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This final chapter draws together the key findings presented in the preceding pages and considers emergent conclusions and implications for mental health and wellbeing in schools. The author draws upon the evidence presented by the book’s eclectic range of contributors and the health promoting schools’ (HPSs) framework to provide a road map of what an effective whole school approach (WSA) might look like moving forward. Crucially, lessons from recent crises in education, including COVID-19-driven changes in pathways to support, are highlighted as pivotal factors in a future vision which sees schools as an effective and sustainable frontline force to help tackle the global burden of child and adolescent mental health through a proactive, preventive stance. With this future focus in mind, next steps are considered in terms of developing effective and sustainable mental health and wellbeing support for all within healthy school settings.

This study aims to examine the visual effects of cause-related marketing (CM) posts on Instagram, with a focus on image resolution and consumer engagement.

Three studies were conducted through an experimental design. Study 1 (N = 155) uncovered the mediation underlying the effects of image quality (low and high image resolution). Study 2 (N = 160) replicated the findings of the first study and extended the investigation by examining the mediator (fluency) and moderator (visual sensitivity). Study 3 (N = 291) further extended the effects of image resolution by demonstrating its interactive effects with the visual complexity of an Instagram post design in a 2 × 2 factorial experiment.

The serial mediation analysis demonstrated that high image resolution CM posts yielded more favorable evaluations in terms of brand credibility and information costs saved, subsequently leading to positive brand attitudes, purchase intentions and increased Instagram engagement. Processing fluency mediated image effects on brand credibility, while individual differences in visual sensitivity moderated the image effects. The image resolution effects were greater for visually complex CM posts compared to simple ones.

To one's best knowledge, little to no research has examined the image quality of Instagram posts in the context of CM and the extent to which such visual cues can affect consumers' brand evaluations and engagement on the platform.

Despite its practical significance, there exists a notable gap in understanding the specific role of CM posts on Instagram and the impact of visual elements on consumer behaviors. The current research findings aim to bridge the research gap.

This paper explores the nature and operations of the supply chain that serves disadvantaged groups. With the increasing reliance on supplementary food provision through food aid, the authors seek to emphasise efficiency and sustainability in these supply chains.

Semi-structured interview data from 32 senior managers and experts from both commercial and food aid supply chains were abductively analysed to develop a relationship-based map of the food chains that serve disadvantaged groups.

Disadvantaged groups are served by a hybrid food supply chain. It is an interconnected supply chain bringing together the commercial and the food aid supply chains. This chain is unsurprisingly plagued with various challenges, the most critical of which are limited expertise and resources, operational inefficiencies, prohibitive logistics costs and a severe lack of collaboration.

This study identifies the currently limited role of logistics companies in surplus food redistribution and highlights future pathways. Additionally, the authors present useful actionable propositions for managers, practitioners and policymakers.

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.

Inspired by a food bank distribution operation, this paper aims to study synchronized vehicle routing for equitable and effective food allocation. The primary goal is to improve operational efficiency while ensuring equitable and effective food distribution among the partner agencies.

This study introduces a multiobjective Mixed Integer Programming (MIP) model aimed at addressing the complex challenge of effectively distributing food, particularly for food banks serving vulnerable populations in low-income urban and rural areas. The optimization approach described in this paper places a significant emphasis on social and economic considerations by fairly allocating food to food bank partner agencies while minimizing routing distance and waste. To assess the performance of the approach, this paper evaluates three distinct models, focusing on key performance measures such as effectiveness, equity and efficiency. The paper conducts a comprehensive numerical analysis using randomly generated data to gain insights into the trade-offs that arise and provide valuable managerial insights for food bank managers.

The results of the analysis highlight the models that perform better in terms of equity and effectiveness. Additionally, the results show that restocking the vehicles through the concept of synchronization improves the overall quantity of food allocation to partner agencies, thereby increasing accessibility.

This paper contributes significantly to the literature on optimization approaches in the field of humanitarian logistics.

This study provides food bank managers with three different models, each with a multifaceted nature of trade-offs, to better address the complex challenges of food insecurity.

This paper contributes significantly to social responsibility by enhancing the operational efficiency of food banks, ultimately improving their ability to serve communities in need.

To the best of the authors’ knowledge, this paper is the first to propose and analyze this new variant of vehicle routing problems in nonprofit settings.

The main purpose of this study is to assess the influence of business analytics (BA) on supply chain ambidexterity (SCA) and market learning (ML) in the context of Iran as a developing country.

The study population encompasses a range of key positions such as senior managers, supply chain managers, senior IT managers and senior marketing and marketing research managers in Iran. Through a survey, a questionnaire was designed to gather data from these individuals. The data collected from a total of 214 participants underwent rigorous analysis using structural equation modeling.

Findings revealed BA has a positive influence on SCA and ML. Furthermore, the study found that distinct facets of ML, namely, exploratory and exploitative learning, exerted a positive influence on SCA. Additionally, the investigation uncovered that the mechanisms of exploratory ML and exploitative ML play a partially mediating role in the relationship between BA and SCA.

It is prudent to acknowledge that the study’s sampled entities were exclusively Iranian companies, potentially curtailing the extent of generalizability of our findings.

This research contributes valuable theoretical insights and practical implications to policymakers and top managers of organizations, particularly the surveyed organizations to formulate and implement an appropriate strategy to avail of BA techniques toward enhancing SCA. Also, this study provides significant insights into the determinants of SCA and demonstrates how organizations can leverage data analytics and ML to attain sustained growth and ambidexterity within the supply chain context.

This study investigates the relationships between data governance (DG), business analytics capabilities (BAC), and decision-making performance (DMP), with a focus on the mediating effects of big data literacy (BDL) and data analytics competency (DAC).

The study was conducted with 178 experienced managers in public service organizations, using a quantitative approach. Structural equation modeling (SEM) and mediation tests were employed to analyze the data.

The findings reveal that DG and BDL are critical antecedents for developing analytical capabilities. Big data literacy mediates the relationship between DG and BAC, while BAC mediates the relationship between DG and DMP. Furthermore, DAC mediates the relationship between BA capabilities and DMP, explaining most of the effect of BAC on DMP.

These results highlight the importance of DG in fostering BDL and analytical skills for improved decision-making in organizations.

By prioritizing DG practices that promote BDL and analytical capabilities, organizations can leverage business analytics to enhance decision-making.