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The authors report a study of heritage conservation linked to rural small-town regeneration in Aotearoa New Zealand. The purpose of this study is to answer the question: how, with limited local resources, do the residents and administrators of small settlements conserve historic heritage in the processes of rural regeneration?

This research is based on an analysis of physical heritage objects (buildings, artefacts and landscapes), associated regulatory arrangements, archival material, news media reporting, community group newsletters and photography. The authors use the river-side town of Rakaia and its environs in Te Waipounamu/the South Island of Aotearoa New Zealand to answer the research question.

This research found that in a context of limited resources, volunteers, supported by small businesses and local and central government, can contribute positively to the conservation and interpretation of heritage as part of wider rural regeneration activities.

There is only limited writing on the links between heritage conservation, rural regeneration and the development of small towns. To advance the debate, the authors combine ideas about community-led heritage conservation and management with concepts drawn from rural studies, particularly the multifunctional rural space paradigm. This allows us to explore heritage conservation in a context of rapid rural change.

Websites, search engines, recommender systems, artificial intelligence and digital libraries have the potential to support serendipity for unexpected interaction with information and ideas which would lead to favored information discoveries. This paper aims to explore the current state of research into serendipity particularly related to information encountering.

This study provides bibliometric review of 166 studies on serendipity extracted from the Web of Science. Two bibliometric analysis tools HisCite and RStudio (Biblioshiny) are used on 30 years of data. Citation counts and bibliographic records of the papers are assessed using HisCite. Moreover, visualization of prominent sources, countries, keywords and the collaborative networks of authors and institutions are assessed using RStudio (Biblioshiny) software. A total of 166 papers on serendipity were found from the period 1989 to 2022, and the most influential authors, articles, journals, institutions and countries among these were determined.

The highest numbers of 11 papers were published in the year 2019. Makri and Erdelez are the most influential authors for contributing studies on serendipity. “Journal of Documentation” is the top-ranking journal. University College London is the prominent affiliation contributing highest number of studies on serendipity. The UK and the USA are the prominent nations contributing highest number of research. Authorship pattern for research on serendipity reveals involvement of single author in majority of the studies. OA Green model is the most preferred model for archiving of research articles by the authors who worked on serendipity. In addition, majority of the research outputs have received a citation ranging from 0 to 50.

To the best of the authors’ knowledge, this paper may be the first bibliometric analysis on serendipity research using bibliometric tools in library and information science studies. The paper would definitely open new avenues for other serendipity researchers.

The purpose of this research is to (1) identify factors affecting food choices of young adults in Canada based on environmental perceptions, personal and behavioral factors as determinants of eating behaviors; (2) segment Canadian young adults based on the importance of the identified factors in their food choices.

An online survey was administered to Canadians aged between 18 and 24 to collect data on socio-demographic factors and eating behaviors (N = 297). An exploratory factor analysis (EFA) was used to identify the main factors affecting eating behaviors in young adults, followed by K-means clustering to categorize the respondents into consumer segments based on their propensity to agree with the factors.

Six factors were extracted: beliefs (ethical, environmental and personal); familiarity and convenience; joy and experience; food influencers and sociability; cultural identity; and body image. Using these factors, six consumer segments were identified, whereby members of each segment have more similar scores on each factor than members of other segments. The six consumer segments were: “conventional”; “concerned”; “indifferent”; “non-trend follower”; “tradition-follower”; and “eat what you love”.

Identifying major factors influencing eating behaviors and consumer segmentation provides insights on how eating behaviors might be shaped. Furthermore, the outcomes of this study are important for designing effective interventions for shaping eating behaviors particularly improving sustainable eating habits.

Transport industry is one of the leading accident causing industries all around the world. Personal attributes like educational qualification, work experience, marital status, consumption of alcohol, hours of work of bus drivers are known to influence such accidents. In the present study, the mediating effects of Workplace spirituality (WS) among bus drivers between the personal attributes and job performance variables like self-reported accidents, punishments and appraisals are carried out.

A mediation analysis of WS between personal attributes and job performances of bus drivers is conducted in the present study. Since there is scarcity of studies measuring the effects of WS of bus drivers, new scale to measure WS was developed. The study was carried out in the government-owned road transport corporation in the state of Kerala, India. Responses were obtained from 617 male drivers.

Using exploratory factor analysis, four factors were identified, namely Meaningful work, Sense of Community, Mindfulness and Compassion. Confirmatory factory analysis provided good fit. The intercorrelations of personal attributes of drivers (independent variables) between WS factors (mediating variables) and job performance variables (dependent variables) were found out. Mediation analysis showed complete mediation of WS factors between marital status, alcohol consumption, hours of work and job performances like number of self-reported accidents, punishments and appraisals. The WS levels of drivers decrease significantly as working hours per goes beyond the legally allowed working hours.

Psychological attributes like Mindfulness, Sense of community, compassion etc. which are collectively known as WS influence the job performances of employees in other industries. Yet it is not studied in the transport industry. Hence, in the present study, the levels of WS are studied among bus drivers of government-owned road transport corporation in the State of Kerala, India.

