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In the developing world, the adoption of new technology in agriculture has emerged as a tool to address the problem of investment-disincentive effect of capital investment on smallholders. In Indian agriculture, which is dominated by smallholders, technological adoption becomes very essential. In this regard, along with the government, local level organizations also provide training and other support to adopt the new agricultural technique. The present study is an attempt to assess the counterfactual impact of this sort of initiative in the context of Bihar, a state in India.

The study uses field survey data which are collected from the Gaya district of Bihar. Overall, 249 sample farmers are surveyed from 23 villages of four blocks of the Gaya district. There are two groups of selected farmers: treatment and control groups. Farmers who adopted the new technique belong to the treatment group, and otherwise, the control group. For analytical purpose, a propensity score matching method has been used to estimate the counterfactual impact of the adoption of the new technique of farming on farmers' agricultural income.

The study observes a significant improvement in the agricultural income of the farmers who adopted the new technique. New agricultural techniques, propagated through the local level organization, might be instrumental to enhance farmer's skill as well as income.

This type of approach may be adopted, complementary to the government's extension initiative to enhance farmers' income through adoption of the advanced farming process, as well as to improve the human capital of agriculture production.

The study laid a framework of assessing the counterfactual impact of intervention of local level organization and adoption of new farming techniques in the context of Bihar, India.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2022-0606

The Persons with Disabilities (PWDs) Act, 2006 (Act 715) was passed in Ghana to grant several rights to PWDs, including access to public services such as healthcare. This paper investigates the inequality in resources and then later assesses the effect of disability on healthcare utilization if all resources are distributed equally between disabled and not disabled persons.

The study uses data from the seventh round of the Ghana Living Standards Survey (GLSS 7), which was conducted in 2016/2017, and employs the estimation method of propensity score matching (PSM), within the framework of the capability approach (CA).

The findings are that there is a disparity in the resources and conversion factors needed to utilize healthcare. Compared to not disabled persons, persons living with disability are more likely to be poorer but pay more out of pocket because they are more likely to be uninsured. They are also older, male, uneducated and live in rural areas. They are also likely to spend more money but less time to travel to the health facility, and they wait longer to receive treatment. After matching these background characteristics, disability reduces healthcare utilization by 12.4%.

The research is limited by the lack of information about the reasons for seeking healthcare by the persons living with disability, whether it is for general treatment as any other person or for treating the disability.

Persons with disability are less endowed. However, even if they have the same resources as their abled counterparts, disability will still reduce healthcare utilization.

This paper identifies and addresses all forms of inequality with respect to healthcare utilization, within Amartya Sen’s Capability Approach.

Persons living with disability are less likely to seek treatment when ill. This is understandable since there is a disparity in the resources and conversion factors needed to utilize healthcare. However, after matching these background characteristics, just being disabled still reduces healthcare utilization. The paper uses Sen’s CA framework.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2022-0084

The paper identifies the influence of socio-economic factors and livelihood training in stimulating micro-entrepreneurship among women self-help group (SHG) members.

The study is based on a sample of 416 women SHG members drawn from all the four districts of Sikkim using cluster sampling procedure. A multivariate binary logistic model is used to find the impact of socio-economic factors, and a Poisson regression has been used to find the impact of training on fostering micro-entrepreneurship. The result is validated using a propensity score matching approach which corrects for the potential self-selection bias in the sample. Subsequently, a covariate adjustment estimator verifies the robustness of the approach.

The study finds that “size of landownership”, “amount of loan borrowed”, “member's age”, “number of earning and dependent members”, “number of years of SHG enrolment” as well as the “district to which the member belongs to” have a statistically significant influence on the graduation of SHG members to micro-entrepreneurs. Furthermore, it is found that members attending the livelihood training programmes had a significantly higher number of microenterprises.

The study differentiates itself by providing empirical evidence on how socio-economic factors and livelihood training stimulate micro-entrepreneurship among SHG women of Sikkim, which has so far remained unexplored. Moreover, advanced econometric method has been used to eliminate the possible self-selection bias involved with training participation and thereby provides reliable and robust results.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0070

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.

The authors determine how the scheduling and sequencing of surgeries by surgeons impacts the rate of post-surgical complications and patient length-of-stay in the hospital.

Leveraging a dataset of 29,169 surgeries performed by 111 surgeons from a large hospital network in Ontario, Canada, the authors perform a matched case-control regression analysis. The empirical findings are contextualized by interviews with surgeons from the authors’ dataset.

Surgical complications and longer hospital stays are more likely to occur in technically complex surgeries that follow a similarly complex surgery. The increased complication risk and length-of-hospital-stay is not mitigated by scheduling greater slack time between surgeries nor is it isolated to a few problematic surgery types, surgeons, surgical team configurations or temporal factors such as the timing of surgery within an operating day.

