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Human perceptions, attitudes, and relationships are shaped by worldviews and values. The rich mosaic of worldviews in today's fast-changing global village, where cultures interact and information flows freely, challenge educators and students. Worldviews influence problem modeling and solutions. Worldviews give us psychological confidence that the world is as we see it, safe, secure, and belonging. Each worldview is consistent with the assumptions, ideals, and analytical processes. Values define behavior, attitudes and decision-making. The global higher education system's long history and recent developments in globalization, technological innovations, and internationalization make it even more complex. Globally, higher education is evolving rapidly. Global political, economic, social, technological, and environmental factors promote rapid change. Higher education institutions have struggled to adapt to these developments due to limited resources and capacity. Growing demand has created new business models and institutions. Access, equity, inclusion, and quality are new issues that emerged. To be relevant in a rapidly changing environment, higher education institutions must adapt to the knowledge society and growing need for access. This anthology contains 14 thought-provoking studies on worldviews and values in teaching-learning, curricula, assessment, and outcomes.

Work-life flexibility policies (e.g., flextime, telework, part-time, right-to-disconnect, and leaves) are increasingly important to employers as productivity and well-being strategies. However, policies have not lived up to their potential. In this chapter, the authors argue for increased research attention to implementation and work-life intersectionality considerations influencing effectiveness. Drawing on a typology that conceptualizes flexibility policies as offering employees control across five dimensions of the work role boundary (temporal, spatial, size, permeability, and continuity), the authors develop a model identifying the multilevel moderators and mechanisms of boundary control shaping relationships between using flexibility and work and home performance. Next, the authors review this model with an intersectional lens. The authors direct scholars’ attention to growing workforce diversity and increased variation in flexibility policy experiences, particularly for individuals with higher work-life intersectionality, which is defined as having multiple intersecting identities (e.g., gender, caregiving, and race), that are stigmatized, and link to having less access to and/or benefits from societal resources to support managing the work-life interface in a social context. Such an intersectional focus would address the important need to shift work-life and flexibility research from variable to person-centered approaches. The authors identify six research considerations on work-life intersectionality in order to illuminate how traditionally assumed work-life relationships need to be revisited to address growing variation in: access, needs, and preferences for work-life flexibility; work and nonwork experiences; and benefits from using flexibility policies. The authors hope that this chapter will spur a conversation on how the work-life interface and flexibility policy processes and outcomes may increasingly differ for individuals with higher work-life intersectionality compared to those with lower work-life intersectionality in the context of organizational and social systems that may perpetuate growing work-life and job inequality.

This study aims to understand independence in internal auditing by investigating how internal auditor independence is constructed when analysed in its corporate governance context.

A critical discourse analysis (CDA) of the corporate governance reports of Swedish large stock market listed non-financial companies, for three consecutive years, is undertaken, using a theoretical lens of organisational embeddedness and operational coupling to understand independence as a situated practice.

The study develops four archetypes of internal auditor independence – autarchic, instrumental, symbiotic and subservient – and discusses each archetype's implications for independence, related to tripartite relations with management and the audit committee, regarding who has the mandate to direct work and how the work is done. It finds that internal auditors always have a capacity to be independent. Although they are not independent in relation to agents in the subservient archetype, they are independent of those down the organisational chain of command, suggesting independence is both situational and relational.

The analysis contributes a novel approach to the literature and develops a conception of independence using the dimensions of embeddedness and coupling. The archetypes offer an analytical framework for future studies on independence.

Internal auditors may understand their practice differently through the archetypes that result from this study.

Internal auditors' power relations within corporate governance further an understanding of the pressures on internal auditors and their role.

This study contributes new knowledge on the situatedness of independence by showing how internal auditors are embedded and coupled helps build their independence.

This paper aims to examine the USA's policy toward Myanmar or Burma, analyzing the bilateral relationship between the two countries from the pre-colonial period to the present day. It highlights the implications of political shifts in the system of government in Myanmar for the bilateral relationship with the USA, particularly after the 1962 coup and the emergence of rising regional powers like China. The paper also shows how the economic role of Myanmar has increased, leading to more equal relations with the United States of America.

This paper employs a qualitative research design, analyzing data from both primary and secondary sources. The methodology includes a literature review and document analysis to gain a comprehensive understanding of the USA–Myanmar bilateral relationship. The research also analyzes political and economic developments in Myanmar and the region, placing the USA policy in a broader context.

