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The authors examine the influence of powerful political corporate appointments on the usage of firm bribery channels. Party Secretaries within Chinese state-owned enterprises (SOEs) may simultaneously hold top management positions, thereby endowing powerful firm-level decision rights on those appointees, hereafter referred to as powerful dual role Party Secretaries.

This study employs panel data analysis with industry and year fixed effects. The authors use a sample of 1,143 Chinese SOEs listed on the Shanghai and Shenzhen Stock Exchanges from 2004 to 2015.

The authors find that powerful dual role Party Secretaries are associated with greater bribery channel usage. Following the ongoing anticorruption campaign, SOEs with the powerful appointments significantly reduce their usage of both transparent (entertainment and travel costs) and opaque bribery (abnormal management expenses) channels. However, in general, Chinese SOEs respond to the anticorruption shock by switching from the more transparent to the opaquer bribery channel.

The authors contribute to the ongoing debate of politicians on corporate boards by examining the relatively unexplored area of government appointed top management and their influence on bribery at the firm level.

This study aims to develop a regression-based machine learning model to predict housing price, determine and interpret factors that contribute to housing prices using different data sets available publicly. The significant determinants that affect housing prices will be first identified by using multinomial logistics regression (MLR) based on the level of relative importance. A comprehensive study is then conducted by using SHapley Additive exPlanations (SHAP) analysis to examine the features that cause the major changes in housing prices.

Predictive analytics is an effective way to deal with uncertainties in process modelling and improve decision-making for housing price prediction. The focus of this paper is two-fold; the authors first apply regression analysis to investigate how well the housing independent variables contribute to the housing price prediction. Two data sets are used for this study, namely, Ames Housing dataset and Melbourne Housing dataset. For both the data sets, random forest regression performs the best by achieving an average R² of 86% for the Ames dataset and 85% for the Melbourne dataset, respectively. Second, multinomial logistic regression is adopted to investigate and identify the factor determinants of housing sales price. For the Ames dataset, the authors find that the top three most significant factor variables to determine the housing price is the general living area, basement size and age of remodelling. As for the Melbourne dataset, properties having more rooms/bathrooms, larger land size and closer distance to central business district (CBD) are higher priced. This is followed by a comprehensive analysis on how these determinants contribute to the predictability of the selected regression model by using explainable SHAP values. These prominent factors can be used to determine the optimal price range of a property which are useful for decision-making for both buyers and sellers.

By using the combination of MLR and SHAP analysis, it is noticeable that general living area, basement size and age of remodelling are the top three most important variables in determining the house’s price in the Ames dataset, while properties with more rooms/bathrooms, larger land area and closer proximity to the CBD or to the South of Melbourne are more expensive in the Melbourne dataset. These important factors can be used to estimate the best price range for a housing property for better decision-making.

A limitation of this study is that the distribution of the housing prices is highly skewed. Although it is normal that the properties’ price is normally cluttered at the lower side and only a few houses are highly price. As mentioned before, MLR can effectively help in evaluating the likelihood ratio of each variable towards these categories. However, housing price is originally continuous, and there is a need to convert the price to categorical type. Nonetheless, the most effective method to categorize the data is still questionable.

The key point of this paper is the use of explainable machine learning approach to identify the prominent factors of housing price determination, which could be used to determine the optimal price range of a property which are useful for decision-making for both the buyers and sellers.

Research has shown that young adults face strong economic burdens when it comes to establishing their intimate relationships in times of labor market deregulation and economic recession. However, little is known about possible protective effects of the transition to cohabitation on subjective worries. Based on economic and gender-specific assumptions, the present paper uses data from the German Socio-economic Panel (GSOEP) from 1991 to 2020. Longitudinal analyses show that the transition into cohabitation reduces the economic worries of German women, especially in times of macroeconomic crisis. For men, cohabitation is only protective against economic worries if they or their partner have high economic resources. The latter may indicate that young men in precarious living situations perceive the male breadwinner model as a subjective burden in the context of cohabitation.

The purpose of this study was to advance research on work-related well-being and age by using a life-span approach to investigate the relationship between mastery goal orientation and work engagement during various age periods. The authors further tested whether a perceived motivational climate moderated the proposed relationships, and whether the nature of the moderation differed between age groups.

The authors utilized a two-wave, web-based questionnaire survey and collected data from 838 employees in the financial sector in Norway. Multiple regressions and PROCESS macro were used to test these hypotheses.

