Search results

Education researchers have been urged to utilize causal inference methods to estimate the policy effect more rigorously. While randomized controlled trials (RCTs) are the gold standard for assessing causality, RCTs are infeasible in some educational settings, particularly when ethical concerns or high cost are involved. Quasi-experimental research designs are the best alternative approach to study educational topics not amenable to RCTs, as they mimic experimental conditions and use statistical techniques to reduce bias from variables omitted in the empirical models. In this chapter, we introduce and discuss the core concepts, applicability, and limitations of three quasi-experimental methods in higher education research (i.e., difference-in-differences, instrumental variables, and regression discontinuity). By introducing each of these techniques, we aim to expand the higher education researcher's toolbox and encourage the use of these quasi-experimental methods to evaluate educational interventions.

Drawing on the elaboration likelihood model (ELM), the authors examine the influence of perceived professor teaching qualities, as central cues, on online professor ratings. Also, our study investigates how the volume and period of reviews, as peripheral cues, affect online professor ratings.

Leveraging stratified random sampling, the authors collect reviews of 892 Information Systems professors from 250 American universities. The authors employ regression models while conducting robustness tests through multi-level logistic regression and causal inference methods.

Our results suggest that the central route from perceived professor qualities to online professor ratings is significant, including most qualitative pedagogical factors except positive assessment. In addition to course difficulty, the effect of the peripheral route is limited due to deficient diagnosticity.

Our primary concern about the data validity is a lack of a competing and complementary dataset. However, an institutional evaluation survey or an experimental study can corroborate our findings in future research.

Online professor review sites can enhance their perceived diagnosticity and credibility by increasing review vividness and promoting site interactivity. In addition to traditional institutional evaluations, professors can obtain insightful feedback from review sites to improve their teaching effectiveness.

To our best knowledge, this study is the first attempt to employ the ELM and accessibility-diagnosticity theory in explicating the information processing of online professor reviews. It also sheds light on various determinants and routes to persuasion, thus providing a novel theoretical perspective on online professor reviews.

Ensuring the early detection of structural issues in aircraft is crucial for preserving human lives. One effective approach involves identifying cracks in composite structures. This paper employs experimental modal analysis and a multi-variable Gaussian process regression method to detect and locate cracks in glass fiber composite beams.

The present study proposes Gaussian process regression model trained by the first three natural frequencies determined experimentally using a roving impact hammer method with crystal four-channel analyzer, uniaxial accelerometer and experimental modal analysis software. The first three natural frequencies of the cracked composite beams obtained from experimental modal analysis are used to train a multi-variable Gaussian process regression model for crack localization. Radial basis function is used as a kernel function, and hyperparameters are optimized using the negative log marginal likelihood function. Bayesian conditional probability likelihood function is used to estimate the mean and variance for crack localization in composite structures.

The efficiency of Gaussian process regression is improved in the present work with the normalization of input data. The fitted Gaussian process regression model validates with experimental modal analysis for crack localization in composite structures. The discrepancy between predicted and measured values is 1.8%, indicating strong agreement between the experimental modal analysis and Gaussian process regression methods. Compared to other recent methods in the literature, this approach significantly improves efficiency and reduces error from 18.4% to 1.8%. Gaussian process regression is an efficient machine learning algorithm for crack localization in composite structures.

The experimental modal analysis results are first utilized for crack localization in cracked composite structures. Additionally, the input data are normalized and employed in a machine learning algorithm, such as the multi-variable Gaussian process regression method, to efficiently determine the crack location in these structures.

The purpose of this study is to examine the impact of mandatory corporate social responsibility (CSR) spending on firm value in the Indian context.

Using firm-level data over the period 2012–2017, this study uses the difference-in-differences (DID) technique combined with matching to control for potential endogeneity of the decision to comply with the CSR Act since the Act in its current form is applicable as a comply-or-explain obligation.

The results of this study suggest that mandatory CSR spending has a positive and statistically significant impact on firm value. These results remain robust to alternative econometric techniques such as regression discontinuity design (RDD) and randomization inference test as well as to alternative empirical specifications. Furthermore, the study demonstrates that the positive effect of CSR spending on firm value is more pronounced for firms with higher information asymmetry problem and lower institutional holdings.

