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Matching foods available at food pantries to client preferences provides a variety of benefits and should be viewed as a best practice.

Data on food pantry client demographics and choices were collected throughout 2019, resulting in 467,384 recorded food choices. Households were segmented using a cluster analysis based on observed choices. Next, a variety of demographics-based statistical models were built to predict food choice segment membership.

Six different segments of clients emerge from the choice data. Some segments choose mostly fruits and vegetables; other segments prefer different options such as grains, animal protein, plant protein or easier-to-prepare but more processed combination foods. Further, these choice-based segments can be predicted with varying levels of accuracy using demographic information. This highlights that the food people choose is related to their identities. For example, as household size grows, clients are less likely to belong to the segment preferring combination foods and more likely to belong to the segment preferring fresh fruits and vegetables. However, other demographics are more predictive of different segments; for example, Asian households are more likely to belong to segments that show preference for plant proteins or for grains.

Large-scale data on pantry client choice is exceedingly rare. The authors use this unique data set to propose food pantries understand their clients better through traditional marketing methods, specifically segmentation.

This study aims to identify climate change perception variables and socioeconomic variables likely to influence the adoption of liquefied petroleum gas (LPG) cooking behavior in Cameroon.

The study carries out a quantitative analysis using a Logit model mobilizing secondary data collected in the database of the Survey on the Capitalization of Achievements of the National Participatory Development Program.

The results show that the variables of flooding and temperature rise influence the likelihood of adopting LPG, alongside the variables of poverty, income, level of education and urban residence.

As well as in addition to improving the standard of living of poor households, these results suggest increasing the supply of LPG in peri-urban areas and raising awareness of the relationship between climate perception, biomass energy consumption and deforestation.

The originality of this study lies in the fact that it identifies the variables climate change perception variables and the socioeconomic variables likely to favor the adoption of LPG in the Cameroonian context.

The purpose of this literature review is, first, to understand how employees with disabilities in the context of COVID-19 have been studied under the talent management (TM) approach; second, to explore what we know about the predictors and outcomes that have been linked to TM practices in that area; and third, to identify gaps in our understanding and provide insights for future research.

This paper presents a systematic literature review (SLR) based on 38 academic sources published in high-impact indexes from 2020 to 2023.

The existing research shows COVID-19 as a crucial context that led organizations to more precarious and segmented TM practices, which had negative consequences for employees with disabilities, both at the individual level (reduced satisfaction and income, and increased health issues) and the organizational level (increased unemployment, turnover and discrimination as well as declining performance and productivity).

This paper provides essential contributions to the field of TM in the relatively unexplored context of employees with disabilities since the emergence of COVID-19. Our literature review suggests there is significant room for developing and implementing adjusted TM strategies and practices to foster effective inclusiveness, accommodations and supportive work environments for employees with disabilities. From this evidence, a number of key avenues for future research and key implications for academics and practitioners are provided.

Based on the transformative learning theory, this paper analyses a French women-only training programme (WOTP) that aims to develop women’s soft skills in their professional contexts. This paper aims to focus on the process of personal transformation, the collective dimensions and the unexpected effects of the transformation.

This paper used a mixed qualitative design that mainly combines a qualitative two-step study of 47 women to assess their personal changes in terms of self-confidence, self-efficacy and assertiveness. This paper used 13 semi-structured interviews to explore the perceived changes in-depth.

The analysis shows that beyond “fixing their lack of skills” – including self-limiting behaviours, low feelings of self-efficacy and difficulty claiming one’s place – a WOTP can trigger a transformational learning experience at the individual level and can modify the surveyed women’s attitudes and behaviours at work. The results also highlight the collective dimension of transformation and, to some extent, an avenue for a societal transformation.

One can state that these WOTPs may positively contribute to human resources development in organisations, and that they may be considered a relevant practice in the move to promote women and gender diversity in organisations.

The findings reveal that, at their individual levels, these women may become agents of change by influencing and acting in their professional lives. The results stress that training women may contribute to organisational changes in terms of gender diversity. These findings contribute to the enrichment of the transformative learning theory by developing the collective and societal dimensions.

This study aims to analyse the outcomes of Indian insolvency proceedings for their ex-post economic efficiency. Ideally, insolvent yet viable companies should witness resolution, whereas insolvent-unviable companies should be liquidated. This study aims to ascertain the key forces that ensure or prevent the application of the first part of this maxim in practice.

The study uses logistic regression on a sample of 320 corporate insolvencies (out of 942 insolvencies) reported under the Insolvency and Bankruptcy Code (IBC), 2016. Two-stage least squares regression is used to check endogeneity issues.

The results claim high levels of rationality from the financial creditors and acceptable levels of viability from the plan proposers for precluding liquidation of insolvent yet viable companies. The findings reveal that an excess of value from resolution over that from liquidation, controls the outcomes of insolvency proceedings. Further examinations indicate that financial creditors’ focus on upfront recovery prevents them from judging the plans on other viability-related factors. Based on the findings, this study recommends that IBC must focus on the importance of both long-term recovery rates and resolution.

To the best of the authors’ knowledge, this is one of the first studies to empirically analyse Type 2 efficiency-related errors prevalent in the Indian insolvency proceedings since the enactment of its new code. The empirical explorations offered in this research can prove to be unique for policy-making.

