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The field of website quality evaluation attracts the interest of a range of disciplines, each bringing its own particular perspective to bear. This study aims to identify the main characteristics – methods, techniques and tools – of the instruments of evaluation described in this literature, with a specific concern for the factors analysed, and based on these, a multipurpose model is proposed for the development of new comprehensive instruments.

Following a systematic bibliographic review, 305 publications on website quality are examined, the field's leading authors, their disciplines of origin and the sectors to which the websites being assessed belong are identified, and the methods they employ characterised.

Evaluations of website quality tend to be conducted with one of three primary focuses: strategic, functional or experiential. The technique of expert analysis predominates over user studies and most of the instruments examined classify the characteristics to be evaluated – for example, usability and content – into factors that operate at different levels, albeit that there is little agreement on the names used in referring to them.

Based on the factors detected in the 50 most cited works, a model is developed that classifies these factors into 13 dimensions and more than 120 general parameters. The resulting model provides a comprehensive evaluation framework and constitutes an initial step towards a shared conceptualization of the discipline of website quality.

Tropical wetland ecosystems are threatened by climate change but also play a key role in its mitigation and adaptation through management of land use and other drivers. Local-level assessments are needed to support evidence-based wetland management in the face of climate change. This study aims to examine the local communities’ knowledge and perception of climate change in Yala wetland, Kenya, and compare them with observed data on climate trends. Such comparisons are useful to inform context-specific climate change adaptation actions.

The study used a mixed methods approach that combined analysis of climate data with perceptions from the local community. Gridded data on temperature and rainfall for the period from 1981 to 2018 were compared with data on climate change perceptions from semi-structured questionnaires with 286 key informants and community members.

Majority of the respondents had observed changes in climate parameters – severe drought (88.5%), increased frequency of floods (86.0%) and irregular onset and termination of rains (90.9%) in the past 20 years. The perceptions corresponded with climate trends that showed a significant increasing trend in the short rains and the average maximum temperature, high incidence of very wet years and variability in onset and termination of rainfall between 1981 and 2018. Gender, age and education had little influence on knowledge and awareness of climate change, except for frequency of floods and self-reported understanding of climate change. The community perceived the wetland to be important for climate change adaptation, particularly the provision of resources such as grazing grounds during drought.

The study faced challenges of low sample size, use of gridded climate data and reproducibility in other contexts. The results of this study apply to local communities in a tropical wetland in Western Kenya, which has a bi-modal pattern of rainfall. The sample of the study was regional and may therefore not be representative of the whole of Kenya, which has diverse socioeconomic and ecological contexts. Potential problems have been identified with the use of gridded data (for example, regional biases in models), although their usefulness in data scarce contexts is well established. Moreover, the sample size has been found to be a less important factor in research of highly complex socio-ecological systems where there is an attempt to bridge natural and social sciences.

This study addresses the paucity of studies on climate change trends in papyrus wetlands of sub-Saharan Africa and the role of local knowledge and perceptions in influencing the management of such wetlands. Perceptions largely influence local stakeholders’ decisions, and a study that compares perceptions vs “reality” provides evidence for engagement with the stakeholders in managing these highly vulnerable ecosystems. The study showed that the local community’s perceptions corresponded with the climate record and that adaptation measures are already ongoing in the area.

This study presents a case for the understanding of community perceptions and knowledge of climate change in a tropical wetland under threat from climate change and land use change, to inform management under a changing climate.

This paper aims to explore consumer perception of “brand voice” authenticity, brand authenticity and brand attitude when the source of text is disclosed as either artificial intelligence (AI)-generated or human-written.

A 3 × 3 experimental design using Adidas marketing texts disclosed as either “AI” or “human”, or not disclosed was applied to data gathered online from 624 English-speaking students.

Text disclosed as AI-generated is not perceived as less authentic than that disclosed as human-written. No negative effect on brand voice authenticity and brand attitude results if an AI-source is disclosed.

Findings offer brand managers the potential for cost and time savings but emphasise the strong effect of AI technology on perceived brand authenticity and brand attitude.

