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Discusses the methods of sensitivity analysis in use generally and by the property appraisal profession. Proposes a simplified structured and systematic technique of selecting critical or sensitive factors for sensitivity analysis in property development and investment appraisal. Concludes that sensitivity analysis has become an integral part of property appraisal.

This paper aims to improve upon the initial quantitative assessment of Kerlin’s macro-institutional social enterprise (MISE) framework (Monroe-White et al., 2015) to test for the effect of country-level institutions on the social enterprise sector. Major improvements are the inclusion of the civil society variable and expansion of the culture component in the analysis.

By following Kerlin’s (2013) original work that draws on the theory of historical institutionalism, this paper employs multi-level regression analysis to test the effect of country-level institutional factors on organizational-level social enterprise across countries. This analysis uses new macro-level data specifically for civil society and culture components.

The initial assessment of the framework found that several country-level factors had a significant effect on the variance in the size of the social enterprise sector across countries. The analysis provided here additionally shows a significant positive influence of civil society on the size of the social enterprise sector and shows that formal institutions capture the effect of informal cultural institutions when included in the model together.

This analysis provides policymakers, development actors and researchers with a better understanding of the influence of civil society on social enterprises and the interaction between formal and informal institutional underlying factors.

This paper’s significant contribution is the addition of civil society in the MISE analysis, which was not possible before owing to lack of data, and additional cultural analysis.

The stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors frequently use technical analysis or fundamental analysis. Data mining techniques coupled with fundamental and technical analysis types have the potential to give satisfactory results for stock market prediction. In the current paper, an effort is made to investigate the accuracy of stock market predictions by using the combined approach of variables from technical and fundamental analysis for the creation of a data mining predictive model.

We chose 381 companies from the National Stock Exchange of India's CNX 500 index and conducted a two-stage data analysis. The first stage is identifying key fundamental variables and constructing a portfolio based on that study. Artificial neural network (ANN), support vector machines (SVM) and decision tree J48 were used to build the models. The second stage entails applying technical analysis to forecast price movements in the companies included in the portfolios. ANN and SVM techniques were used to create predictive models for all companies in the portfolios. We also estimated returns using trading decisions based on the model's output and then compared them to buy-and-hold returns and the return of the NIFTY 50 index, which served as a benchmark.

The results show that the returns of both the portfolios are higher than the benchmark buy-and-hold strategy return. It can be concluded that data mining techniques give better results, irrespective of the type of stock, and have the ability to make up for poor stocks. The comparison of returns of portfolios with the return of NIFTY as a benchmark also indicates that both the portfolios are generating higher returns as compared to the return generated by NIFTY.

As stock prices are influenced by both technical and fundamental indicators, the current paper explored the combined effect of technical analysis and fundamental analysis variables for Indian stock market prediction. Further, the results obtained by individual analysis have also been compared. The proposed method under study can also be utilized to determine whether to hold stocks for the long or short term using trend-based research.

Drawing on legitimacy theory, this study aims to investigate whether the benefits of the external assurance process pass beyond the current period and help firms improve corporate social responsibility (CSR) performance in the subsequent periods. Furthermore, the authors examine whether corporate governance (CG) and firm visibility moderate the relationship between assurance and CSR performance.

The authors retrieved data from Thomson Reuters from 2002 to 2019 and executed a fixed-effects (FE) panel regression analysis. The country-level sample distribution includes 63 countries with 4,625 unique firms and 29,054 data points within these countries. The authors run several robustness tests using an alternative subsample, instrumental variable regression analysis, country-industry-year FE regression analysis, excluding the financial sector and including additional control variables and regression analysis based on propensity score matching.

The findings indicate that external assurance helps firms achieve greater CSR performance in the current period and the subsequent two periods following external assurance. However, external assurance exerts its strongest positive impact on CSR performance in the current period, and its influence extends, albeit at a weaker level, to the following two periods. Furthermore, the first moderation analysis reveals that governance structure helps firms translate the assurance process into the greater social performance but does not help to achieve higher environmental performance. The second moderation analysis reveals that firm visibility/size positively moderates between the assurance process and governance and social performance but not between the assurance process and environmental performance.

Despite the concurrent association between CSR performance and assurance being examined before, the lag-lead relationship is the novelty of the study to highlight the long-term effect of assurance on CSR performance. Besides, although the direct effect of both CG practices and firm visibility on CSR performance and the external assurance process has been investigated before, the authors extend the literature by examining the moderating effect of CG practices and firm visibility on the external assurance and CSR performance relationship. This provides a better explanation of the extent to which the effect of external assurance on CSR performance is constructed and conditioned by CG practices and firm visibility, thereby drawing attention to contingencies’ role in firms’ practices.

