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This conceptual paper explores the complex, and neglected, area of mentor development in initial teacher education (ITE) in Australia. It focuses on the emotionality of mentoring, drawing on concepts of emotional labour and emotional intelligence to develop a framework of effective mentoring that helps explain the essence of a mentor’s role in supporting preservice teachers.

This conceptual paper draws together mentor-support practice wisdom and research literature from several relevant areas. It draws on constructive developmental theories and complex stage theory to reaffirm the intricate nature of mentor learning and development. This paper critiques the current utilitarian emphasis on mentoring as a way to improve student outcomes without first having clarity on how to improve mentoring itself.

We introduce the mentoring as emotional labour framework as a way to better understand the nature of mentoring within ITE and as a tool for developing more effective mentor supports. We present “exemplar cases”, which are amalgamations of field observations to illustrate aspects of the framework – however, we do not claim they provide evidence of the utility or accuracy of the framework.

Previous research and policy have tended to gloss over the skills required for effective mentoring, whereas this paper places the emotional labour of mentoring front and centre, explicitly conceptualising and describing the personal and interpersonal skills required in a way that aims to support and empower mentors to recognise existing strengths and areas of potential growth.

The research paper aims to study dissatisfaction of teleworking employees in Spain during the Covid-19 health pandemic in order to propose three models: sociodemographic profile of the teleworking dissatisfied employee; advantages and disadvantages for the teleworking dissatisfied employee and advantages for the teleworking dissatisfied employee.

This study uses official open data obtained from the Spanish National Statistical Institute (INE, 2022) through Decision Trees statistical multivariable models implementing Classification and Regression Trees and Recursive Partitioning and Regression Trees techniques to determine the variables that can influence the satisfaction or dissatisfaction of the subjects.

This investigation offers three models with two sociodemographic profiles of dissatisfied teleworking employee, who is a high/middle-level manager/employee around 45 years old, and she/he lives with the partner. Regarding the most important advantage of teleworking, employees consider “use/saving of time” and as disadvantage “worse organization and coordination of work”.

This research provides empirical evidence with inductive reasoning on understanding the challenges of teleworking dissatisfied employees in Spain not only in turbulent times but also in “normalcy” to improve overall teleworker well-being and accomplish company’s and organization’s long-term objectives for better productivity and effectivity. The study has high practical value due to the integral approach incorporating dissatisfaction as a driver that can trigger negative behaviours towards the organizations and that is seldom addressed in the literature. Additionally, this paper could provide some new ideas for accomplishing “Spain Digital 2025” and “Europe’s Digital Decade: 2030” plans on institutional level.

The aim of this study is to empirically investigate the impact of marketing analytics capability on business performance from the perspective of RBV theory.

This study used a survey method to gather information from 225 food processing SMEs registered with the Ghana Enterprise Agency (GEA) in Ghana’s eastern region. A structural equation modeling (SEM) path analysis was used to assess the impact of marketing analytics capability (MAC) on the performance of SMEs.

The results of the study show that MAC significantly and positively affect the financial performance (FP), customer performance (CF), internal business process performance (IBPP) and learning and growth performance (LGP) of Ghanaian SMEs. The findings of this study also illustrated the significance of MAC determinants, including marketing analytics skills (MAS), data resource management (DRM) and data processing capabilities (DPC), in achieving SME success in Ghana.

The research’s conclusions give RBV theory strong credence. The results of this study also provide credence to previous research finding that SMEs should view MAC and its determinants (i.e. DRM, DPC, MAS) as a crucial strategic capability to improve their performance (i.e. FP, CF, IBPP, LGP). With regard to its contribution, this study broadens the body of knowledge on MAC and SME performance, particularly in the context of an emerging economy.

