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Jason has had a string of bad luck: he was fired from his job, his car got repossessed, he had to move back in with his mother when he was unable to make rent on his apartment, and his girlfriend dumped him. He is feeling unmotivated and discouraged, but also recognizes—at his mother's insistence—that he needs to start contributing to the household. Following his mother's orders, he heads to the local strip mall seeking employment.

How hard could it be to get a job, anyway?

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

Women are exposed to vulnerabilities that can lead to drug use or hinder recovery. However, there is a dearth of studies on recovery programs for women. This study aims to add to the literature by examining the feasibility of a women-only aftercare program for recovering users in the Philippines.

The study used a mixed-method design with pre and post-program surveys used to measure changes in participants’ recovery capital. Focused group discussions elicited participants’ context, their reactions, perceived outcomes and suggestions on the program.

Women in the program shared narratives of pain, trauma and abuse before treatment. Participants reported significant improvements in personal, community and family recovery capital dimensions. The program enabled personal growth in the form of new knowledge, skills and self-confidence. The women-only program also provided a safe space for women, to receive support from other women, community members and family. However, the women continue to face continuing challenges related to stigma and discrimination and a lack of livelihood opportunities.

A limitation of the study was its small sample size and the lack of a control group. Another limitation was the variability in treatment received by the women, which could have affected overall outcomes. Future studies using a randomized control trial and longitudinal designs may provide more robust conclusions on the effectiveness of the program.

Given punitive contexts, gender-sensitive and trauma-informed programs and services for women involved in drug use could potentially mitigate the abuse, stigma and vulnerabilities they experience.

This study contributes to the sparse literature on women-only aftercare, particularly in countries that criminalize drug use.

Human Resource Practices (HRPs) have undergone a revolutionary change, with their practices being more strategic for facilitating the change process. Extant literature studies the impact of Lean Thinking Practices (LTPs) on Organizational Performance (OP). However, the role of HRP as a strategic partner in the inculcation of LTP appears to have been explored sparingly. Hence, this paper aims to identify the specific HRPs that enhance the impact of Lean Thinking on OP.

A cross-sectional survey method was adopted. A total of 528 responses from IT organizations across various levels and processes were collected. The proposed conceptual framework was tested and validated SPSS-Process Macro.

Findings revealed that the presence of HRPs as moderators is significantly impacting the relationship between LTP and OP. LTP when moderated by HRPs significantly impacts employee well-being when compared to other performance variables.

This study is a maiden attempt to study the role of HRP in the inculcation of LTP in IT organizations. Earlier studies, which have mainly concentrated on the need for Human Resource (people) involvement, have spoken and researched less about the specific HRPs in the inculcation of LTP. An empirically validated specific HRP for inculcating LTP in IT organizations is a significant contribution.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.