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Organisational slack has been widely considered in strategic management, but there is a gap in understanding the process of accumulation and application of slack resources. From a dynamic perspective and over an extended period of time, this paper analyses the management of slack resources and evaluates whether the different behaviours, in relation to the accumulation and consumption of slack resources, have any effect on performance.

The resource-based view and the dynamic extension of this theory, i.e. resource management and resource orchestration, were analysed in order to evaluate how slack resources can be managed and generate value. Assuming a configurational approach, the analysis was structured into two stages to answer the proposed hypothesis. The first stage studied whether there were different patterns of management of slack resources over time using the DistatisR package. The second stage evaluated which behaviours had the greatest impact in terms of profitability by using a dynamic panel data regression.

Three different types of slack resource management were found in companies: efficient, effective and erratic. Different types do not have the same impact on performance.

The dynamic management of slack resources has scarcely been considered, even during periods of crisis and economic expansion. This research advances the understanding of how firms transform slack resources into performance from a dynamic perspective.

This analysis of chat reference transcripts assesses differences in how librarians and graduate assistants (GAs) incorporate teaching strategies in responding to chat reference inquiries in social sciences, health sciences, humanities, STEM and business/economics at a large, public R1 university in the United States.

Researchers with disciplinary assignments in five different subject domains conducted qualitative analysis of a purposive sample of 982 transcripts of chat interactions during four semesters in 2021 and 2022.

Some form of information literacy instruction (ILI) occurred in 58% of the transcripts, with slightly less teaching occurring in social sciences inquiries than in other subject areas. Of transcripts that included teaching strategies, search procedures predominated, followed by a mix of concepts and procedures, and the least with concepts only. Chat providers taught concepts specific to social sciences, health sciences and humanities, but not to STEM or business.

The study compares transcripts at one institution; findings may be most applicable to large, research institutions that seek to incorporate ILI in online reference services.

Chat reference training should include best practices for ILI relevant to specific subject domains for providers without background in those disciplines and recommendations for referrals to subject specialists.

Existing ILI literature does not address the question of how chat providers teach concepts rooted in a specific subject domain or offer a comparison of teaching strategies employed in different disciplines, by librarians versus GAs or staff.

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.

This study aims to distinguish how unabsorbed and absorbed slack affects market and financial performance via proactive and reactive supply chain resilience (SCRES), particularly under varying conditions of organizational ambidexterity.

By collecting survey data from 277 Chinese manufacturers, we verify the conceptual model applying structural equation modeling.

Proactive SCRES mediates the positive impacts of both unabsorbed and absorbed slack on market and financial performance, whereas reactive SCRES mediates only their positive effects on financial performance. High levels of organizational ambidexterity strengthen the indirect effects of both types of slack on market and financial performance via proactive SCRES, but not when mediated by reactive SCRES.

We introduce a new theoretical perspective to view fits (as mediation) between the use of unabsorbed/absorbed slack in different ways when switching attentions to proactive or reactive SCRES, both of which can be improved through organizational ambidexterity. This study offers novel insights into how managers can switch attentions between proactive and reactive SCRES knowing when to appropriately use unabsorbed/absorbed slack for which purposes, and the use of different learning modes (explorative vs exploitative).

This research examines the long-held belief in the adaption-related literature that a firm’s ability to adapt organizational structure to changing environments is related to superior performance. We create and test a construct that reflects an organization’s ability to change structure, which we call Supply Chain Structural Adaptability (SCSA), rather than relying on proxies (e.g. structural form or organizational modularity) used in prior studies.

Quantitative data was collected from 218 firms to test our conceptual model.

We find that SCSA has a mixed effect on profitable growth under various environmental conditions.

We find evidence that refutes two widely held assumptions in organization research, namely, that structural form serves as a reasonable proxy for structural adaptability and that the benefits of adaptive capabilities always increase as environmental dynamism increases.

This paper seeks to understand how a new elite, known as the cork aristocracy, emerged in the Bordeaux wine field, France, between 1850 and 1929 as wine merchants replaced aristocrats. Classic class and status perspectives, and their distinctive social closure dynamics, are mobilized to illuminate the individual and organizational transformations that affected elite wineries grouped in an emerging classification of the Bordeaux best wines. We build on a wealth of archives and historical ethnography techniques to surface complex status and organizational dynamics that reveal how financiers and industrialists intermediated this transition and how organizations are deeply interwoven into social change.

This paper aims to compare the mission statements of Indian and Singaporean firms in the healthcare sector, and define the main components of Indian and Singaporean mission statements.

The study was based on a network analytic approach and content analysis. The research was performed on 200 companies (100 Indian companies and 100 Singaporean companies). For each company, we searched for a mission statement published in the company website. Nonnegative Matrix Factorization (NMF) in Python programming language was utilized to obtain the differences in the components of mission statements between Indian and Singaporean firms.

The study results indicate a similarity and variation between Indian and Singaporean mission statements. Both countries are more concerned about patients, service, community, quality, and healthcare in their mission statements, but Indian mission statements emphasize quality, affordable price, and technology more than Singaporean firms. In contrast, Singaporean mission statements tend to highlight innovation and company value. This research will assist strategic managers in identifying the mission statement components and choosing the right strategy for the organization.

This study contributes to the literature and ethos theory by identifying and distinguishing the paramount differences between the Indian and Singaporean mission statement components in the healthcare sector.