The authors found that both work engagement and mastery goal orientation differed across age groups and that the relationship between mastery goal orientation and work engagement was stronger for older than for younger ages. The results further support the moderating role of a motivational climate. Whereas a perceived mastery climate moderated the relationship between mastery goal orientation and work engagement for older workers, a perceived performance climate moderated the suggested relationship for younger workers.

The study extends research on work engagement in an age-diverse workforce by applying a life-span approach to the interplay between person and contextual elements in fostering work engagement. Furthermore, the study involved investigating factors that may inhibit or enhance the link between mastery orientation and work engagement for various age groups, which is important given work engagement’s link to central work outcomes.

High-quality nursing home (NH) care has long been a challenge within the United States. For decades, policymakers at the state and federal levels have adopted and implemented regulations to target critical components of NH care outcomes. Simultaneously, our delivery system continues to change the role of NHs in patient care. For example, more acute patients are cared for in NHs, and the Center for Medicare and Medicaid Services (CMS) has implemented value payment programs targeting NH settings. As a part of these growing pressures from the broader healthcare delivery system, the culture-change movement has emerged among NHs over the past two decades, prompting NHs to embody more person-centered care as well as promote settings which resemble someone's home, as opposed to institutionalized healthcare settings.

Researchers have linked culture change to high-quality outcomes and the ability to adapt and respond to the ever-changing pressures brought on by changes in our regulatory and delivery system. Making enduring culture change within organizations has long been a challenge and focus in NHs. Despite research suggesting that culture-change initiatives that promote greater resident-centered care are associated with several desirable patient outcomes, their adoption and implementation by NHs are resource intensive, and research has shown that NHs with high percentages of low-income residents are especially challenged to adopt these initiatives.

This chapter takes a novel approach to examine factors that impact the adoption of culture-change initiatives by assessing knowledge management and the role of knowledge management activities in promoting the adoption of innovative care delivery models among under-resourced NHs throughout the United States. Using primary data from a survey of NH administrators, we conducted logistic regression models to assess the relationship between knowledge management and the adoption of a culture-change initiative as well as whether these relationships were moderated by leadership and staffing stability. Our study found that NHs were more likely to adopt a culture-change initiative when they had more robust knowledge management activities. Moreover, knowledge management activities were particularly effective at promoting adoption in NHs that struggle with leadership and nursing staff instability. Our findings support the notion that knowledge management activities can help NHs acquire and mobilize informational resources to support the adoption of care delivery innovations, thus highlighting opportunities to more effectively target efforts to stimulate the adoption and spread of these initiatives.

The authors propose and test a theoretical framework that develops and analyzes precursors to firm acquisitions to determine if acquirers differ from other firms.

The authors use longitudinal, archival data from a sample of the largest firms in the global pharmaceutical industry from 1991 to 2012 with 1,327 firm-year observations.

The authors integrate prior research to show that the firm characteristics involving (1) R&D investment, (2) prior experience and (3) network centrality influence the likelihood that a firm will complete an acquisition.

In contrast to research focusing on the performance of acquiring firms, the authors show that firm characteristics predict acquisition activity by highlighting that acquiring firms differ from other firms. The authors also develop how network synergies can be realized by acquirers that have information advantages from more central network positions.

This study aims to discover a practical and effective way to apply the quality cost concept in Strategic Cost Management (SCM) framework. The interaction of preventive, appraisal and failure (PAF) activities in a company's internal value chain will be the starting point of SCM implementation.

This study begins by establishing value chain and quality costs as the scope of conceptual analysis. Discussions on the interrelationships between activities, quality and costs were gathered to clarify conceptual and practical gaps in the scope of the study. The PAF quality cost model is applied to find viable, practical solutions. The costs of activities will serve as performance indicators.

The PAF quality cost model depicts opportunities to lower costs and increase profit in a business simultaneously; current poor quality costs are the benchmark. Identifying PAF activities and costs in the business value chain and linking it with others is crucial in evaluating SCM applications. These linkages will generate a Quality Cost Chain (QCC). The leading indicator of improvement is a higher ratio between new possible failure costs (FC) and the combination of prevention and appraisal costs (PAC) than the current value, followed by a lower total quality cost (TQC). The subsequent attention is a lower ratio between the appraisal cost (AC) and prevention cost (PC). Mathematically, for assessing the operability of new quality-related activities, ΔPAC_new < ΔFC_new, TQC_new < TQC_current, (FC/PC)_new>(FC/PC)_current and (AC/PC)_new<(AC/PC)_current are proposed as feasible conditional-quantitative improvement criteria.

