Search results

This paper investigates the focal role of close co-worker friendship in reducing incivility. Furthermore, this paper examines negative workplace gossip as a mediator and gender and promotion focus as moderators.

Using a time-lagged approach, 553 full-service restaurant front-line co-workers in Greater Cairo responded. Further, the data were analyzed using SmartPLS v.4.

Promotion focus weakened close co-workers’ friendships, causing them to speak negatively about each other with other co-workers. Multi-group analysis showed that males were more likely to spread negative gossip about their close co-workers and thus were subjected to incivility-related behaviors by their co-workers.

This paper is an early attempt to explore the focal role of promotion focus in the full-service restaurant context. This paper adds to affective events theory (AET) with a limited understanding of explaining and predicting co-worker incivility.

本文擬探討同僚間緊密的友好關係在減少不文明行為方面所扮演的重要角色。此外、本文擬把職場的流言蜚語看作是調解員而對其加以探索; 本文亦把性別和對晉升的關注看作是仲裁人而進行探究。

研究人員使用時間差距法進行研究和探討。數據來自553名於大開羅提供整套服務的餐館內工作的一線員工所給予的回應。研究人員以SmartPLS 結構方程建模軟體第四版 (SmartPLS v.4) 對數據進行分析。

研究人員發現，僱員對晉升的關注削弱了同僚間緊密的友好關係，並驅使他們在其他同事中對同僚作負面的評價。另外，多組分析顯示了男性員工更有可能散播關於其要好同僚的閒言閒語，因此，他們會遭受同僚不文明的待遇。

本研究是早期的嘗試，去探索在提供整套服務餐館的背景下，僱員對晉升的關注所扮演的重要角色。另外，本研究的結果將會添加至情感事件理論 - 該理論就解釋和預測同僚不文明行為所提供的闡釋似有點不足。

The current study evaluated (1) characteristics of the community leadership development program associated with successful participant recruitment, (2) active ingredients that promoted fellow engagement and program completion and (3) how the program addressed blackness and racism.

Individual interviews were conducted with a representative subset of former program fellows.

Results indicated that offering training in small cohorts and matching fellows with individual mentors promoted program interest. Program strengths and unique ingredients included that the program was primarily led by people from the Black community, program malleability, and that the program was a partnership between fellows and leadership. Additionally, the program was responsive to fellows’ needs such as by adding a self-care component. Fellows also noted dedicated space and time to discuss race and racism. Results offer a unique theoretical perspective to guide leadership development away from the uniform or standardized approach and toward one that fosters diversity and equity in leadership.

Altogether, this work demonstrates how leadership development programs can be participant-informed and adapted to participants’ social and cultural needs.

This paper aims to explore how leaders can develop value-based leadership for sustainable quality development in Lean manufacturing.

A qualitative meta-analysis was conducted using data from a three-year study of Lean manufacturing in Sweden using the Shingo business excellence model as an analytical framework.

This study demonstrates that leaders can develop value-based leadership to support Lean manufacturing by defining and articulating the organization’s values and accompanying behaviors that are needed to support the strategic direction; creating forums and time for leaders to identify the why behind decisions and reflect on their experiences to be able to lead a transformative process; and using storytelling to create a coaching culture to connect values and behaviors, to the processes and systems of work.

This paper contributes insights for developing value-based leadership to support a systemic approach to sustainable quality development in lean manufacturing. Findings are based on a limited case sample size of three manufacturing companies in Sweden.

The findings were derived using a unique methodological approach combining storytelling, appreciative inquiry and coaching with traditional data collection methods including surveys and interviews to identify, define and shape value-based leadership in Lean manufacturing.

The purpose of this paper is to fill a gap in the literature regarding the open innovation management approaches that small and medium-sized enterprises (SMEs) can use to access digital technologies and incorporate them into their organizational processes. The research question is: What organizational and process-level managerial actions do SMEs take to successfully access and implement digital technologies within their organizational processes?

Using Guertler et al.'s (2020) Action Innovation Management Research (AIM-R) framework, this study examines the digital transformation experiences of 10 European SMEs to gain insights into the managerial actions that foster successful digital transformation.

The findings of the paper reveal two major contributions. First, a digital transformation roadmap for SMEs is proposed, with a focus on accessing external resources and reconfiguring internal ones to ease their digital transformation journey. Second, three distinct paradoxes that influence the digital transformation process in SMEs are highlighted, providing useful insights into the challenges and tensions SMEs face during this journey.

This paper provides a unique perspective on the digital transformation of SMEs by examining the managerial actions required for successful technology adoption and revealing the paradoxes that may emerge during this transformative process. The findings form the basis for future research, deepening our understanding of digital transformation in SMEs and providing actionable advice to managers and practitioners navigating this journey.

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 study aims to explore the influence of conscious business practices (CBPs) on the reputational capital (RC) of SMEs with the generation of the manager (X/Y) as a moderator of such relationships.

The authors conducted empirical research based on a cross-sectional survey on 115 Western Mexican SME managers using PLS structural equation modeling to test the proposed hypotheses.

SMEs RC is nurtured by adopting two CBPs. The effect on RC may differ according to managers’ generation. In Western Mexican SMEs, Gen X managers perceive that a higher purpose is more important for building RC, while conscious culture comes first for millennials.

The generalisability of the findings is decreased, given that the study relied on convenience and non-probabilistic sampling in one economy. The lack of previous studies on SMEs, and the difficulty in conducting research in an emerging economy, gives the findings an importance in furthering research.

It contributes to strengthen SMEs’ RC through CBPs.

It broadens the perspective of SMEs in emerging economies to adopt CBPs for increasing their RC. This relationship varies depending on the managers’ generation.

The study used the quantitative approach to explore the perception of Mexican Gen X managers and millennials on the relationship between CBPs and their effects on RC.

The current paper is part of a larger scoping review project investigating the intersection of leader(ship) identity development and meaning-making. In this review, we analyzed 100 articles to determine the current extent of literature that covers the intersection of leader(ship) identity development, meaning-making and marginalized social identities.

A review of the extant literature is included, and a conceptual model is suggested for further exploration into this critical and under-researched domain.

More research is needed at the intersection of leadership identity development, meaning-making and marginalized social identities.

As this area of study has expanded, scholars have noted an absence of research on the effect of multiple social identities, especially marginalized identities, on meaning-making and leadership identity construction.

The ranking of universities and other research-intensive institutions in global ranking systems is based on numerous indicators, including number of articles with external collaboration, number of articles with international collaboration, number of articles with industry collaboration as well as co-patents with industry. The purpose of this paper is to examine university–industry research collaboration in Ghana, with the aim of exploring the relationship between the research output collaborations in the top four universities in Ghana and industry across different geographical scales.

This study’s data was obtained from the SciVal database, which drawn its data from the Scopus bibliographic and citation database. The bibliographic and citation data were extracted using a search of the publications affiliated to the University of Ghana, for the period 2011–2020.

Key findings demonstrate a constant rise in the number of research publications by the selected universities over time. Research collaboration intensity in the selected universities in terms of co-authored publications was higher as compared to single-authored publications. University–industry research co-authorships were, however, lower when compared to university–university research co-authorships. The university–industry research co-authorships occurred mostly with Europe, Asia-Pacific and North American-based institutions as opposed to African-based institutions. In Ghana, four industry-based institutions were engaged in intensive research with the selected universities.

This study demonstrates that, for each selected university, it is possible to measure the performance of individual universities in both intra-regional and international collaboration. Such results may be useful in informing policy as well as merit-based public funding of universities in Ghana.