Within food industry several changes and innovations are affecting the management of the entire supply chain (production, logistics, etc.). As strategy for the survival and competition, digitalization has assumed a crucial role during the pandemic emergence by causing the reconfiguration of traditional chains and business models. Framed in these premises, the research analyses how digital technologies have innovated the sub-chains of bakery products and pasta within food industry with reference to customers' interactions, delivery and marketing during the COVID-19 pandemic emergence.

Moving from a critical literature review about the perspectives of digital technologies within the tradition of food industry, action research has been adopted to analyze in deep a case study of the start-up “ArteBianca Delivery” located in South Italy. Through this method, researchers have been deeply involved within the start-up to face the challenge of transforming the marketing and customer care into digital ones due to the COVID-19 restriction.

Findings provide empirical evidence about the reconfiguration of the traditional business model of a family firm in the food sector into a digital one with the start-up “ArteBianca Delivery”. The marketing, delivery, e-commerce and customer care components of the business models have been supported and enhanced through the adoption of digital tools, such as mobile applications and social technologies useful both for users and for a more urgent digitization of company.

Implications for practice can be identified into the pattern of digital transformation implemented as well as in the opportunity of replication and contextualization of the results to other companies looking for setting up a digital strategy.

Elements of original contribution can be identified into: (1) the exploration of digital transformation in food family firms caused by the pandemic emergence, (2) the contextualization of the digital transformation to the sub-chains of bakery and pasta and (3) the geographical location of the case.

Children’s sensory involvement refers to the degree to which children engage their senses, such as sight, touch, taste, smell and hearing, in their interactions with the environment. In the context of parents' purchase decisions, children’s sensory involvement pertains to how children's sensory involvement influences the purchasing decisions made by their parents. The aim of this study was to evaluate the effect of children's sensory involvement on parents’ purchase decisions considering the mediating role of the parent’s attitude.

In this study, a structured questionnaire survey was conducted with parents of children aged 7–12 in Isfahan, Iran. The sample consisted of 210 parents, aimed at elucidating the relationship between variables. Structural equation modeling (SEM) was employed to analyze the relationship between variables.

Results showed a significant relationship between children’s sensory involvement and parents’ purchase decisions, children’s sensory involvement and parents’ attitudes and parents’ attitudes and purchase decisions. It was concluded that children’s sensory involvement could indirectly influence the parents’ purchase decisions considering the mediating role of parents' attitudes.

In today's business landscape, it is imperative for organizations to discern the multitude of factors influencing consumers' purchasing decisions. Among these, family dynamics play a substantial role, with children often exerting a strong influence on their parents' buying choices. Despite the acknowledged importance of this dynamic in existing literature, the specific impact of children's sensory involvement on parental purchasing decisions remains largely unexplored. Therefore, this paper aims to fill this gap in the literature by shedding light on the role of children's sensory involvement in shaping parental buying behaviors.

Inaction inertia occurs when people are less likely to act on a similar but inferior option after missing a superior opportunity, compared to if they had not missed out. This study aims to explore how promotional formats and their sequence affect the inaction inertia effect in online shopping, under the assumption of economic equivalence.

The authors performed two online experiments and analyzed the data by analysis of variance.

The findings indicate that, under the premise of economic equivalence: Monetary promotions exhibit a higher inaction inertia effect on consumers than nonmonetary promotions. When consumers miss a more favorable promotion and subsequently encounter a relatively less attractive one presented in a different promotional format, the inaction inertia effect is lower than when reencountering the same promotion format. When consumers miss a better monetary promotion and presently encounter a relatively less attractive nonmonetary promotion, the inaction inertia effect is lower than when they miss a superior nonmonetary promotion and currently encounter a relatively less attractive monetary promotion.

This study reveals the sequence effects of promotional formats, indicating that nonmonetary promotions following monetary ones effectively reduce inaction inertia. A strategically sequenced set of formats enhances consumer recommendations, mitigating inaction inertia. These findings open new research paths, providing insights into the impact of promotional format sequences on the inaction inertia effect. Consequently, this knowledge helps e-retailers in implementing effective promotional strategies and driving online purchases.