Higher education institutions, as influential social institutions, play an important role in promoting social responsibility and sustainable practices. However, approaches to implementing sustainable development (SD) in higher education institutions (HEIs) themselves remain fuzzy. One way to achieve holistic embedding can lie in organizational culture. This study aims to examine ways by which internal sustainability governance can promote sustainability culture by using empirical data from German HEIs.

This study uses factor analysis to identify relevant governance indicators. With a regression analysis, this study assesses the indicators’ effects on organizational sustainability culture in HEIs. A moderator analysis tests potential determinants derived from literature and their influence on sustainability governance and sustainability culture.

Operationalizing formalized sustainability governance for holistic implementation reveals a gap in sustainability management at HEIs. This study proposes a model for operationalizing sustainability governance and shows an effect on sustainability culture at the formal organizational level.

Based on the operationalization of sustainability governance, this empirical study provides evidence for the development of a holistic approach along a sustainability culture in organizations. This paper proposes a model for operationalization, analyzes multiorganizational data and shows the effects of sustainability governance on formalized organizational sustainability culture. This paper provides a transorganizational perspective for implementing SD following a top-down approach.

The purpose of this paper is to provide a methodology by which qualitative analyses serve as rich source materials for discovery of theoretically cogent interrelations between latent variables.

In an illustrative case, qualitative data are collected from US franchisee managers from a single branded franchise of automotive repair outlets. Qualitative analysis of franchisee experiences and attitudes is critical for construction of a causal model used to predict conflict intensity between franchisee managers and franchisors.

The model is based on franchisees' normative expectations for resource allocation within the franchise; and their perceptions of franchisor normative violations, which are determinative of grievances, distrust, and hostility. This theoretical orientation serves to generate a system of interrelated empirically testable propositions.

In principle, the primary limitation of using qualitative analysis for the construction of causal models is the fruitfulness of the theoretical orientation shared by the qualitative analyst and the causal modeler.

The methodological approach advanced in this paper advances qualitative research and causal modeling beyond the individual contributions. Qualitative analysis infuses variables and process imagery into causal modeling. In turn, causal modeling elaborates the qualitative analysis and makes explicit logical connections between variables.

This paper advances a methodology by which qualitative analysis and causal model construction may be usefully integrated. Theory‐based qualitative analysis may be formalized to map latent concepts and their interrelations. Further, operational measures of these concepts may be adduced from the analysis of textual data.

The purpose of this paper is to further the understanding of the determinants of audit report lag, which is the number of days from a company’s fiscal year-end to the date of its auditor’s report, by synthesizing extant literature. Audit report lag has been a variable of interest in many studies due to its use as a proxy for the occurrence of auditor-client management negotiations and audit efficiency and because long audit report lags delay the release of earnings information to the market.

The author uses meta-analysis to examine commonly identified predictors of audit report lag to determine if the prior research provides a consistent portrayal of audit report lag drivers.

The author finds that a number of variables relating to client profitability and financial condition, client complexity and audit opinion modifications increase audit report lag. In addition, audit report lag decreases with client size, when clients have positive earnings news to report and when the auditor has long tenure and provides non-audit services. Several variables, such as those relating to corporate governance and various auditor characteristics, have been little explored and would benefit from future research.

These results will be useful to researchers when selecting control variables for future audit report lag studies and provide insights into the key factors that contribute to the delay in audit reporting.

This study aims mainly to test the effect of audit committee independence and expertise attributes on corporate social responsibility (CSR) reporting, assurance and global reporting initiative (GRI) framework adoption and to investigate how CSR committee existence moderates this main relationship.

The study uses a large global sample that includes all (59,172) firm-year observations having CSR-related data in the Thomson Reuters Eikon database for a period between 2002 and 2019. The empirical analyses are based on random-effects logistic panel regression and Hayes methodology for the moderation analysis.

The study finds that audit committee independence and expertise are significantly associated with CSR reporting, CSR report assurance and GRI framework adoption. Moderation analysis largely supports the existence of a substitution role between audit and CSR committees and implies that audit committees are significant predictors of CSR reporting, assurance and GRI framework adoption mostly in the absence of the CSR committee.

The findings propose audit committee members be extra-vigilant in CSR reporting and assurance practices arising from undertaking substitution roles with the CSR committee. Hence, firms may configure their corporate structure in line with the results such as augmenting the audit committee with independent and expert members if they do not constitute a CSR committee. If firms establish a CSR committee, audit committee members may allocate less time to CSR reporting and assurance and more time to financial reporting quality.

