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The paper comprises an extended discussion of the possibilities that Web 2.0 applications offer to doctoral researchers, and where such applications fit in the early twenty‐first century in the research environment. It explores the main issues associated with their use by doctoral researchers, and how these factors have influenced the design of a series of four information and communication technologies (ICT) development courses.

Over 29 Web 2.0 applications were reviewed and grouped into 13 subject categories in terms of how they might support the activities of doctoral researchers. The resulting categories were then themed into four different groups to facilitate the delivery in order to address where and how Web 2.0 applications can enhance doctoral researchers' activities.

Four groups of applications emerged: social networking, online project collaboration, online virtual desks and reusable multi‐media. The four groups were developed into four courses that together comprise a new ICT skills development module intended for doctoral researchers.

In terms of portability, the 13 categories of web‐based applications identified could, when taken together, comprise the infrastructure for a complete research environment that can be accessed anywhere in the world on an internet‐connected PC or laptop. The aim of the module is to enhance the research experience of doctoral researchers by raising awareness of the potential and possibilities associated with using Web 2.0 applications in the research environment.

The paper offers both ICT developers and doctoral researchers insights into the possibilities and problems of using Web 2.0 applications in the process of academic research.

Grounded in the job demands–resources (JD-R) theory, this study investigates how the difficulty in social distancing at work, resulting from the COVID-19 crisis, may lead to intention to quit and career regret and how and when these effects may be attenuated.

Three-wave survey data were collected from 223 frontline service workers in a large restaurant company during the COVID-19 crisis.

The results show that difficulty in social distancing reduced employees' work engagement, and consequently, increased their turnover intention and career regret. These relationships were moderated by external employability, such that the influence of difficulty in social distancing weakened as external employability increased.

Social distancing measures have been applied across the globe to minimize transmission of COVID-19. However, such measures create a new job demand for service workers who find it difficult to practice social distancing due to the high contact intensity of service delivery. This study identified personal resources that help service workers cope with the demand triggered by COVID-19.

This macroeconomic analysis chronicles the risk behavior of market-based financial intermediaries and traditional depository institutions from 1980 to 2010 and assesses the role that competition, financial innovation and regulation played in their evolving risk behaviors. The paper aims to discuss these issues.

Using a two-part CAPM framework in line with Campbell et al. (2001), risk measures are constructed through the decomposition of industry-level risk and firm-level idiosyncratic risk. These constructed measures are used in a VAR model with a historical decomposition approach to assess the impact of the three factors on the relative risk behavior of these firms.

The results indicate that the market-based and traditional intermediaries exhibited a period of diverging relative average firm-level risk behavior followed by a period of converging risk behavior. Using the derived firm-level risk measures, the impact of competition, financial innovation and regulatory changes on explaining these changing risk behaviors is explored. The results suggest that regulatory changes (i.e. deregulation) can best explain the relative risk behavior over the divergence period through late 1999 relative to the other two variables. The period from November 1999 through the financial crisis marks the converging risk behaviors across these intermediaries. Over this period, the changing nature of competition played the most important role in driving these behaviors.

The key contribution of this analysis highlights the evolutionary changes in the risk behaviors of market-based and traditional financial intermediaries and the factors driving both their diverging and converging nature over time.

Using a sample of some 300 small independent businesses, drawn from Central London, the paper examines how entrepreneurial behaviour affects business performance. It is argued that small businesses motivated by a desire to grow in terms of sales and/or employees and to survive in a dynamic and competitive environment need to be innovative. However, to what extent they will innovate successfully depends on their capacity to plan ahead, their capacity to innovate and their willingness to take risk. It is shown that entrepreneurial businesses are characterised by these competencies that allow them to innovate and thus develop and grow successfully. Not surprisingly, not all small businesses are equipped with these three competencies owing to their diverse array of strengths and weaknesses arising from the diversity in the managerial motives and aspirations of entrepreneurship. These results highlight the importance of the capacity to innovate and the capacity to plan ahead as strong predictors of small businesses’ performance.

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.

Scholars identify the value proposition as representing the essence of strategy and the firm's single most important organizing principle. However, research suggests less than 10 per cent of companies formally develop value propositions. The purpose of this paper is to undertake case study research investigating the process by which leading companies develop their value propositions.

The research identifies that the financial services and telecommunications vertical markets are viewed as the highly sophisticated industry sectors in terms of customer management. These industry sectors are selected for investigation. The paper develops case studies of two companies’ approaches to developing value propositions in the business-to-business (B2B) and business-to-consumer (B2C) sectors within these vertical markets.

This paper contributes to the marketing literature by examining how two large and complex service enterprises have adopted structured processes for developing innovative value proposition within the B2B and B2C sectors. The authors argue that innovation in value proposition development represents a substantive opportunity for marketing to reassert its influence in the boardroom.