Escalation in the number of online food ordering platforms, along with extensive junk food marketing, lucrative offers and discounts, innovation in food flavors, and doorstep delivery of food, have triggered the consumption of high-calorie and unhealthy food products which pose serious threats to the health and future well-being of individuals by making them more obese. To date, several public policy frameworks have been developed to confront obesity; however, their efficacy seems debatable. Directionally, the objective of this study is to highlight the potential influence of “digital nudging” which aims at steering individuals in desired directions, at the same time delimiting their freedom of choice. The study also establishes the effectiveness of digital nudges promoting a healthy lifestyle by steering individuals toward healthier food choices. The author strongly believes that this conceptual perusal will offer immense inputs to healthy food marketers and researchers alike in addressing the matters of obesity. Addressing the menace of obesity calls for joint efforts of the government, the public, researchers, and more specifically food product manufacturers/marketers who should incorporate healthier food options into their portfolios. E-tailers are also urged to adopt such practices in virtual markets and promote healthier food options to effectively tackle obesity.

This study aims to assess green food packages’ role in sustaining a restaurant’s curbside pickup service on three stages of consumer experiences: choosing a restaurant, evaluating their experiences of a recent purchase and weighing their post-consumption behavioral intentions after the recent purchase.

The service encounters framework and relevant literature guided the development of the questionnaire. A Qualtrics panel data of 314 valid questionnaires were collected and analyzed with choice experience, ordinary least squares regression and PROCESS modeling.

First, word-of-mouth (WOM) and function encounters significantly influence consumers’ first-time curbside pickup purchasing decisions. Then, service results encounter (besides distributor encounter) most significantly affects consumers’ overall curbside pickup experience. Finally, green food packages increase consumers’ shares of future purchases through their positive WOM intentions and extra efforts of revisiting the restaurant. Consumers’ perceived importance of green restaurant practices strengthens green food packages’ positive impact on extra efforts.

This study provides operational and marketing insights for restaurants to use food packages and sustain their curbside pickup service.

Besides assessing consumers’ evaluations and behavioral intentions for an off-premises restaurant service expected to stay beyond the pandemic, this research uniquely focuses on green food packages, a sustainability issue lacking research attention. The findings add new empirical insights to studies about sustainability and restaurant/food–retail operations.

This study aims to explore how sustainable business models (SBM) can be developed within agri-innovation systems (AIS) and emphasize an integration of the two with a systemic understanding for reducing food loss and value loss in postharvest agri-food supply chain.

This study conducted longitudinal qualitative research in a developing country with food loss challenges in the postharvest supply chain. This study collected data through multiple rounds of fieldwork, interviews and focus groups over four years. Thematic analysis and “sensemaking” were used for inductive data analysis to generate rich contextual knowledge by drawing upon the lived realities of the agri-food supply chain actors.

First, this study finds that the value losses are varied in the supply chain, encompassing production value, intrinsic value, extrinsic value, market value, institutional value and future food value. This happens through two cumulative effects including multiplier losses, where losses in one model cascade into others, amplifying their impact and stacking losses, where the absence of data stacks or infrastructure pools hampers the realisation of food value. Thereafter, this study proposes four strategies for moving from the loss-incurring current business model to a networked SBM for mitigating losses. This emphasises the need to redefine ownership as stewardship, enable formal and informal beneficiary identification, strengthen value addition and build capacities for empowering communities to benefit from networked SBM with AIS initiatives. Finally, this study puts forth ten propositions for future research in aligning AIS with networked SBM.

This study contributes to understanding the interplay between AIS and SBM; emphasising the integration of the two to effectively address food loss challenges in the early stages of agri-food supply chains. The identified strategies and research propositions provide implications for researchers and practitioners seeking to accelerate sustainable practices for reducing food loss and waste in agri-food supply chains.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.