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This chapter revisits the Hausman (1978) test for panel data. It emphasizes that it is a general specification test and that rejection of the null signals misspecification and is not an endorsement of the fixed effects estimator as is done in practice. Non-rejection of the null provides support for the random effects estimator which is efficient under the null. The chapter offers practical tips on what to do in case the null is rejected including checking for endogeneity of the regressors, misspecified dynamics, and applying a nonparametric Hausman test, see Amini, Delgado, Henderson, and Parmeter (2012, chapter 16). Alternatively, for the fixed effects die hard, the chapter suggests testing the fixed effects restrictions before adopting this estimator. The chapter also recommends a pretest estimator that is based on an additional Hausman test based on the difference between the Hausman and Taylor estimator and the fixed effects estimator.

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.

Pakistan ranks third globally in terms of newborn deaths occuring within the first 24 hours of life. With a neonatal mortality rate of 42.0%, it carries the highest burden compared to neighboring countries such as Bangladesh (17%), India (22.7%) and Afghanistan (37%). While there has been a decline in neonatal mortality rates in Pakistan, the pace of this decline is slower than that of other countries in the region. Hence, it is crucial to conduct a comprehensive examination of the risk factors contributing to neonatal mortality in Pakistan over an extended period. This study aims to analyze the trends and determinants of neonatal mortality in Pakistan over three decades, providing valuable insights into this persistent issue.

The study focused on neonatal mortality as the response variable, which is defined as the death of a live-born child within 28 days of birth. Neonates who passed away during this period were categorized as “cases,” while those who survived beyond a specific timeframe were referred to as “noncases.” To conduct a pooled analysis of neonatal mortality, birth records of 39,976 children born in the five years preceding the survey were extracted from four waves (1990–2018) of the Pakistan Demographic and Household Survey. The relationship between risk factors and the response variable was examined using the Cox Proportional Hazard Model. Neonatal mortality rates were calculated through the direct method using the “syncmrates” package in Stata 15.

During the extended period in Pakistan, several critical protective factors against neonatal mortality were identified, including a large family size, improved toilet facilities, middle-aged and educated mothers, female children, singleton live births, large size at birth and longer birth intervals. These factors were found to reduce the risk of neonatal mortality significantly.

This study makes the first attempt to analyze the trends and patterns of potential risk factors associated with neonatal mortality in Pakistan. By examining a large dataset spanning several years, the study provides valuable insights into the factors influencing neonatal mortality.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2022-0604

The provision of facilities management (FM) services, facilitated by a well-suited sourcing strategy, significantly influences the optimization of dwelling unit performance, augmentation of occupants’ living experiences and the establishment of a secure and comfortable environment. Consequently, this study aims to examine the sourcing approach used for delivering FM services in social housing within Nigeria’s semi-arid climate and to assess the effectiveness of the chosen sourcing approach in the provision of FM services.

Data collection involved one-on-one interviews conducted with five principal/senior staff members of the Borno State Housing Corporation in Maiduguri, Northern Nigeria. For data analysis, the NVivo R1 software package and Stata 13.0 were used. Thematic analysis was applied to the data, and the findings were presented through narrations and direct quotes from participants. Logistic regression analysis was subsequently used to assess the effectiveness of the adopted in-house approach in providing FM services in the context of social housing.

Administrators of social housing in the semi-arid climate of Nigeria exclusively adopted an in-house sourcing approach for providing FM services. Nevertheless, the in-house approach was conclusively determined to be ineffective in delivering FM services within the specific context of social housing in the semi-arid climate of Nigeria.

This investigation centred on examining the sourcing strategy used by social housing administrators for delivering FM services in social housing within a semi-arid climate of Nigeria. Additionally, the study delved into evaluating the efficacy of the chosen sourcing approach in the actual provision of FM services.