Results show that brands can afford to be transparent in disclosing the use of AI to support brand voice as communicated in product description or specification or in chatbot text.

The paper aims to expand on the works well documented by Joy Boulamwini and Ruha Benjamin by expanding their critique to the African continent. The research aims to assess if algorithmic biases are prevalent in DALL-E 2 and Starry AI. The aim is to help inform better artificial intelligence (AI) systems for future use.

The paper utilised a desktop study for literature and gathered data from Open AI’s DALL-E 2 text-to-image generator and StarryAI text-to-image generator.

The DALL-E 2 significantly underperformed when it was tasked with generating images of “An African Family” as opposed to images of a “Family”. The pictures lacked any conceivable detail as compared to the latter of this comparison. The StarryAI significantly outperformed the DALL-E 2 and rendered visible faces. However, the accuracy of the culture portrayed was poor.

Because of the chosen research approach, the research results may lack generalisability. Therefore, researchers are encouraged to test the proposed propositions further. The implications, however, are that more inclusion is warranted to help address the issue of cultural inaccuracies noted in a few of the paper’s experiments.

The paper is useful for advocates who advocate for algorithmic equality and fairness by highlighting evidence of the implications of systemic-induced algorithmic bias.

The reduction in offensive racism and more socially appropriate AI can be a better product for commercialisation and general use. If AI is trained on diversity, it can lead to better applications in contemporary society.

The paper’s use of DALL-E 2 and Starry AI is an under-researched area, and future studies on this matter are welcome.

The purpose of this paper is to conceptualise our understanding of public value accounting (PVA) by studying the use and usefulness of performance measurements (PM) as a management tool. The authors do this from a perspective in which they address the complexity of various (sometimes conflicting) assessments of performance measurement and management (PMM) by different stakeholders.

An interpretative case study using qualitative methods. The paper is based on 30 interviews conducted in 2018 and 2019 with respondents working with PMM at different levels, such as politicians, officials and health-care professionals. The study context was Region Stockholm (RS) in Sweden and its health-care division.

PMs become an instrumental tool for PMM, which led to output being promoted above outcome. The authors show that there is a conceptual shortcoming in the discussion of PVA, as the effort needed to achieve outcome-based information might exceed the ability of an organisation to deliver it. The authors address the importance of studying the interaction among different stakeholders, including politicians, the public and media, in research on PVA, as well as possible power relationships among stakeholders.

The authors contribute to the growing research on PVA and its call for more empirical research by offering a more nuanced interpretation of PVA activities. The authors do this by studying PMM and the nature of these activities in a public sector organisation from a multiple-stakeholder perspective.

The management of employee happiness and well-being has been gaining interest in academic research in recent years; however, few studies have focussed on the entrepreneur's perspective. The aim of this paper is to analyse the state of research on women-led businesses, well-being and happiness management.

A bibliometric study has been carried out since 1996, the first year in which publications in this field were detected. In total, 128 papers are identified in the most reliable database, Web of Science Core Collection. A network mapping of authorship, citation and co-occurrence of keywords in scientific publications is shown.

The results of this study confirm that societal changes resulting from crises increase research interest in improving organisational environments and happiness. After the economic crises of 2013, there was a boost, and after the pandemic, there is again a boost in research. More than half of the publications and citations on female entrepreneurship and happiness management are post-pandemic. The study offers some research directions and emphasises the role of gender.

This article brings a new approach to the study of well-being in organisations, highlighting the relevance of the role female leadership plays in promoting happiness at work.

Although supporting and assessing the non-academic “impact” of research are not entirely new developments in higher education, academics and research institutions are under increasing pressure to produce work that has a measurable influence outside the academy. With a view to supporting the solution of complex societal issues with evidence and expertise, and against the background of increased emphasis on impact in the United Kingdom's 2021 Research Excellence Framework (REF2021) and a proliferation of impact guides and tools, this article offers a simple, easy to remember framework for designing impactful research. We call this framework “The 7Cs of Impact” – Context, Communities, Constituencies, Challenge, Channels, Communication and Capture.