This study explicitly considers the “comply-or-explain” flexibility option, in terms of spending on CSR, provided to Indian firms for the initial two to three years and investigates whether spending on CSR helps firms enhance their firm value. The study also finds that the positive effect of CSR spending on firm value is more pronounced for firms with higher information asymmetry problems and lower institutional holdings.

This paper aims to investigate the impact of the implementation of the critical audit matters (CAMs) disclosure requirement and the subsequent relationship between CAM disclosures and audit report lag, as well as audit fees in the USA.

This study used difference-in-differences analyses to investigate the impact that the implementation of the requirement for auditors to report CAMs on their audit report has on the audit process. It also used levels regression models to examine the relationship that CAM disclosures have with audit report lag and audit fees.

This study found that the implementation of the CAM disclosure requirement in the USA reduced audit report lag while not significantly affecting audit fees. This suggests that the CAM disclosure requirement may increase the cooperation between auditors and managers and improve the efficiency of the audit process.

This study’s results are informative for assessing the economic impact of requiring CAM disclosures, which should be of importance to regulators, auditors and accounting researchers.

This study used different approaches to investigate two aspects of the CAM disclosure requirement – the effect of the implementation of the disclosure requirement and the subsequent effects related to CAM reporting outcomes. Unlike many previous studies investigating CAM disclosures, which relied on experiments and questionnaires, this study used actual CAM disclosure data in the USA to investigate the impact on audit report lag and audit fees.

The purpose of this paper is to determine if there is a link between corporate shareholder value creation and economic growth. The first objective of this paper is to determine which specific shareholder value measurement best explains shareholder value creation for a particular industry. The next objective of the study is to establish, for each of nine different categories of firms examined, a set of value drivers that are unique and significant in expressing shareholder value for that particular category of firms. Lastly, the relationship between shareholder value creation and economic growth is tested.

To quantify and measure value creation, the paper investigates the various value creation measurements that are being applied. The next step is to ascertain whether various industries have different value creation measures that best explain value creation for the respective industries. Then, the value drivers of these specific value creation measures can be determined and their relationship with economic growth tested.

The results of this study indicate that each industry does have a specific shareholder value creation measurement that best explains shareholder value creation for that industry; for example, for five of the nine categories (industries) that were analyzed, market value added was found to be the best shareholder value creation measurement, but for capital-intensive firms and manufacturing firms, the Qratio is the best measure, while for the food and beverage industry, the market to book ratio was found to be a better measure of shareholder value creation than other measures tested. It was further found that an increase in corporate shareholder value creation is to the detriment of economic growth.

The contribution of the present study is its determination of a unique shareholder value creation measurement for particular industries. In addition, a specific set of variables per industry that create shareholder value is identified. Lastly, the important link between shareholder value creation and economic growth is exposed.

This study assessed the heterogeneous impact of the Anchor Borrower Program (ABP) on the welfare distribution of rice farming households in Nigeria.

Self-selection bias and treatment endogeneity were accounted for by employing the Instrumental Variable Quantile Regression (IVQR) model. The estimates obtained from the IVQR model were further compared with those from the conventional quantile regression, and quantile regression using Propensity Score Matching. This was to highlight the extent to which endogeneity bias has been purged from the treatment, in order to establish a consistent causal link between participation in the ABP and the welfare of a cross-section of rice farming households.

ABP farmers had significantly higher rice yields across all quantiles of the yield distribution under treatment exogeneity assumption, and in only two quantiles upon controlling for observable confounders. However, this yield gain did not translate to higher Per capita Consumption Expenditure (PCE). The estimates of the more robust IVQR model provided further evidence that the rice yield and PCE of ABP farmers are not statistically different from that of non-ABP farmers across all quantiles of the welfare distribution.

The negligible impact of ABP was relatively higher for lower-yielding households. Thus, implying that, although the ABP is a pro-poor development intervention, the program has not been sufficiently implemented to significantly improve the welfare of the dominant resource-poor farming households in Nigeria.

