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In a dynamic, continuously evolving sports landscape, social media have become an indispensable tool for sports organizations to cultivate meaningful connections with fans. The rapid pace of technological advancements has elevated these digital platforms from a supplementary role to a pivotal position within strategic management frameworks. The existing literature explores how football clubs can utilize social media, but analyzing social media strategies within the context of football leagues is lacking. The absence of comparative studies benchmarking clubs across different geographical regions while simultaneously analyzing multiple social media platforms is especially noteworthy. In this study, a comprehensive analysis of social media engagement is undertaken within esteemed football leagues spanning Europe, South America and North America.

Drawing on relationship marketing and employing content analysis as a methodological tool, the study examined 10,772 posts from the official accounts of eight football leagues on Facebook, Twitter and Instagram.

Across the leagues, the findings reveal that content quality drives engagement more than frequency. In addition, several format combinations were identified that facilitate engagement and Instagram emerged as the top social media platform for generating fan engagement.

This is one of the first empirical studies focusing on optimizing the use of social media to amplify fan engagement across various geographies and social media accounts and formats simultaneously.

Senior Indigenous leadership positions across the Australian higher education sector has increased over the past decade. Despite this advancement, there is limited understanding in terms of how to best integrate Indigenous leadership into existing governance structures of Australian universities. In 2018 the Walan Mayiny: Indigenous Leadership in Higher Education project commenced, aimed at establishing a model of best practice for the inclusivity of Indigenous leadership in higher education governance structures. This article presents key findings from the project, namely, a model of senior Indigenous leadership within the Australian universities based on the perceptions of a group of Indigenous academics.

Through qualitative semi-structured interviews with Indigenous academic staff, the perceived value, characteristics and challenges of senior Indigenous leadership were examined. The varying opinions held by Indigenous academics in relation to the qualifications and experience required to fulfil a senior Indigenous leadership position were also highlighted. In doing so, a model of senior Indigenous leadership within the Australian higher education system is presented. The model of best practice presented in this article is underpinned by Indigenous Institutional Theory (Coates et al., 2022), a theoretical framework developed from the Walan Mayiny study.

The research findings highlight the diverse opinions of Indigenous academics in relation to the qualifications and experience required to fulfil a senior Indigenous leadership position. The six essential components are built upon the core characteristics, values and behaviours that senior Indigenous leaders need to have according to Indigenous academics, in order to advance Indigenous success within the academy.

Given Australian universities are being called upon to ensure that senior Indigenous leaders are in the best position possible to forge institutional change, senior Indigenous leaders within the academy may find the contextual Indigenous leadership model beneficial. The model allows one to uphold cultural integrity and fulfil the responsibilities and obligations of their higher education institution, while being able to serve their Indigenous colleagues and communities, leading to the advancement of Indigenous higher education outcomes. Importantly, the model can be adapted to suit all First Nations Peoples globally, who also find themselves working within the shackles of Western institutions.

This study aims to investigate how art events can enrich novice visitors by transforming their practices.

This research uses an interpretive case study of the art exhibition “1/1/1/1/1” in the Oppenheim gallery in Wroclaw. It draws on multiple sources of evidence, namely, novice visitors’ interviews, observation including photo studies and content analysis of art-makers’ mediation sources. This study is an example of contextual theorizing from case studies and participatory action research with researchers as change agents.

The evidence highlights that aesthetic values and experiences are contextual to practices and are transformable into other values. The findings illustrate the role of practice theory in studying how art-makers inspire the transformation of practices, including values driving the latter.

The findings provide implications for transformations of co-creating contextual values in contemporary visual art consumption and customer experience management.

Practical implications to arts organizations are also provided regarding cultural mediation conducted by art-makers. Exhibition makers should explain the meanings of the particularly visible artefacts to allow visitors to develop a congruent understanding of the meanings. The explanations should not provide ready answers or solutions to the problem art-makers suggest to rethink.

The social implication of our findings is that stakeholders in artistic ventures may undertake adequate, qualified and convergent actions to maintain or transform the defined interactive practices between them in co-creating contextual aesthetic values.

The study provides new insights into co-creating values in practices in the domain of contemporary art exhibitions by bringing the practice theory together with an audience enrichment category, thus illustrating how novice visitors get enriched by transforming their practices led by contextual values of “liking” and “understanding”.

This study presents the development of a supply chain (SC) observatory, which is a benchmarking solution to support companies within the same industry in understanding their positioning in terms of SC performance.

A case study is used to demonstrate the set-up of the observatory. Twelve experts on automatic equipment for the wrapping and packaging industry were asked to select a set of performance criteria taken from the literature and evaluate their importance for the chosen industry using multi-criteria decision-making (MCDM) techniques. To handle the high number of criteria without requiring a high amount of time-consuming effort from decision-makers (DMs), five subjective, parsimonious methods for criteria weighting are applied and compared.

A benchmarking methodology is presented and discussed, aimed at DMs in the considered industry. Ten companies were ranked with regard to SC performance. The ranking solution of the companies was on average robust since the general structure of the ranking was very similar for all five weighting methodologies, though simplified-analytic hierarchy process (AHP) was the method with the greatest ability to discriminate between the criteria of importance and was considered faster to carry out and more quickly understood by the decision-makers.

Developing an SC observatory usually requires managing a large number of alternatives and criteria. The developed methodology uses parsimonious weighting methods, providing DMs with an easy-to-use and time-saving tool. A future research step will be to complete the methodology by defining the minimum variation required for one or more criteria to reach a specific position in the ranking through the implementation of a post-fact analysis.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

Given the importance of the growing segmentation of ageing consumers and their increasing interaction with the Internet, digital marketing scholars are becoming more interested in this market. Prior research needs to pay more attention to this market in many contexts of digital marketing. This study aims to provide insights into ageing consumers’ content usage, content typology choices, and online brand advocacy (OBA).