There are four major limitations: (1) the inability to access data that reveals the cognition behind the behavior of the task performer and then directly links this behavior to quality outcomes; (2) the authors’ definition of task complexity may be too simplistic; (3) the authors’ analysis is predicated on the fact that surgeons in the study are independent contractors with hospital privileges and are responsible for scheduling the patients they operate on rather than outsourcing this responsibility to a scheduler (i.e. either a software system or an administrative professional); (4) although the empirical strategy attempts to control for confounding factors and selection bias in the estimate of the treatment effects, the authors cannot rule out that an unobserved confounder may be driving the results.

The study demonstrates that the scheduling and sequencing of patients can affect service quality outcomes (i.e. post-surgical complications) and investigates the effect that two operational levers have on performance. In particular, the authors find that introducing additional slack time between surgeries does not reduce the odds of back-to-back complications. This result runs counter to the traditional operations management perspective, which suggests scheduling more slack time between tasks may prevent or mitigate issues as they arise. However, the authors do find evidence suggesting that the risk of back-to-back complications may be reduced when surgical pairings are less complex and when the method involved in performing consecutive surgeries varies. Thus, interspersing procedures of different complexity levels may help to prevent poor quality outcomes.

The authors empirically connect choices made in scheduling work that varies in task complexity and to patient-centric health outcomes. The results have implications for achieving high-quality outcomes in settings where professionals deliver a variety of technically complex services.

The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

This study aims to clarify the relationship between the coefficient of friction (COF) and temperature of aluminum-based brake discs.

Three friction blocks with different COFs are examined by a TM-I-type reduced-scale inertial braking dynamometer. On this basis, the thermo-mechanically coupled model of friction pairs is established to study the evolution of brake disc temperature under different COFs using ADINA software.

Results indicate that the calculated disc temperature field matches the experimental well. The effect of COF on the peak temperature is magnified by the braking speed. With the COF increasing, the rise rate of instantaneous peak temperature is accelerated, and the dynamic equilibrium period and cooling-down period are observed in advance. The increase in COF promotes the area ratio of the high-temperature zone and the maximum radial temperature difference. When the COF is increased from 0.245 to 0.359 and 0.434 at 140 km/h, the area ratio of high-temperature zone increases from 12% to 44% and 49% and the maximum radial temperature difference increases from 56°C to 75°C and 83°C. The sensitiveness of the axial temperature difference to the COF is related to the braking time. The maximum axial temperature difference increases with COF in the early stages of braking, while it is hardly sensitive to the COF in the later stages of braking.

The effect of COF on the aluminum-based brake disc temperature is revealed, providing a theoretical reference for the popularization of aluminum-based brake discs and the selection of matching brake pads.

This study aims to assess the impact of participation in self-help group (SHG) on the psychological empowerment of rural tribal women.

Primary data was collected using multistage random sampling from the rural women of Jharkhand, India. The propensity score matching method was adopted using the psmatch2 command in STATA.

The results show a significant positive change in women’s self-esteem, self-confidence, self-efficacy, autonomy, knowledge and skills, reduction in agony and quality of life after participation in SHG. This reveals that participation in SHG has a significant positive impact on the psychological empowerment of rural tribal women.

Despite the numerous studies on rural women’s empowerment, there is little evidence of literature focusing on the impact of participation in SHG on psychological empowerment, specifically in the tribal context. This study primarily focuses on women belonging to the scheduled tribe category of Jharkhand, one of the poorest states of India.

This study aims to examine how entrepreneurship education influences intentions for starting a technology venture among science, technology, engineering and mathematics (STEM) students with particular attention to gender differences. This study builds on the model of entrepreneurial event and social role theory to assess the impact of entrepreneurship education on feasibility, desirability and intentions for technology entrepreneurship.

The hypotheses are tested with a sample of 879 Bulgarian science and engineering students from 15 universities. To test the models, this study uses ordinary least squares and logistic regressions with robust standard errors and Hayes mediation analysis with bootstrap bias-corrected confidence interval estimations for indirect effects. Two-stage Heckman regressions to control for sample selection bias and other robustness checks including propensity score matching were used.

Results show that entrepreneurship education, measured as participation in an entrepreneurship course, has a stronger impact on feasibility, desirability and intentions for technology entrepreneurship for female STEM students compared to their male counterparts. As such, this study supports the notion that entrepreneurship education could be part of a solution to counteract societal norms that position technology entrepreneurship as a less desirable and/or less feasible choice for women in STEM. However, attention should be paid to the operationalization of entrepreneurship education, as other measures of entrepreneurship education (role models, entrepreneurship education support) did not have a moderation effect with gender.

The authors assume a positive correlation between entrepreneurial intentions and entrepreneurial behavior. Future studies should include actual entrepreneurial behavior to paint a more complete picture of the effect of entrepreneurship education.

Little is known about the role of entrepreneurship education in the field of technology entrepreneurship, and even less about the potential gender differences in entrepreneurship education among STEM students. The study contributes to the literature by examining factors that could help close the persistent gender gap in technology entrepreneurship.