Past research has shown there is a relationship between body image, sexual behavior, and pleasure. However, the majority of this research has centered on heterosexual participants. In this analysis, the author considers how this relationship between body image, sexual behavior, and pleasure may look within women and genderqueer individuals who are all AFAB (assigned female at birth) with 26 out of 30 participants identifying as LGBTQIA+. The author examines perceptions of body size, body hair, and genitals to consider how intersections of social structures – specifically internalized sexism, racism, and misogyny – influence the participants’ experience of sexual interactions. Both resistance and embodiment of traditional gender norms, even as queer women and genderqueer individuals, were examined in these narratives. The majority of the moments where traditional gender norms are examined describe situations when the participants were sexually interacting with cis-gendered men.

The ripple effect (i.e. disruption propagation in networks) belongs to one of the central pillars in supply chain resilience and viability research, constituting a type of systemic disruption. A considerable body of knowledge has been developed for the last two decades to examine the ripple effect triggered by instantaneous disruptions, e.g. earthquakes or factory fires. In contrast, far less research has been devoted to study the ripple effect under long-term disruptions, such as in the wake of the COVID-19 pandemic.

This study qualitatively analyses secondary data on the ripple effects incurred in automotive and electronics supply chains. Through the analysis of five distinct case studies illustrating operational practices used by companies to cope with the ripple effect, we uncover a disruption propagation mechanism through the supply chains during the semiconductor shortage in 2020–2022.

Applying a theory elaboration approach, we sequence the triggers for the ripple effects induced by the semiconductor shortage. Second, the measures to mitigate the ripple effect employed by automotive and electronics companies are delineated with a cost-effectiveness analysis. Finally, the results are summarised and generalised into a causal loop diagram providing a more complete conceptualisation of long-term disruption propagation.

The results add to the academic discourse on appropriate mitigation strategies. They can help build scenarios for simulation and analytical models to inform decision-making as well as incorporate systemic risks from ripple effects into a normal operations mode. In addition, the findings provide practical recommendations for implementing short- and long-term measures during long-term disruptions.

Post-COVID-19, public K–12 schools are still facing the consequences of the years of interrupted learning. Schools serving minoritized students are particularly at risk for facing challenges with academics, behavior and student social emotional health. The university counseling programs are in positions to build capacity in urban schools while also supporting counselors-in-training through service-learning opportunities.

The following conceptual manuscript demonstrates how counselor education counseling programs and public schools can harness the capacity-building benefits of university–school partnerships. While prevalent in fields like special education, counselor educators have yet to heed the hall to participate in mutually beneficial partnership programs.

Using the multi-tiered systems of support (MTSS) and the components of the university–school partnerships, counselor educators and school stakeholders can work together to support student mental health, school staff well-being and counselor-in-training competence.

The benefits and opportunities within the university–school partnerships are well documented. However, few researchers have described a model to support partnerships between the university counseling programs and urban elementary schools. We provide a best practice model using the principles of university–school partnerships and a school’s existing MTSS framework.

This study developed a new analytical model to quantify the influence of business intelligence (BI) adoption on bank performance. An in-depth review of academic literature revealed a significant research gap exists in investigating BI's performance impacts, especially in the under-studied Indian banking context. Additionally, customer relationship management (CRM) was incorporated as a moderating variable given banks' large customer databases.

A survey was administered to 413 employees across leading Indian banks to collect empirical data for evaluating the conceptual model. Relationships between variables were analysed using partial least squares structural equation modelling (PLS-SEM). This technique is well-suited for theory building with smaller sample sizes and non-normal data.

Statistical analysis supported the hypothesised positive effect of BI adoption on bank performance dimensions including growth, internal processes, customer satisfaction, and finances. Furthermore, while CRM did not significantly moderate this relationship, its inclusion represents an incremental contribution to the limited academic literature on BI in Indian banking.

The model provides a quantitative basis for strategies leveraging BI's performance benefits across the variables studied. Moreover, the literature review revealed an important knowledge gap and established a testable framework advancing BI theory in the Indian banking context. Significant future research potential exists through model replication, expansion, and empirical verification.

This research thoroughly reviewed existing academic literature to develop a novel testable model absent in prior studies. It provides a robust conceptual foundation and rationale for ongoing scholarly investigation of BI's deployment and organisational impacts.

This study aims to explore children’s cognitions about data flows online and their understandings of algorithms, often referred to as algorithmic literacy or algorithmic folk theories, in their everyday uses of social media and YouTube. The authors focused on children ages 8 to 11, as these are the ages when most youth acquire their own device and use social media and YouTube, despite platform age requirements.

Nine focus groups with 34 socioeconomically, racially and ethnically diverse children (8–11 years) were conducted in California. Groups discussed data flows online, digital privacy, algorithms and personalization across platforms.

Children had several misconceptions about privacy risks, privacy policies, what kinds of data are collected about them online and how algorithms work. Older children had more complex and partially accurate theories about how algorithms determine the content they see online, compared to younger children. All children were using YouTube and/or social media despite age gates and children used few strategies to manage the flow of their personal information online.

The paper includes implications for digital and algorithmic literacy efforts, improving the design of privacy consent practices and user controls, and regulation for protecting children’s privacy online.

Research has yet to explore what socioeconomically, racially and ethnically diverse children understand about datafication and algorithms online, especially in middle childhood.