This study assessed the impact of ABP beyond the conventional potential mean outcome framework by accounting for heterogeneity in treatment effect.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0083

Much prior work involving director incentives and corporate behaviour has been focussing on their absolute dollar value or the intrinsic value and generated mixed findings. Comparison theories, however, suggest that the relative value of an incentive may be the main drive for individual performance. This study attempts to investigate the role of director relative pay in promoting the board’s intervention with unrelated diversification decisions.

The analysis uses data from firms operating in more than one segment during the period from 1999 to 2019. Data were obtained from WRDS databases. Ordinary least squares (OLS) regression analysis and the two-stage system generalized method of moments (GMM) were run to test the hypotheses. To test the robustness of the findings, alternative proxies for the key independent variables were used in separate analyses.

The results support the hypothesis that unrelated diversification negatively impact firm performance, while higher director relative pay will help reduce unrelated business diversification. The absolute director pay, however, has no significant impact on corporate strategic choices. The results also highlight the moderating effect of director overcompensation. Director overcompensation will cancel out the impact of relative director pay on unrelated diversification.

This study takes a fresh theoretical perspective by framing the investigation using the dimensional comparison theory to address the single untended comparison framework in the director pay structure – the intra-individual framework. It is the first to investigate the role of director relative pay in corporate strategic choices. The findings support the contention that the relative value of the incentive is an important indicator of the effectiveness of the pay.

This study aims to uses biosocial gender theory to describe successful entrepreneurial behavior in conflict zones. Specifically, the authors investigate how the reliance on agentic (assertive, individual focused) behavior and communal (facilitative and friendly) behavior lead to differential outcomes depending on the physical gender of the entrepreneur exhibiting the behavior.

The authors developed a conceptual framework based on extant literature. To test the framework, the authors gathered survey data from Iraqi-Kurdish entrepreneurs who have been living in a state of war since the late 1980s and use a novel analytical method to deal with the limitations inherent in gathering survey data in conflict zones. Qualitative data is presented to generate a better understanding of the survey results.

The findings indicate females who are successful in taking on the traditional male role of entrepreneur in conflict zones engage in lower levels of agentic behavior compared to their male counterparts. Successful entrepreneurs (male and female) rely extensively on communal behavior in their ventures. When it comes to community development, male entrepreneurs engaging in agentic behavior, seem to mentor aspiring entrepreneurs more than females. Females relying on communal behavior engage in more mentoring of aspiring entrepreneurs than males.

An understanding of the unique gender dynamics underlying entrepreneurial behavior in conflict zones remains incomplete. The study introduces evidence that gender differences, as well as social factors, combine with the unique characteristics of conflict zones resulting in different behavioral paths to entrepreneurial success. The analytical method introduces some statistical tools to scholars attempting to understand the unique conflict zone context. As such, the study provides guidance for scholars working in this context, as well as NGO’s and other institutions seeking to train entrepreneurs and improve economic conditions in conflict zones.

The purpose of this paper is to investigate the impact of changes in credit stock on real and financial indicators in Turkey with a focus on conditional and time-varying dynamics.

In addition to lag-augmented vector autoregression (LA-VAR) based time-varying Granger causality tests, threshold models and a research setting that identifies high/low states of credit growth based on 24-month moving averages are used to explore regime-dependent behavior. For investigating the asymmetric dynamics, the authors use a methodology that identifies good/bad news in credit growth based on 24-month moving averages and standard deviations.

Results strongly suggest that the impact of changes in credit stock induces conditional responses. Moreover, we find evidence for asymmetric responses. In the case of Turkey, efforts to spur growth through credit produce a strong negative byproduct, a depreciation in the exchange rate. The authors also find that changes in credit stock have become more relevant for uncertainties in inflation and exchange rate expectations, particularly in the era after mid-2018 in which credit growth volatility has increased noticeably.

This study provides a comprehensive analysis of time-varying and conditional responses to a change in credit stock in a major emerging economy. Using a moving threshold based only on the available information in the analysis of state-dependency represents a new approach.