The research finds that the USA has been an influential player in Myanmar's politics and economy. However, with Myanmar's integration into the Chinese Belt and Road Initiative and its accession to Association of Southeast Asian Nations (ASEAN), its economic role has become more significant, leading to a more balanced relationship with the USA. The paper also shows that the USA's policy toward Myanmar has undergone significant changes, particularly after the 2011 easing of sanctions by former President Barack Obama.

Although many works have traced the history of USA–Myanmar ties, this study provides a fresh perspective by setting past events against the backdrop of contemporary geopolitical upheavals, therefore highlighting the complex evolution of their bilateral dynamics. The paper contributes to the literature on the USA–Myanmar bilateral relationship by providing a comprehensive analysis of the relationship from a historical perspective. The research also adds to the discussion of the implications of political and economic developments in Myanmar for the bilateral relationship with the USA. The findings of this study have important implications for policymakers and scholars interested in the USA's role in Southeast Asia and its relations with rising regional powers like China.

The purpose of this paper is to present a framework of ideation pathways that organically extend the current stock of knowledge to generate new and useful knowledge. Although detailed, granular guidance is available in the strategy literature on all aspects of empirically testing theory, the other key aspect of theory development – theory generation – remains relatively neglected. The framework developed in this paper addresses this gap by proposing pathways for how new theory can be generated.

Grounded in two foundational principles in epistemology, the Genetic Argument and the open-endedness of knowledge, I offer a framework of distinct pathways that systematically lead to the creation of new knowledge.

Existing knowledge can be deepened (through introspection), broadened (through leverage) and rejuvenated (through innovation). These ideation pathways can unlock the vast, hidden potential of current knowledge in strategy.

The novelty and doability of the framework can potentially inspire research on a broad, community-wide basis, engaging PhD students and management faculty, improving knowledge, democratizing scholarship and deepening the societal footprint of strategy research.

Knowledge is open-ended. The more we know, the more we appreciate how much we don’t know. But the lack of clear guidance on rigorous pathways along which new knowledge that advances both theory and practice can be created from prior knowledge has stymied strategy research. The paper’s framework systematically pulls together for the first time the disparate elements of transforming past learning into new knowledge in a coherent epistemological whole.

Culture significantly influences individuals' lives and shapes their behaviour in an ecological framework. In this chapter, we examine the issue of children's participation in research about child abuse and neglect (CAN) in the context of Turkey – a country that bridges the Asian and European continents. This study was based on a review examining studies on CAN in Turkey. Thus, the main goal was to find cultural explanations for the scarcity of participatory research with children in the field of child maltreatment. A review examining studies on CAN in Turkey found that no study included children victims of CAN or explored why children victims of CAN have not been participating in research. Therefore, we analysed ecological factors influencing the participation of children in CAN studies and interpreted the findings based on our observations as experts in this field. The analysis indicated that causes are not only due the characteristics of the caregivers and children but also result from the interaction of various environmental and systemic factors. Recommendations for politicians and researchers to increase children's participation in research are discussed.

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.

We studied the relationship between job engagement and systematic problem solving (SPS) among shop-floor employees and how lean production (LP) and Internet of Things (IoT) systems moderate this relationship.

We collected data from a sample of 440 shop floor workers in 101 manufacturing work units across 33 plants. Because our data is nested, we employed a series of multilevel regression models to test the hypotheses. The application of IoT systems within work units was evaluated by our research team through direct observations from on-site visits.

Our findings indicate a positive association between job engagement and SPS. Additionally, we found that the adoption of lean bundles positively moderates this relationship, while, surprisingly, the adoption of IoT systems negatively moderates this relationship. Interestingly, we found that, when the adoption of IoT systems is complemented by a lean management system, workers tend to experience a higher effect on the SPS of their engagement.

One limitation of this research is the reliance on the self-reported data collected from both workers (job engagement, SPS and control variables) and supervisors (lean bundles). Furthermore, our study was conducted in a specific country, Italy, which might have limitations on the generalizability of the results since cross-cultural differences in job engagement and SPS have been documented.

Our findings highlight that employees’ strong engagement in SPS behaviors is shaped by the managerial and technological systems implemented on the shop floor. Specifically, we point out that implementing IoT systems without the appropriate managerial practices can pose challenges to fostering employee engagement and SPS.

This paper provides new insights on how lean and new technologies contribute to the development of learning-to-learn capabilities at the individual level by empirically analyzing the moderating effects of IoT systems and LP on the relationship between job engagement and SPS.