The purpose of this research is to investigate the acceptance and support of neurodiverse people in society, with a focus on autism, and to use this to propose a framework to enhance inclusivity that can inform pedagogy within the education sectors.

Three case studies from higher education have been presented and mapped onto a multi-dimensional spectrum of characteristics normally associated with autistic people. Further examples have been taken from the general population and these have been used, along with user scenarios to propose a framework for inclusivity.

A framework, the human spectrum, has been proposed which encompasses all of society, regardless of diagnoses and within which people have mobility in terms of their characteristics. It is proposed that this framework should be incorporated into pedagogy in primary, secondary and tertiary education so that teaching and assessment is inclusive and so that people’s understanding of human nature is built from an early age to counter stigma and herd mentality, or othering.

The contribution of this paper could have significant implications for society as the framework provides a structure to enable people to consider others with new perspectives.

The framework proposed provides a new and original way of shaping the way people think within the education sector and elsewhere.

Gender bias in artificial intelligence (AI) should be solved as a priority before AI algorithms become ubiquitous, perpetuating and accentuating the bias. While the problem has been identified as an established research and policy agenda, a cohesive review of existing research specifically addressing gender bias from a socio-technical viewpoint is lacking. Thus, the purpose of this study is to determine the social causes and consequences of, and proposed solutions to, gender bias in AI algorithms.

A comprehensive systematic review followed established protocols to ensure accurate and verifiable identification of suitable articles. The process revealed 177 articles in the socio-technical framework, with 64 articles selected for in-depth analysis.

Most previous research has focused on technical rather than social causes, consequences and solutions to AI bias. From a social perspective, gender bias in AI algorithms can be attributed equally to algorithmic design and training datasets. Social consequences are wide-ranging, with amplification of existing bias the most common at 28%. Social solutions were concentrated on algorithmic design, specifically improving diversity in AI development teams (30%), increasing awareness (23%), human-in-the-loop (23%) and integrating ethics into the design process (21%).

This systematic review is the first of its kind to focus on gender bias in AI algorithms from a social perspective within a socio-technical framework. Identification of key causes and consequences of bias and the breakdown of potential solutions provides direction for future research and policy within the growing field of AI ethics.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-08-2021-0452

The goal of this narrative review was to look at the link between the Mediterranean diet (MD) and the telomere length. Furthermore, this study aims to understand the impact of the MD on obesity-related telomere length.

Relevant literature was reviewed to explore the potential influence of the MD on telomere length and its association with obesity.

The MD is one of the healthiest diets of all known dietary patterns, and it is also linked to the telomere length. Except for fruits and vegetables, the main findings for other MD components are inconsistent. In terms of antioxidant and antiinflammatory properties, using the MD as a weight loss approach is a good method. For predicting changes in obesity characteristics, the initial telomere length is critical. However, there are not many studies in the field that have looked at the MD as a weight loss approach and its link to the telomere length. As a result, more research is needed to understand these connections in various groups.

This study is unique since it examines the MD, telomere length and obesity-related consequences. This study examines the MD, telomere length and obesity to determine if the MD can help lose weight while maintaining telomere length. As there are few studies on MD weight loss and telomere length, the work emphasizes the need for greater research in this area. This study fills a research gap and improves the understanding of nutrition, telomere biology and obesity-related outcomes.

This case is designed to enable students to understand the role of women in artificial intelligence (AI); understand the importance of ethics and diversity in the AI field; discuss the ethical issues of AI; study the implications of unethical AI; examine the dark side of corporate-backed AI research and the difficult relationship between corporate interests and AI ethics research; understand the role played by Gebru in promoting diversity and ethics in AI; and explore how Gebru can attract more women researchers in AI and lead the movement toward inclusive and equitable technology.

The case discusses how Timnit Gebru (She), a prominent AI researcher and former co-lead of the Ethical AI research team at Google, is leading the way in promoting diversity, inclusion and ethics in AI. Gebru, one of the most high-profile black women researchers, is an influential voice in the emerging field of ethical AI, which identifies issues based on bias, fairness, and responsibility. Gebru was fired from Google in December 2020 after the company asked her to retract a research paper she had co-authored about the pitfalls of large language models and embedded racial and gender bias in AI. While Google maintained that Gebru had resigned, she said she had been fired from her job after she had raised issues of discrimination in the workplace and drawn attention to bias in AI. In early December 2021, a year after being ousted from Google, Gebru launched an independent community-driven AI research organization called Distributed Artificial Intelligence Research (DAIR) to develop ethical AI, counter the influence of Big Tech in research and development of AI and increase the presence and inclusion of black researchers in the field of AI. The case discusses Gebru’s journey in creating DAIR, the goals of the organization and some of the challenges she could face along the way. As Gebru seeks to increase diversity in the field of AI and reduce the negative impacts of bias in the training data used in AI models, the challenges before her would be to develop a sustainable revenue model for DAIR, influence AI policies and practices inside Big Tech companies from the outside, inspire and encourage more women to enter the AI field and build a decentralized base of AI expertise.

This case is meant for MBA students.

Teaching Notes are available for educators only.

CCS 11: Strategy