In the new economy, governed by technological progress and informational abundance, e-government service represents one of the drivers of the digital economy and society. The government and its institutions have the role of stimulating, leading, and controlling the process of transition to the digital society, which is a key component for the future prosperity and resilience of the European Union (EU). With focus on a better functioning of society by improving the citizens' access and use of e-government services, in this work we aim to identify the factors that influence the online interaction of individuals with public authorities in the EU member states. We used panel data for the EU member states in the period 2013–2021 to investigate the determinants of individuals' interaction with public authorities through institutional websites, using clustering regression with fixed effects, which allows both the clustering of the states and obtaining different slope parameters for each cluster. The results indicated the grouping of the EU states in an optimal number of two clusters, and the fixed effects regression clustering pointed out different coefficients for the two clusters, indicating distinct patterns. The main factors that influence the online interaction of citizens with public authorities are related to internet use, education, and government effectiveness, but the impact is different for the two clusters, depending on the specifics of the component countries.

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.

Generative pretrained transformers (GPTs), soaring to one million users at lightning speed, outpaced social media giants (15 times faster) (Buchholz, 2023). Despite this, scant research explored GPT’s impact on the digital entrepreneurial intentions (EIs) of students and tech-savvy generations. This study aims to pioneer a fusion of the technology acceptance model (TAM) and the theory of planned behavior (TPB), bridging the gap in research.

In this bold quantitative quest, business administration students became fearless participants, engaging in a survey of profound significance. Guided by the mighty powers of G*Power and Stata’s structural equation modeling builder, the intricate relationships within a robust sample of (n = 400) were unraveled.

The mediating power of GPT usefulness and GPT ease of use part of the TAM emerges, paving the way for a future brimming with digital entrepreneurship (DE) boundless possibilities. Predictably, the study found that TPB constructs also affect the EI of students.

This groundbreaking study brings together the powerful combination of TAM and TPB, while pioneering the exploration of GPT models’ mediating role. Its findings offer invaluable contributions to the field of DE and policymakers.

This paper aims to examine how the firm’s global coopetition network position impacts market share and to explore the multiple moderating effects of trade network strength and structures on the relationship between firm global coopetition network position and market share.

This paper selects global automobile manufacturing firms as samples whose classification is “Automobile” in the Factiva database from 2014 to 2018 and develops the measurement for global coopetition network and trade network by using Ucinet6. Finally, Stata was used for data analysis.

This paper finds that structural holes and centrality are beneficial to improve global market share. And the trade network strength and structures have positive multiple moderating effects on the relationship between the firm global coopetition network position and market share.

This paper explores industrial international competitiveness according to the intricate trade relations among countries and the impact of industrial international competitiveness on the relationship between global coopetition network position of brand firms and market share. The results of this paper expand the current literature on the relationship between characteristics of coopetition network and trade network.

This paper aims to examine the relationship between environmental, social and governance (ESG) performance and systemic risk sensitivity of Chinese listed companies. From the consumer loyalty and investor structure perspectives, the relationship between ESG performance and systemic risk sensitivity is analyzed.

Since Morgan Stanley Capital International (MSCI) ESG officially began to analyze and track China A-shares from 2018, 275 listed companies in the SynTao Green ESG testing list for 2015–2021 are selected as the initial model. To measure the systematic risk sensitivity, this study uses the beta coefficient, from capital asset pricing model (CPAM), employing statistics and data (STATA) software.

The study reveals that high ESG rating companies have high corresponding consumer loyalty and healthy trading structure of institutional investors, thereby the systemic risk sensitivity is lower. This paper reveals that companies with high ESG rating are significantly less sensitive to systemic risk than those with low ESG rating. At the same time, ESG has a weaker impact on the systemic risk of high-cap companies than low-cap companies.

The study helps the companies understand the influence of market value on the relationship between ESG performance and systemic risk sensitivity. Moreover, this paper explains explicitly why ESG performance insulates a firm’s stock from market downturns with the lens of consumer loyalty theory and investor structure theory.

The paper provides new insights on the company’s ESG performance that significantly affects the company’s systemic risk sensitivity.

To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles.

The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method.

The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage.

This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.