This is the first study, to the best of the authors’ knowledge, to investigate the direct and indirect effect of audit committees’ attributes not only on CSR disclosure but also on GRI implementation and CSR reporting external assurance, considering the CSR committee’s possible substitutability or complementarity moderating role. This research develops a deeper understanding of audit committees’ non-financial role.

Considering the relevance of operational excellence as a business strategy, organizations are striving to improve themselves by adopting best practices and universally accepted principles through the process of continuous improvement, and these principles should be embedded in the culture of an organization. Organizations pursue to align themselves by continuously improving their processes by adopting scientifically proven techniques and cultural transformation throughout the organization. However, there is a lack of scientific instruments for the assessment of operational excellence. The objective of this study is to develop a scale for the assessment of practices of operational excellence principles in the organizations. Further reliability and validity of the developed scale are measured by testing the relationship between Human Resource Practices (HRP) and Operational Excellence (OE).

This study comprises quantitative design through exploratory and confirmatory studies and also includes qualitative analysis to develop a scale for the assessment of Operational Excellence (OE). Interviews from industry experts have been conducted to identify the major components for which organizations are striving for OE. Previous literature and excellence models, especially principles of the Shingo Operational Excellence Model (SOEM), have been reviewed and considered to finalize the scale items. Data were collected in two stages from both Telecommunication subsectors (Cellular Mobile Operators and Fixed Local Loop Operators) of Pakistan through the cross-sectional survey. In the first stage, exploratory factor analysis (EFA) was performed on the sample of 611 respondents from both Cellular Mobile and Fixed Local Loop operators of Pakistan. In the second stage, confirmatory factor analysis (CFA) was performed on the sample of 423 respondents from the Fixed local loop operators. EFA was conducted by using SPSS version 23 to finalize the OE scale, and for confirmatory factor analysis, PLS-SEM using Smart PLS was used to confirm the reliability and validity of the OE Scale.

The results of EFA reveal that OE is a multidimensional construct with three dimensions and 23 items. The dimensions of the developed OE Scale explored in this study are cultural enablers (CE), continuous process improvement (CPI) and enterprise alignment (EA). The confirmatory factor analysis of OE confirmed the scale dimensionality, reliability and validity along with the hypothesis testing to measure the impact of antecedent variable HRP on OE.

Organizations pursue to improve and align their operational processes but usually unable to confirm the implementation of their desired objectives. Based on the developed OE scale, managers may assess the implementation of OE principles in their organizations. This research has been conducted in the telecommunication sector of Pakistan only, and the developed instrument needs to be further tested in other organizations.

The instrument developed in this study will help both researchers and practitioners to assess the principles of operational excellence in their organizations and enable them to design the strategies for improving organizational performance.

The results of this study will create awareness about the principles of operational excellence. The developed OE instrument will assist in identifying the gaps in organizational norms and values from the perspective of paying respect to every individual inside and outside the organization. OE instrument will be further helpful in the identification and assurance of health, safety, protection of the environment and community issues.

This study provides a reliable and validated scale for the scientific area of operation management and helps managers with the assessment of operational excellence in their organizations. This newly developed scale is also valid to test and use in different studies and industries by researchers and practitioners.

The research aims to study the regression, cointegration and causality between the construction sector (CS) and the Gross Domestic Product (GDP), considering other variables in the study such as interest rate, taxation, industry sector, investment and Foreign Direct Investment (FDI), which are analyzed through unique panel models. The study was conducted in Turkey and the ten other countries of the European Union (EU) from 1988 to 2019.

Regression, cointegration and causality methods were used to investigate the different types of relationships between variables in the models. Data were obtained from official databases and the study contains four main stages, which are explained in detail in the methodology section.

The study used the analysis methods of regression, cointegration and causality tests and found that the CS and GDP have long-run estimates and the relationship between the two for different countries is negative in a two-way direction. Results are detailed in the analysis section.

No data were available for the variables before 1988 for most countries, which led to a limited number of observations and issues in statistical analysis methods.

Previously, only input and output tables were used in the analysis. The impact of interest rate, taxation, investment and FDI has not been analyzed. Key variables are very relevant for Turkey, which suffers from chronical inflation and taxation regimes. These show variability with the EU countries for comparative analysis and have not been explored to date, remaining as a major gap for the construction industry. No attempts were made to use regression, cointegration and causality methods with variables. These analysis methods enable an understanding of the differences in variance (heteroscedasticity) and the presence of cross-sectional dependence (CSD), both critical for the reliability of the comparison of data sets and analysis.