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The study directs attention to the psychological conditions experienced and knowledge management practices leveraged by faculty in higher education institutes (HEIs) to cope with the shift to emergency remote teaching caused by the COVID-19 pandemic. By focusing attention on faculty experiences during this transition, this study aims to examine an under-investigated effect of the pandemic in the Indian context.

Interpretative phenomenological analysis is used to analyze the data gathered in two waves through 40 in-depth interviews with 20 faculty members based in India over a year. The data were analyzed deductively using Kahn’s framework of engagement and robust coding protocols.

Eight subthemes across three psychological conditions (meaningfulness, availability and safety) were developed to discourse faculty experiences and challenges with emergency remote teaching related to their learning, identity, leveraged resources and support received from their employing educational institutes. The findings also present the coping strategies and knowledge management-related practices that the faculty used to adjust to each discussed challenge.

The study uses a longitudinal design and phenomenology as the analytical method, which offers a significant methodological contribution to the extant literature. Further, the study’s use of Kahn’s model to examine the faculty members’ transitions to emergency remote teaching in India offers novel insights into the COVID-19 pandemic’s effect on educational institutes in an under-investigated context.

The purpose of this study is to describe latent classes explaining the innovation logic in the Finnish construction companies. Innovativeness is a driver of competitive performance and vital to the long-term success of any organisation and company.

Using finite mixture structural equation modelling (FMSEM), the authors have classified innovation logic into latent classes. The method analyses and recognises classes for companies that have similar logic in innovation activities based on the collected data.

Through FMSEM analysis, the authors have identified three latent classes that explain the innovation logic in the Finnish construction companies – LC1: the internal innovators; LC2: the non-innovation-oriented introverts; and LC3: the innovation-oriented extroverts. These three latent classes clearly capture the perceptions within the industry as well as the different characteristics and variables.

The presented latent classes explain innovation logic but is limited to analysing Finnish companies. Also, the research is quantitative by nature and does not increase the understanding in the same manner as qualitative research might capture on more specific aspects.

This paper presents starting points for construction industry companies to intensify innovation activities. It may also indicate more fundamental changes for the structure of construction industry organisations, especially by enabling innovation friendly culture.

This study describes innovation logic in Finnish construction companies through three models (LC1–LC3) by using quantitative data analysed with the FMSEM method. The fundamental innovation challenges in the Finnish construction companies are clarified via the identified latent classes.

Effective customer journey design (ECJD) is considered a key variable in customer experience management and an essential source of brand meaning and pro-brand behavior. Although previous research has confirmed its importance for driving brand attitudes and loyalty, the role of consumer-brand identification as a social identity-based influence in this relationship has not yet been discussed. Drawing on construal level and social identity theories, this paper aims to investigate whether effective journeys and the resulting overall journey experience are equally powerful in driving brand loyalty among customers with different levels of consumer-brand identification.

The present article develops and tests a research model using data from the European and US service sectors (N = 1,454) to investigate how and when ECJD affects service brand loyalty.

Across two cultural contexts, four service industries and 33 service brands, the results reveal that ECJD is a crucial driver of service brand loyalty for customers with low consumer-brand identification. Moreover, the findings show that different aspects of journey effectiveness positively impact the valence of customers’ experience related to those journeys – a process that is ultimately decisive for their brand loyalty.

This study is unique because it generates theoretical and practical knowledge by combining the literature streams of customer journey design, customer experience and branding. Furthermore, this work demonstrates that consumer-brand identification is a critical boundary condition to be considered in the relationship between ECJD and brand loyalty in services.

The first-aid courses organized by the Youth Red Cross Carinthia (Austria) had a quality problem, necessitating a professionalization in teaching and time structure. This research aimed to enhance the quality and effectiveness of these courses by implementing modified lesson studies with non-professional trainers. The paper presents the realization process, empirical research and results obtained by applying the first-aid curriculum.

Around 22 lesson study first-aid courses (14 classes with 2 cycles, 8 with 3 cycles) were conducted and evaluated in different Austrian school types. An observation sheet was created to evaluate attention and competencies. Interviews were conducted with both teachers and students to validate the results.

The research findings demonstrate that lesson studies can significantly enhance the quality and effectiveness of first-aid courses. Inexperienced and experienced first-aid teachers significantly improved their teaching skills. Newly educated first-aid teachers showed substantial improvement, leading to the introduction of an induction period and coaching opportunity within the Youth Red Cross Carinthia.

This is the first lesson study conducted in a non-academic context. It highlights the adaptation process of Carinthian first-aid courses. It illustrates how lesson studies impact lesson clarity, instructional variety, student engagement in the learning process, student outcome, student feedback and teaching effectiveness in a non-academic context. It contributes to the literature on the application of lesson study in first-aid education and provides insight into the benefits of this approach in enhancing the quality of first-aid training.

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 purpose of the current study is to assess Omani teachers’ performance on tasks related to the stages of engineering design. To achieve this, data from an engineering design test was used, and demographic variables that are correlated with this performance were identified.

This descriptive study employed a cross-sectional design and the collection of quantitative data. A sample of preservice science teachers from Sultan Qaboos University (SQU) (n = 70) participated in this study.

Findings showed low and moderate levels of proficiency related to the stages of engineering design. Differences between males and females in terms of performance on engineering design tasks were found, with females scoring higher overall on the assessment. Biology preservice teachers scored higher than teachers from the other two majors (physics and chemistry) in two subscales. There were also differences between teachers studying in the Bachelor of Science (BSc) program and the teacher qualification diploma (TQD) program.