Drawing on core elements of the Policy Institute at King's College London's Impact by Design training course and the authors' practical experience in supporting and delivering impact, this paper outlines how this framework can help address key aspects across the lifecycle of a research project and plan, from identifying the intended impact of research and writing it into grants and proposals, to engaging project stakeholders and assessing whether the project has had the desired impact.

While preparations for current and future REF submissions may benefit from using this framework, this paper sets out the “7Cs” with a more holistic view of impact in mind, seeking to aid researchers in identifying, capturing, and communicating how research projects can and do contribute to the improvement in society.

This paper aims that privacy research is divided in distinct communities and rarely considered as a singular field, harming its disciplinary identity. The authors collected 119.810 publications and over 3 million references to perform a bibliometric domain analysis as a quantitative approach to uncover the structures within the privacy research field.

The bibliometric domain analysis consists of a combined directed network and topic model of published privacy research. The network contains 83,159 publications and 462,633 internal references. A Latent Dirichlet allocation (LDA) topic model from the same dataset offers an additional lens on structure by classifying each publication on 36 topics with the network data. The combined outcomes of these methods are used to investigate the structural position and topical make-up of the privacy research communities.

The authors identified the research communities as well as categorised their structural positioning. Four communities form the core of privacy research: individual privacy and law, cloud computing, location data and privacy-preserving data publishing. The latter is a macro-community of data mining, anonymity metrics and differential privacy. Surrounding the core are applied communities. Further removed are communities with little influence, most notably the medical communities that make up 14.4% of the network. The topic model shows system design as a potentially latent community. Noteworthy is the absence of a centralised body of knowledge on organisational privacy management.

This is the first in-depth, quantitative mapping study of all privacy research.

Crowdfunding (CF) has become an increasingly popular means of financing for entrepreneurs and has attracted significant attention from both researchers and practitioners in recent years. The purpose of this study is to investigate the core content and knowledge diffusion paths in the CF field. Specifically, we aim to identify the main topics and themes that have emerged in this field and to trace the evolution of CF knowledge over time.

This study employs co-word clustering and main path analysis (MPA) to examine the historical development of CF research based on 1,528 journal articles retrieved from the Web of Science Core Collection database.

The results of the analysis reveal that CF research focuses on seven themes: sustainability, entrepreneurial finance, entrepreneurship, fintech, social entrepreneurship, social capital, and microcredits. The analysis of the four main paths reveals that equity CF has been the dominant topic in the past years. Recently, CF research has tended to focus on topics such as fintech, the COVID-19 pandemic, competition, Brexit, and policy response.

To the authors' best knowledge, this is the first attempt to explore knowledge diffusion dynamics in the CF field. Overall, the study offers a structure for analyzing the paths through which knowledge is diffused, enabling scholars to effectively manage a large volume of research papers and gain a deeper understanding of the historical, current, and future trends in the development of CF.

Given that online search visibility is influenced by various determinants, and that influence may vary across industries, this study aims in investigating the major predictors of online search visibility in the context of blood banks.

To formalize the online visibility, the authors have found theoretical foundations in activity theory, while to quantify online visiblity the authors have used the search engine optimization (SEO) Index, ranking, and a number of visitors. The examined model includes ten hypotheses and was tested on data from 57 blood banks.

Results challenge shallow domain knowledge. The major predictors of online search visibility are Alternative Text Attribute (ALT) text, backlinks, robots, domain authority (DA) and bounce rate (BR). The issues are related to the number of backlinks, social score, and DA. Polarized utilization of SEO techniques is evident.

The methodology can be used to analyze the online search visibility of other industries or similar not-for-profit organizations. Findings in terms of individual predictors can be useful for marketers to better manage online search visibility.

The acute blood donation problems may be to a certain degree level as the information flow between donors and blood banks will be facilitated.

This is the first study to analyze the blood bank context. The results provide invaluable inputs to marketers, managers, and policymakers.