified into four different categories on the basis of loyalty matrix, and the majority (78 percent) of them were located in the hostage category. Structural equation modeling has been used for data analysis.

The findings imply that job satisfaction does not have a significant influence on the loyalty of hostage employees.

Organizations are encouraged to identify the individual and organizational factors and obstacles, take necessary measures to increase job satisfaction and maintain the level of employee loyalty and gradually shift them from the hostage category to the apostle category, which results in an increased number of loyal and satisfied employees.

This study indicates how the application of the concepts of loyalty matrix, particularly its hostage category, can be expanded in the field of organizational behavior management.