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Given declining tuition funds and government grants, Kenyan universities need to develop strategies, including increased research grants and collaborations, to diversify their income sources. Well-managed doctoral students can boost a university’s teaching and research outputs. However, numbers of students enrolled in doctoral programmes at Kenyan universities are low, and graduation rates and time-to-graduate statistics are disturbing. Research undertaken elsewhere underline the important role played by supervisors and peers in facilitating students’ sense of belonging and their success. Therefore, this study aims to investigate the influence of supervisory and peer support on PhD students’ sense of belonging and their success at Kenyan universities.

In this cross-sectional study, data were gathered through an online questionnaire from 614 students admitted to doctoral programmes at Kenyan universities between 2010 and 2018. We used multi-item scales to collect data on PhD students’ self-efficacy, supervisory and peer support and a sense of belonging.

Structural equation modelling results revealed that PhD students’ modes of study and self-efficacy were significantly associated with the quality of supervision, peer support and a sense of belonging. However, only age, a sense of belonging and the quality of supervision were directly linked to their success.

This study contributes to the literature on doctoral-level education, responding to the need for research on the influence of relationships with supervisors and peers on PhD students’ sense of belonging and their success, especially in developing countries.

The pandemic has enhanced the global phenomenon of disinformation. This paper aims to study the false news concerning COVID-19, spread through social media in Spain, by using the LatamChequea database for a duration from 01/22/2020, when the first false information has been detected, up to 03/09/2021.

A quantitative analysis has been conducted with regard to the correlation between fake news stories and the pandemic state, the motive to share them, their dissemination in other countries and the effectiveness of fact checking. This study is complemented by a qualitative method: a focus group conducted with representatives of different groups within the society.

Fake news has been primarily disseminated through several social networks at the same time, with two peaks taking place in over a half of the said false stories. The first took place from March to April of 2020 during complete lockdown, and we were informed of prevention measures, the country’s situation and the origin of the virus, whereas the second was related to news revolving around the coming vaccines, which occurred between October and November. The audience tends to neither cross-check the information received nor report fake news to competent authorities, and fact-checking methods fail to stop their spread. Further awareness and digital literacy campaigns are thus required in addition to more involvement from governments and technological platforms.

The main limitation of the research is the fact that it was only possible to conduct a focus group of five individuals who do not belong to generation Z due to the restrictions imposed by the pandemic, although a clear contribution to the analysis of the impact of fake news on social networks during the COVID-19 pandemic in Spain can be seen from the privileged experiences in each of the fields of work that were identified. In this sense, the results of the study are not generalizable to a larger population. On the other hand, and with a view to future research, it would be advisable to carry out a more specific study of how fake news affects generation Z.

This research is original in nature, and the findings of this study are valuable for business practitioners and scholars, brand marketers, social media platform owners, opinion leaders and policymakers.

Using the theoretical framework of the substantive economy, this study aims to point out the main aspects of the substantive mode of operation that help the integration of disadvantaged people while at the same time shedding light on the barriers that hinder economically efficient functioning in a market economy.

Research focuses on Hungarian rural work integration social cooperatives, which are engaged in producing activity by the employment of disadvantaged people. In the research, mixed methods were applied: results of a questionnaire survey covering 102 cooperatives, as well as 20 semi-structured interviews and experiences from the field. A total of 17 indicators were used to explore the substantive operational features, promoting mechanisms and problems in the following areas: organisational goals and outcomes; integrating roles and functions; productive functions; and the embeddedness of cooperatives.

As for results, substantive operational mechanisms and tools that support the integration of disadvantaged people have been identified such as mentoring, social incentives, the ability to create local value or the expansion of local community services. At the same time, several barriers have been detected that make it difficult to operate economically, such as cooperatives being a stepping stone for workers, excessive product heterogeneity or the lack of vertically structured bridging relationships.

The value of the study is to counterpoint the mechanisms promoting social purposes of work-integration social cooperatives and the obstacles to their long-term sustainability within the framework of the substantive economy, to better understand their functioning and the less quantifiable factors of their performance.

This study aims to explore how the absorptive capacity of emerging market multinationals (EMNEs) facilitates increased acquirer performance in industry exploration and technology exploration cross-border acquisitions (CBAs).

The research context for this study is Brazilian EMNEs and their CBAs. The final database contains 101 CBAs.