This study only discusses the relationship between quality costs and activities related to quality management in the PAF quality cost model, not cost behavior. This limitation opens up opportunities for future research that intends to link QCC with cost behavior in the context of managerial accounting and Strategic Cost Management. The use of QCC in certain industrial areas is the next research opportunity. The variety of PAF activities this study addresses originates from a wide range of industrial sectors; QCC research by sector may produce unique industrial quality cost phenomena.

QCC will make it easier for managers to evaluate how strategically their departments or activities contribute to quality costs at the departmental or organizational level, as well as to effectively and efficiently improve quality cost performance.

The quality-related activity and quality cost issues are still rarely treated as subjects of research studies in the field of Strategic Cost Management. Even so, the discussion tends to be very broad, complex and difficult to apply. This study combines a simple diagrammatic and mathematical approach to simplify the discussion and, at the same time, manage the value of strategic quality management.

This study aims to explore organizational factors that impact women’s career advancement. Knowledge of organizational practices that promote or obstruct women’s career progress is vital for women, firms and governments.

A total of 237 women participated in the study from the service sector in Johannesburg (South Africa). Participants were selected using a convenient sampling approach. Researchers used a questionnaire, consisting of demographic and open-ended questions, to gather data. Comments were analyzed using a thematic content analysis approach.

Findings reveal that prioritizing women, offering skills development opportunities, providing growth opportunities, assisting women in managing their careers, offering mentorship and having work-family support initiatives are practices that promote women’s career advancement. Conversely, practices that foster stagnation of women’s careers, distrust in women’s leadership abilities, preference for external candidates, people–related malpractices, male domination/preference, lack of role models and work-family balance support, hamper women’s career advancement.

The findings of this study will contribute to women’s empowerment. Organizations should provide a conducive atmosphere by instituting practices that promote their female employees' career advancement. Firms also should intentionally take practical steps to address practices that impede women’s career progression. The results of this study will also help the government to design appropriate policies that will promote the career progression of women employees.

This study presents findings from an analysis of qualitative data collected from 237 women to provide insight into the experiences of women working within the service industry in Johannesburg, South Africa.

In recent years, the global consumption of craft beer witnesses remarkable growth. This growth is attributed to the evolving demographics of beer consumers, particularly the emergence of a new generation known as Digitarians or Generation Z. This study aims to analyze the key determinants influencing craft beer consumption among Digitarians.

An online questionnaire is administered, and a total of 296 completed responses are included in the statistical analysis. The methodology uses logistic regressions combined with a backward selection process and variance inflation factor analysis to address multicollinearity. The logistic regressions are conducted in three steps to delve into the research objective and gain insights into the behavior of young consumers. The stepwise backward selection aids in obtaining robust coefficients as a variable selection tool.

The results shed light on how Digitarians’ preferences for craft beer are influenced by various factors, including self-perceived knowledge, alcohol content, gender, food pairings, environment and companionship.

To the best of the authors’ knowledge, this paper contributes novel insights by being the first study to explore the significance of craft beer choices among Digitarians, identifying the role of several predictors in their consumption patterns.

The purpose of this study is to offer a checklist that can be used for both creating and evaluating digital collections, which are also sometimes referred to as data sets as part of the collections as data movement, suitable for computational use.

The checklist was built by synthesising and analysing the results of relevant research literature, articles and studies and the issues and needs obtained in an observational study. The checklist was tested and applied both as a tool for assessing a selection of digital collections made available by galleries, libraries, archives and museums (GLAM) institutions as proof of concept and as a supporting tool for creating collections as data.

Over the past few years, there has been a growing interest in making available digital collections published by GLAM organisations for computational use. Based on previous work, the authors defined a methodology to build a checklist for the publication of Collections as data. The authors’ evaluation showed several examples of applications that can be useful to encourage other institutions to publish their digital collections for computational use.

While some work on making available digital collections suitable for computational use exists, giving particular attention to data quality, planning and experimentation, to the best of the authors’ knowledge, none of the work to date provides an easy-to-follow and robust checklist to publish collection data sets in GLAM institutions. This checklist intends to encourage small- and medium-sized institutions to adopt the collection as data principles in daily workflows following best practices and guidelines.