This study provides an overview, in an Arab setting, of preservice science teachers’ proficiency with engineering design process (EDP) tasks. It is hoped that the results may lead to improved instruction in science teacher training programs in similar contexts. Additionally, this research demonstrates how EDP competency relates to preservice teacher gender, major and preparation program. Findings from this study will contribute to the growing body of research investigating the strengths and shortcomings of teacher education programs in relation to science, technology, engineering and mathematics (STEM) education.

Scholars and practitioners alike wish to understand what makes workplace conflict beneficial or injurious to, for example, performance and satisfaction. The authors focus on parties’ personal experience of the conflict, which is complementary to studying conflict issues (i.e. task- or relationship-related conflict). Although many authors discuss the personal experience of conflict, which the authors will refer to as conflict personalization, different definitions are used, leading to conceptual vagueness. Therefore, the purpose of this paper is to develop an integrative definition of the concept of conflict personalization.

The authors conducted a systematic literature review to collect definitions and conceptualizations from 41 publications. The subsequent thematic analysis revealed four building blocks that were used to develop an integrative definition of conflict personalization.

The authors developed the following definition: Conflict personalization is the negative affective as well as cognitive reaction to the self being threatened and/or in danger as a result of a social interaction about perceived incompatibilities.

The integrative definition of this study enables the development of a measurement instrument to assess personalization during workplace conflict, paving the way for developing effective research-based interventions.

Conceptual vagueness hampers theoretical development, empirical research and the development of effective interventions. Although the importance of conflict personalization is mentioned within the field of workplace conflict, it has not been empirically studied yet. This paper can serve as the basis for future research in which conflict issue and personal experience are separated.

Although there is a growing body of work on immigrants' information behavior, little is known about the pre-arrival information experiences of immigrants who consult formal information sources such as immigration agents. Drawn from a larger study on the information behavior of immigrants, this paper mainly reports the semi-structured interview findings on the pre-arrival information experiences of Bangladeshi immigrants who used formal information sources with discussion on how that affected their post-arrival settlement into Canada.

The study used a mixed method approach with semi-structured interviews (n = 60) and surveys (n = 205) with participants who arrived in Canada between the years of 1971 and 2017. Data were collected from May 2017 to February 2018.

Although the overall scope of the original study is much larger, this paper features findings on the pre-arrival information experiences derived mainly from an analysis of interview data. This study provides insights into the pre-arrival information experiences of Bangladeshi immigrants consulting formal information sources such as immigration firms, individual immigration consultants and more formal government agencies. The author introduces a new concept of “information crafting” by exploring the negative consequences of selective information sharing by immigration consultants/agents in newcomers' settlements in Canada, primarily positive information about life in Canada, sometimes with exaggeration and falsification. The interview participants shared story after the story of the settlement challenges they faced after arriving in Canada and how the expectations they built through the information received from immigration consultants and government agencies did not match after arrival. This study emphasizes the importance of providing comprehensive information about life in Canada to potential newcomers so that they can make informed decisions even before they apply.

The findings of this study have theoretical and practical implications for policy and research. This study provides insights into the complicated culturally situated pre-arrival information experiences of Bangladeshi immigrants. Moreover, the study findings encourage researchers in various disciplines, including psychology, migration studies and geography, to delve more deeply into newcomers' information experiences using an informational lens to examine the information newcomers receive from diverse sources and their effects on their post-arrival settlement in a new country. The study challenges the general assumptions that formal information sources are always reputable, useful, and comprehensive, and it provides some future directions for research that seeks to understand the culturally situated information behavior of diverse immigrant groups.

We aimed to identify factors that influence student engagement in distance learning.

The research involved a group of 671 students from economic and technical higher education institutions in Poland. We collected the data with the CAWI technique and an original survey. Next, we processed the data using principal component analysis and then used the extracted components as predictors in the induced smoothing LASSO regression model.

The components of the students’ attitude toward remote classes learning conditions are: satisfaction with teachers’ approach, attitude to distance learning, the system of students’ values and motivation, IT infrastructure of the university, building a network of contacts and communication skills. The final model consisted of seven statistically significant variables, encompassing the student’s sex, level of studies and the first five extracted PCs. Student’s system of values and motivation as well as attitude toward distance learning, were those variables that had the biggest influence on student engagement.

The research result suggests that in addition to students’ system of values and motivation and their attitude toward distance learning, the satisfaction level of teachers’ attitude is one of the three most important factors that influence student engagement during the distance learning process.

The main value of this article is the statistical model of student engagement during distance learning. The article fills the research gap in identifying and evaluating the impact of various factors determining student engagement in the distance learning process.

This paper investigates the constraints an organisation faces when using recruitment agencies and having to trade-off between the speed of hiring a candidate, the cost of a candidate and the match of the candidate against the job requirements across different job seniorities. We analyse how technology can shift the cost and hiring speed in spite of these constraints.

The research design is exploratory, quantitative and cross-sectional. The study employed a two-factor, unbalanced class Analysis of Variance (ANOVA) including interaction effects to test the difference between the means of the class of interest and a control class.

Our empirical findings confirm that (1) the technological innovation of a recruitment agency marketplace can liberate organisations from their time, cost and quality hiring constraints, accelerating the time to hire by four times and reducing costs by over 12%, and (2) these results hold across varying role seniority levels.

This study contributes to the existing literature in three ways: (1) it introduces the recruitment triangle from project management into the recruitment literature; (2) it demonstrates how technological innovations such as recruitment agency marketplaces are able to provide a shift in the constraints posed by the recruitment triangle.