The authors find that industry exploration strategies negatively affect financial performance, but technology exploration strategies have a positive effect. The acquirer’s absorptive capacity can exacerbate the negative effects, except in instances of technology exploration strategies, where there is a demonstrable benefit from the acquirer’s absorptive capacity.

The study contributes first by providing a more nuanced understanding of the effects of absorptive capacity on postacquisition performance, depending on the type of knowledge explored. Second, by drawing on EMNE learning perspectives, the authors demonstrate the versatility of absorptive capacity in emerging markets.

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.

The authors aim to develop a conceptual framework for longitudinal estimation of stress-related states in the wild (IW), based on the machine learning (ML) algorithms that use physiological and non-physiological bio-sensor data.

The authors propose a conceptual framework for longitudinal estimation of stress-related states consisting of four blocks: (1) identification; (2) validation; (3) measurement and (4) visualization. The authors implement each step of the proposed conceptual framework, using the example of Gaussian mixture model (GMM) and K-means algorithm. These ML algorithms are trained on the data of 18 workers from the public administration sector who wore biometric devices for about two months.

The authors confirm the convergent validity of a proposed conceptual framework IW. Empirical data analysis suggests that two-cluster models achieve five-fold cross-validation accuracy exceeding 70% in identifying stress. Coefficient of accuracy decreases for three-cluster models achieving around 45%. The authors conclude that identification models may serve to derive longitudinal stress-related measures.

Proposed conceptual framework may guide researchers in creating validated stress-related indicators. At the same time, physiological sensing of stress through identification models is limited because of subject-specific reactions to stressors.

Longitudinal indicators on stress allow estimation of long-term impact coming from external environment on stress-related states. Such stress-related indicators can become an integral part of mobile/web/computer applications supporting stress management programs.

Timely identification of excessive stress may improve individual well-being and prevent development stress-related diseases.

The study develops a novel conceptual framework for longitudinal estimation of stress-related states using physiological and non-physiological bio-sensor data, given that scientific knowledge on validated longitudinal indicators of stress is in emergent state.

This study aims to explore aesthetic atmospheres and their affordances in urban squares to advance knowledge on the research and design of attractive living environments.

Descriptions of pleasant and unpleasant experiences of urban squares were collected using qualitative questionnaires with open-ended questions. The theoretical framework and the lens of aesthetic affordances were applied to pinpoint and understand the connections between the place attributes and experiences.

This study found four distinct aesthetic atmospheres formed by perceived synergies of both the material and immaterial aspects of the environment. It was also found that the atmospheres may shift. A model that shows the aesthetic atmospheres and their potential affordances as layered and emerging is presented.

Everyday aesthetics considered as affordances open new research perspectives for the understanding of what generates attractive living environments – or not.

Aesthetics affordances may provide the design professionals and alike means on how to design places that engender specific aesthetic atmosphere.

Gathering and discussing commonplace aesthetic experiences in everyday life may enhance democratic participation in place development among people with different levels of design expertise.

This study combines theories of place with a novel concept of aesthetic affordances to identify distinct aesthetic atmospheres. A holistic overview structure of how the various constituents of aesthetic atmospheres relate to each other provides new ways of studying and understanding urban aesthetic atmospheres.

The aim of this article is to highlight the major part played by executives in the escalation of corporate social irresponsibility (CSI). Based on the upper echelons theory, the authors developed a model which shows the essential role of CEOs in explaining CSI. The authors proposed that the key personality traits of CEOs—narcissism—, as well as their power, could explain the degree of CSI.

Due to the significant methodological challenges when investigating CSI, the authors explored a novel method for measuring CSI in order to assess the degree of irresponsible behaviors. The authors build a CSI scale based on the perceptions of key informants, i.e. experts with diverse professional backgrounds. The authors apply CSI scale in a sample of 84 Spanish companies that were involved in CSI.

The results of the authors’ empirical study show the positive and significant influence of CEO narcissism and CEO power on the degree of CSI.

On the one hand, corporate irresponsibility scandals have relevant social consequences and practical implications. On the other hand, narcissism is a natural feature of managers in top positions that is increasing in societies.

The authors’ findings may help CEOs, TMTs and corporate boards to acknowledge potential sources of CSI decreasing its likelihood through counterbalancing CEO's power and considering the dark side of narcissism.

