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This study aims to understand client food preferences and how pantry offerings can be optimized by those preferences.

This study develops and administers customized surveys to study three food pantries within the Second Harvest Food Bank of Northwestern North Carolina network. This study then categorizes food items by client preferences, identifies the key predictors of those preferences and obtains preference scores by fitting the data to a predictive model. The preference scores are subsequently used in an optimization model that suggests an ideal mix of food items to stock based upon client preferences and the item and weight limits imposed by the pantry.

This study found that food pantry clients prefer fresh and frozen foods over shelf-friendly options and that gender, age and religion were the primary predictors. The optimization model incorporates these preferences, yielding an optimal stocking strategy for the pantry.

This research is based on a specific food bank network, and therefore, the client preferences may not be generalizable to other food banks. However, the framework and corresponding optimization model is generalizable to other food aid supply chains.

This study provides insights for food pantry managers to make informed decisions about stocking the pantry shelves based on the client’s preferences.

An emerging topic within the humanitarian food aid community is better matching of food availability with food that is desired in a way that minimizes food waste. This is achieved by providing more choice to food pantry users. This work shows how pantries can incorporate client preferences in inventory stocking decisions.

This study contributes to the literature on food pantry operations by providing a novel decision support system for pantry managers to aid in stocking their shelves according to client preferences.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

Researchers continue to address the concept of self-disclosure because it is foundational for helping social networking sites (SNS) function and thrive. Nevertheless, the authors' literature review indicates that uncertainty remains around the underlying mechanisms and factors involved in the self-disclosure process. The purpose of this research is to better understand the self-disclosure process from the lens of dual-process theory (DPT). The authors consider both the controlled factors (i.e. self-presentation and reciprocity) and an automatic factor (i.e. social influence to use an SNS) involved in self-disclosure and broaden The authors proposed a model to include the interactive facets of enjoyment.

The proposed model was empirically validated by conducting a survey among users of WeChat Moments in China.

As hypothesized, this research confirms that enjoyment and automatic processing (i.e. social influence to use an SNS) are complementary in the SNS self-disclosure process and enjoyment negatively moderates the positive relationship between controlled factor (i.e. self-presentation) and self-disclosure.

Theoretically, this study offers a new perspective on explaining SNS self-disclosure by adopting DPT. Specifically, this study contributes to the extant SNS research by applying DPT to examine how the controlled factors and the automatic factor shape self-disclosure processes and how enjoyment influences vary across these processes – enriching knowledge about SNS self-disclosure behaviors. Practically, the authors provide important design guidelines to practitioners concerning devising mechanisms to foster more automatic-enjoyable value-added functions to improve SNS users' participation and engagement.

This paper explores the information needs and behaviors of undergraduate engineers.

The paper reports on a qualitative study employing semi-structured interviews with 18 students.

The study identified the types of information needs undergraduate engineers encounter while working on problem solving tasks and the strategies they use to resolve these needs. The findings reveal that students often encounter difficulties due to a lack of procedural knowledge rather than conceptual gaps or misunderstandings. Students look for step-by-step solutions to address their information needs and become more efficient problem-solvers. However, most instructors do not provide answers or solutions, leaving students uncertain about their progress and unable to correct their mistakes. Consequently, students seek information from their peers, including step-by-step solutions and access to previous course materials. They use file-sharing and instant messaging platforms like Google Drive and Facebook Messenger as covert means of seeking help, sharing solutions and engaging in coursework-related discussions.

The findings enrich the theory of information needs by delineating between conceptual and procedural information needs. These findings also underscore the significant role that classmates and friends play as sources of information. The study offers implications for conceptual development of information needs, and for instructors to provide solutions and support sharing between peers on official platforms.

This study aims to examine the association between mandatory corporate social responsibility (CSR) regulations (CSR mandate) and social disclosures (SOCDS) in India. It also investigates whether CSR committees mediate the relationship between CSR mandate and SOCDS. Furthermore, this paper explores how business group (BG) affiliation moderates CSR committee quality and SOCDS.

This study uses a data set of 5,345 observations from the Bombay stock exchange (BSE)-listed firms over 10 years (2011–2020) to examine the research questions. Baron and Kenny’s (1986) three-step model is estimated to examine the mediating role of CSR committees on the relationship between CSR mandate and SOCDS.

The study reveals that the CSR mandate positively impacts SOCDS in India due to coercive pressures. CSR committees mediate this relationship, with higher CSR committee quality leading to increased SOCDS. Furthermore, the authors report that SOCDS in India is positively related to CSR committee quality, and this relationship is stronger for BG firms. Finally, the supplementary analysis reveals that promoting CSR committee quality enhances firms’ likelihood of meeting CSR mandatory spending and actual CSR spending in India.

This research contributes to the academic literature by shedding light on the intricate dynamics of CSR mandates, CSR committees and SOCDS in emerging economies. Notably, the authors identify the previously unexplored mediation role of CSR committees in the link between CSR mandates and SOCDS. The creation of a composite index that measures complementary CSR committee attributes allows us to undertake a novel assessment of CSR committee quality. An examination of the moderating influence of BG affiliation documents the importance of CSR committee quality, particularly in governance, for enhancing SOCDS transparency within BG firms.

This study aims to examine the influence of perceived supervisor support (PSS) for strengths use on knowledge sharing (KS) intentions, mediated through work engagement and knowledge self-efficacy, based on the job demand-resources theory and the broaden and build theory.

Structural equation modeling and bootstrap analyses were performed to examine the research model using data derived from a two-wave questionnaire survey of 162 employees from five health-care organizations.

The results indicate that PSS for strengths use promoted KS intentions fully mediated through work engagement and subsequently through knowledge self-efficacy. However, there was no direct relationship between PSS for strengths use and KS intention.

The contribution of this research to the literature on KS is to find the effectiveness of a strengths-based approach in promoting KS intentions across boundaries and identifying mediating factors that link PSS for strengths use to KS intentions.

Autism spectrum conditions (ASC) and borderline personality disorder (BPD) have overlapping symptom profiles. Dialectical behaviour therapy (DBT) is an established treatment for self-harm and BPD, but little research has investigated the outcomes of DBT for ASC populations. This exploratory service evaluation aims to investigate the outcomes of a comprehensive DBT programme for adolescents with a diagnosis of emerging BPD and a co-occurring ASC diagnosis as compared to those without an ASC diagnosis.

Differences from the start to end of treatment in the frequency of self-harming behaviours, BPD symptoms, emotion dysregulation, depression, anxiety, the number of A&E attendances and inpatient bed days, education and work status, and treatment non-completion rates were analysed for those with an ASC diagnosis, and compared between those with an ASC diagnosis and those without.

Significant medium to large reductions in self-harming behaviours, BPD symptoms, emotion dysregulation and inpatient bed days were found for those with an ASC diagnosis by the end of treatment. There were no significant differences between those with an ASC and those without in any outcome or in non-completion rates. These findings indicate that DBT may be a useful treatment model for those with an ASC diagnosis, though all results are preliminary and require replication.

To the best of the authors’ knowledge, this is the first study to report the outcomes of a comprehensive DBT programme for adolescents with an ASC diagnosis, and to compare the changes in outcomes between those with a diagnosis and those without.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.