Semi-structured interviews were applied, and 16 consumers from Southern Europe aged 55+ were included. The interviews were transcribed and examined following the principles of content analysis.

According to the research, older consumers display their usage and concerns regarding online content. They have different decision-making processes depending on whether they are purchasing products or services. Likewise, their choices of content typology vary based on the utilitarian or hedonic product category.

This study contributes to the literature by providing insights into this growing segmentation and proposing an OBA framework for older consumers related to content marketing. Finally, the study suggests that older consumers are passive online and active offline brand advocates.

To combat climate change, protect biodiversity, maintain water quality, facilitate a just transition for workers and engage citizens and communities, a diversity of stakeholders across multiple levels work together and collaborate to co-create mutually beneficial solutions. This paper aims to illustrate how a 7.5-year collaboration between local communities, researchers, academics, companies, state agencies and policymakers is contributing to the reframing of industrial harvested peatlands to regenerative ecosystems and carbon sinks with impacts on ecological, economic, social and cultural systems.

The European Union LIFE Integrated Project, Peatlands and People, responding to Ireland’s Climate Action Plan, represents Europe’s largest rehabilitation of industrially harvested peatlands. It makes extensive use of marketing research for reframing strategies and actions by partners, collaborators and communities in the evolving context of a just transition to a carbon-neutral future.

The results highlight the ecological, economic, social and cultural reframing of peatlands from fossil fuel and waste lands to regenerative ecosystems bursting with biodiversity and climate solution opportunities. Reframing impacts requires muddling through the ebbs and flows of planned, possible and unanticipated change that can deliver benefits for peatlands and people over time.

At 3 of 7.5 years into a project, the authors are muddling through how ecological reframing impacts economic and social/cultural reframing. Further impacts, planned and unplanned, can be expected.

This paper shows how an impact planning canvas tool and impact taxonomy can be applied for social and systems change. The tools can be used throughout a project to understand, respond to and manage for unplanned events. There is constant learning, constantly going back to the impact planning canvas and checking where we are, what is needed. There is action and reaction to each other and to the diversity of stakeholders affected and being affected by the reframing work.

This paper considers how systemic change through ecological, economic, social and cultural reframing is a perfectly imperfect process of muddling through which holds the promise of environmental, economic, technological, political, social and educational impacts to benefit nature, individuals, communities, organisations and society.

This study aims to help understand individual donors’ preferences over different designs of humanitarian–business partnerships in managing humanitarian operations and to help understand if donors’ preferences align with their actual donation behavior.

Choice-based conjoint analysis was used to understand donation preferences for partnership designs, and a donation experiment was performed using real money to understand the alignment of donors’ preferences with actual donation behavior.

The results show that partnering with the business sector can be a valuable asset for humanitarian organizations in attracting individual donors if these partnerships are managed well in terms of partnership strategy, partnership history and partnership report and disclosure. In particular, the study finds that the donation of services and products from businesses corporations to humanitarian organizations are preferable to individual donors, rather than cash. Furthermore, donors’ preferences are not necessarily aligned with actual donation behavior.

The results highlight the importance of presenting objective data on projects to individual donors. The results also show that donors value the provision of services and products by business corporations to humanitarian operations.

Partnerships between humanitarian organizations and business corporations are important for the success of humanitarian operations. However, little is known about which partnership designs are most preferable to individual donors and have the biggest chance of being supported financially.

This paper addresses the issue of the underrepresentation of women in leadership positions within the information and communication technology (ICT) and engineering fields. The study examines the complex issue of vertical segregation and gender equality by exploring the barriers that women encounter and the potential coping strategies they adopt to advance in their careers.

The study employs a qualitative approach, conducting semi-structured interviews with 17 women at middle and upper management levels in the ICT and engineering sectors. This approach aims to better understand women's workplace experiences and gain deeper insights into the nature of the barriers they face.

The study identifies four main factors hindering women's progression toward senior management positions, namely working hours, work-family conflict, social role stereotypes and second-generation bias. The results also offer useful insights into the coping strategies adopted by women to overcome these barriers.

The study highlights the persisting underrepresentation of women in senior positions, indicating a societal and organizational lag in terms of inclusion and equity. It underscores the importance of developing effective policies to address the challenges faced by employees striving to balance their work and family commitments. Training is recognized as an important tool for raising awareness about gender stereotypes among employees and reducing second-generation bias.

This study provides valuable lessons derived from its findings, including potential strategies that organizations can implement to help women navigate and overcome workplace barriers in the ICT and engineering fields.

This research addresses a need in early childhood education for evidence-based teaching strategies that build emotional self-regulation skills in young children. The intervention assessed in this study focused on increasing the emotion vocabulary of preschool-aged students.

This mixed-methods, quasi-experimental study evaluated the impact a dialogic reading approach combined with direct instruction of emotion words during a shared book-reading activity had on students' emotion vocabulary knowledge. The study was conducted in a licensed daycare center in a suburb of Chicago, Illinois, with ten four- and five-year-old students. Pre- and post-session surveys assessed the intervention's impact on the students' receptive and expressive vocabulary knowledge, and observation notes captured the students' responses to the intervention activities.

The results showed significant increases with small to medium effect sizes between the students’ pre- and post-session survey scores for both receptive and expressive emotion vocabulary knowledge, a strong positive correlation between the level of student engagement during the intervention and their emotion vocabulary assessment scores, and the impact other variables had on the intervention’s effectiveness.

This research provides information on a culturally adaptable and quickly learned teaching strategy that could be used to build emotional self-regulation skills in the early childhood classroom.

This research uniquely applies this intervention as a universal strategy with preschool-aged children.