On the one hand, corporate scandals have relevant social and practical implications. On the other hand, narcissism is a natural feature of managers in top positions that is increasing in societies.

In this paper, the authors highlight the role of CEOs characteristics and their firms as the key actors for explaining and understanding the degree of CSI.

本研究擬強調行政主管在加劇企業無社會責任感上所扮演的主要角色。我們根據高層梯隊理論, 開發了一個模型, 來顯示執行長如何在解釋企業無社會責任感上所起的關鍵作用。我們提出一個見解, 就是: 執行長的主要人格特質-自戀和其權力-或許能解釋企業無社會責任感的程度。

由於面對在關於企業無社會責任感的研究法上的重大挑戰, 我們探索了一個估量企業無社會責任感的新穎方法, 以能對企業無社會責任感行為的程度進行評估。我們根據主要的資訊提供者的觀念和看法, 制定了一個企業無社會責任感的等級 (這些主要的資訊提供者均為具有不同專業背景的專家), 我們在84間西班牙公司的樣本裡, 使用了這企業無社會責任感等級, 而這些公司均涉及企業無社會責任感的。

我們這實證研究的結果顯示, 執行長的自戀和其權力, 對企業無社會責任感的程度, 起著正面和顯著的影響。

在本學術論文裡, 我們強調了執行長的特徵和他們的公司, 是瞭解和解釋企業無社會責任感的程度的關鍵行為者。

一方面, 企業無社會責任感的醜聞會帶來相關的社會後果和實務方面的影響;而另一方面, 自戀是高層主管的自然特徵, 而這些特徵, 在我們的社會裡不斷增強。本研究的結果, 或許可幫助執行長、高層管理團隊和公司董事會去承認企業無社會責任感的潛在原因, 並以透過平衡執行長的權力和考慮自戀的陰暗面而減低企業無社會責任感產生的可能。

While extant research has increasingly examined minority entrepreneurs, less attention has been paid to Eastern European immigrant entrepreneurs and the role that marketing agility and risk propensity play in their resilience and survival in Nordic countries. This paper aims to highlight the importance of these factors for Eastern European immigrant entrepreneurs in the developed Nordic economy of Denmark.

This paper adopts the dynamic capabilities view as a theoretical framework and uses a qualitative research approach with interviews as the main data collection method. The empirical sample comprises 12 entrepreneurs originating from Hungary, Slovakia, Latvia, Lithuania and Romania, who operate in Denmark.

The findings show that contrary to prior studies that have highlighted a reliance among the migrant entrepreneurial community on ethnic networks as their dominant target market, Eastern European immigrant entrepreneurs located in Denmark, in contrast, focused on attracting Danish consumers as their target market audience. Leveraging multiple networks was therefore found to be critical to the survival of these immigrant ventures. Additionally, the entrepreneurs' marketing agility, underpinned by their optimistic approach, growth ambitions and passion for entrepreneurship, was found to play a pivotal role in their survival. Finally, despite the stable institutional environment in Denmark and the ease of doing business (both of which are influential factors in shaping the risk propensity and risk perception of entrepreneurs), the authors found immigrant entrepreneurs' risk propensity to be rather low, which was contrary to the expectations.

The current paper is one of the first studies that explicitly analyzes the roles of marketing agility and risk propensity in the resilience and survival of the ventures of relatively skilled immigrant entrepreneurs from Eastern Europe in a developed Nordic economy (Denmark). The paper's findings also challenge the notion associated with immigrant entrepreneurial ventures being primarily focused on ethnic customers or enclaves. The paper also specifies the peculiarities of marketing agility in immigrant entrepreneurial contexts and solidifies the importance of diverse networks in immigrant business survival and development.

This conceptual, multi-voiced paper aims to collectively explore and theorize family entrepreneuring, which is a research stream dedicated to investigating the emergence and becoming of entrepreneurial phenomena in business families and family firms.

Because of the novelty of this research stream, the authors asked 20 scholars in entrepreneurship and family business to reflect on topics, methods and issues that should be addressed to move this field forward.

Authors highlight key challenges and point to new research directions for understanding family entrepreneuring in relation to issues such as agency, processualism and context.

This study offers a compilation of multiple perspectives and leverage recent developments in the fields of entrepreneurship and family business to advance research on family entrepreneuring.