This case study research provides guidelines of the processes by which enterprises can successfully develop innovative value propositions.

This research is considered to be the first case-based academic exploration of the formal processes by which large organizations develop value propositions.

The purpose of this paper is to set out an argument for a way to design, implement and manage IS with an emphasis on first, the learning that can be created through undertaking the approach, and second, the learning that may be created through using the IS that was implemented. The paper proposes joining two areas of research namely, technology management with soft systems methodology (SSM). The framework was developed through undertaking a customer concern management project within a manufacturing organisation.

Reviewing the literature on information systems management, the learning organisation, and systems theory a proposed synergy is found. The outcome of this synergy allows a number of methodologies to be identified that are argued as suitable for IS design. From these information system development (ISD) methodologies, SSM is expanded to incorporate the principles of the learning organisation and systems theory. The expanded SSM framework is applied in practice through a process of participatory action research.

The outcome of the practical work argues for a complete framework that joins the areas of research (SSM and technology management) and emphasises other thinking from the areas of systems theory and the “learning organisation”.

The paper concludes with a discussion on the advantages of joining soft systems with technology management but also the limitations created. Such limitations have been identified as moving from the soft, tacit issues of the design phases to the harder more structured aspects of technology implementation and management. A change in philosophy may restrict other issues from being explored. This issue needs to be focussed on in future research.

A framework has been developed that draws on the work of soft systems methodology (SSM) and a technology management process framework (TMPF) used in the area of technology management. By expanding the SSM model and joining it with the TMPF an attempt to give individuals and teams a practical tool to help design, implement, and manage IS with an emphasis on learning the framework promotes.

The framework provides advantages for academics, consultants and other practitioners and gives a central focus on what issues need to be accomplished more explicitly in order to undertake an ISD project.

This paper seeks to report the initial phase of a cross‐institutional screencasting project designed to provide digital, multimedia support for referencing skills. Use of screencasting software, with strong educational design, has the potential to reach all learners asynchronously, regardless of mode of study, and this paper focuses on the transferability of the principles and skills in this project to other contexts and institutions.

The referencing tutorial makes use of dual coding theory to provide an aligned visual and auditory learning experience and is discussed in reference to the current literature. The foundation of the project was collaboration between the academic libraries and lecturers in the field of academic learning skills. The pedagogical and technical design and challenges are discussed, with a view to incorporating feedback into further iterative development stages.

This paper finds that screencasting has been used to effectively support the development of referencing skills across a diverse student cohort, but recognises that further, in‐depth analysis will be required to determine the impact of the project. It also provides an example of a low‐cost project which could be replicated by other institutions to positively frame referencing within the context of broader academic writing.

The paper provides an overview of a short project to collaboratively develop a screencast and add value to existing referencing resources (which are predominantly text‐based). The design approach situates referencing within an academic writing continuum seeking to explicitly provide a rationale for the mechanics of referencing, whilst also acknowledging the challenges presented by a rapidly changing information environment.

Using insights from the relevant literature and recent empirical data, this paper investigates the relationship between organisational change and employee turnover. It proposes a mechanism for how widespread change translates into individual decisions to quit, and corroborates four relevant hypotheses. The paper also illustrates the importance for managers of understanding avoidability – the extent to which turnover decisions can be prevented – and concludes with a research agenda, encapsulated by a model describing the relationship between organisational change and turnover.

The purpose of this paper is to identify and discuss the idiosyncratic features of the adoption and institutionalization of corporate social responsibility (CSR) practices.

This is a conceptual paper in which current theory on the institutionalization of practices within organizational fields is extended. This is achieved through considering how well established models of the institutionalization process accommodate the idiosyncrasies of CSR practices.

Established models of the institutionalization process do not properly account for the patterns of CSR adoption that are identified. This is because CSR has some features that differentiates it from other organizational initiatives, including idealism, delayed discovery of instrumental benefits, public attention, and the tension between public and private logics.

This is a conceptual paper which now needs to be explored empirically, either at the level of the CSR practice or at the organizational field. It is believed that a detailed examination is warranted of the effects of the truncated adoption process (a coercive bandwagon) on organizations' adoption of CSR practices. Neither has it been considered whether all categories of CSR practices are subject to the same dynamics or development path.

It is argued that prizes and regulations that are introduced before the organizational case has been worked through properly can have a negative effect on the adoption of beneficial practices throughout the wider field. Similarly, accusations of greenwashing of firms who implement CSR prematurely, and the negative publicity that results, can result in the valuable ideals of CSR being operationalised in a sub‐optimal form.

The paper offers a new conceptualisation of the path of the institutionalization of CSR practices.