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Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

Against the backdrop of the global reshaping of supply chains, supply chain ecosystems have emerged as a critical force in ensuring the high-quality development of enterprises and fostering stable economic growth. However, a systematic theoretical understanding of how to construct these supply chain ecosystems remains nascent. This study aims to explore the mechanism of the process of building supply chain ecosystems between digital innovation platform enterprises and digital trading platform enterprises from the perspective of dynamic capabilities.

An explanatory case study is conducted based on a theoretical framework grounded on dynamic capabilities view. Two preeminent digital platform enterprises in China (Haier and JD.com) are studied. The authors primarily conducted this research by collecting a large volume of these Chinese public materials.

First, the construction processes of supply chain ecosystems in both digital platform enterprises can be delineated into three stages: embryonic, development and maturity. Second, digital innovation platform enterprises’ construction process is primarily influenced by factors such as production and operational collaboration, consumer demand and research and development. This influence is exerted through interactions on digital platforms and within sub-ecosystems. Meanwhile, digital trading platform enterprises’ construction process is influenced by factors such as infrastructure development, consumer demand and financial support, driving dynamic capability formation through multi-party cooperation and ecological interactions based on conceptual identity.

In the establishment of supply chain ecosystems, digital platform enterprises should prioritize the cultivation of opportunity expansion, resource integration and symbiotic relationship capabilities. Furthermore, this study shows that digital platform enterprises need to actively adjust their interactive relationships with cooperating enterprises based on changes in the market, industry, policies and their own developmental stages.

This study addresses prior deficiencies in understanding the comprehensive construction of supply chain ecosystems and provides significant insights to enhance the theoretical foundation of supply chain ecosystem studies. Additionally, this paper uncovers the dynamic capability development behaviors and contextual features inherent in the construction process of supply chain ecosystems by digital platform enterprises.

The proliferation of big data analytics (BDA)-enabled tools and technologies has endowed organizations with the capacity to augment decision-making processes, optimize operational endeavors and foster innovation across diverse business domains. Consequently, BDA has been posited as a catalyst for enhanced customer relationship management, improved risk mitigation strategies and heightened operational efficiencies, all of which converge to augment overall firm performance. Thus, the purpose of this research is to introduce a conceptual framework aimed at explaining the influence of BDA capabilities on the performance of telecommunications firms in Pakistan. Additionally, it examines the potential mediating effect of talent capabilities and moderating effect of top management attitude on firm performance.

Data from a sample comprising 520 participants were collected via survey questionnaires. The study employed Partial Least Squares-Structural Equation Modeling to empirically evaluate the proposed model.

Results reveal a positive association between BDA technology and information capabilities with both BDA talent capabilities and firm performance. Furthermore, the analysis suggests that BDA talent capabilities mediate the relationship between BDA dynamic capabilities and firm performance, while top management attitude acts as a moderator, enhancing the relationship between BDA talent capabilities and firm performance.

There is a scarcity of research that has examined the relationship of BDA capabilities, top management attitude and firm performance. This study attempts to examine their interrelationships. First, it enhances the extant literature by elucidating the mediating role of BDA talent capabilities in the relationship between BDA technology and information capabilities and firm performance. Second, the study introduces a novel dimension by incorporating top management attitude as a moderator variable. This augmentation adds layers of complexity to comprehending BDA implementation dynamics, emphasizing leadership’s role in fostering an enabling environment for effective utilization of BDA capabilities.

Despite the considerable hype about how Big Data Analytics (BDA) can transform businesses and advance their capabilities, recognising its strategic value through successful adoption is yet to be appreciated. The purpose of this paper is to focus on the process-level value-chain realisation of BDA adoption between SMEs and large organisations.

Resource-based theory offered the lens for developing a conceptual BDA process-level value chain adoption model. A combined two-staged regression-artificial neural network approach has been utilised for 369 small, medium (SMEs) and large organisations to verify their critical value chain process-level drivers for successful organisational adoption of BDA.

The findings revealed that organisational BDA adoption success is driven predominantly by product—and service-process-level value, with distinctive discrepancies dependent on the organisation’s size. Large organisations primarily embrace BDA for their external value chain dimensions, while SMEs encompass its internal value chain cues. As such, businesses will be advised to acknowledge their organisational dynamics and precise size to develop the right strategies to adopt BDA successfully.

The study advances the understanding of the role of internal and external value chain drivers in influencing how BDA can be successfully adopted in SMEs and large organisations. Thus, appreciating the organisation’s unique attributes, including its size, will need to be carefully examined. By investigating these elements, this research has shed new light on how developing such innovative capabilities and competencies must be carefully crafted to help create a sustainable competitive advantage.

For an organisational positioning, acknowledging the role of internal and external value chain drivers is critical for implementing the right strategies for adopting BDA. For larger businesses, resources for innovation often can be widely available compared to SMEs. As such, they can manage their costs and associated risks resourcefully. By considering the identified value-chain-related adoption success factors, businesses should be better positioned to assess their competencies while being prepared to adopt BDA.

The study offers the research and business community empirical-based insights into the strategies needed to successfully adopt big data in an organisation from a process-level value chain perspective.

This chapter aims to analyse how tourism has oscillated from a wicked problem and a geopolitical strategy tool in Brazilian federal tourism public tourism policies (PTP) over the past century (spanning from 1921 to 2022). Recently tourism has garnered significant relevance, emerging as an alternative avenue for development within the constraints and resource limitations faced by the National States. The empirical study collected secondary data from the government official press, encompassing records from the Senate, the House of Representatives, as well as the executive and judiciary branches. Considering this timeframe, a corpus comprising more than 31,000 documents TNAs (‘Tourism Normative Acts’) was meticulously gathered and systematically analysed. Our analytical framework integrates classical geopolitics, with a primary focus on State actors and the nation-building process, and the public policy approach, which is focussed on the degrees of wickedness. Our findings show that (a) the number of international tourists as well as the number of NAT have increased in a considerable way recently, but we cannot directly connect both; (b) three are the periods (1970–1980, 1990–2000, and 2002–2016) in which we can see a tourism geopolitical strategy has been more explicitly and effectively mobilized, and it is not necessarily reflected in the number of NAT, but in the actions generated in each period; and (c) the wicked degree of the tourism policies seem to be reduced according to the more explicit geopolitical strategy is. Despite, the importance tourism has reached, the support system underpinning this endeavour remains deficient, notably in terms of material and financial resources essential for its efficacious execution.

Taking job demand-resource (JD-R) and self-determination perspective, the current study focused to see how basic need satisfaction (BNS) – as a personal demand – impacts work engagement directly and indirectly through personal resource (i.e. self-efficacy). Moreover, the aim was to test the dimension-wise impact of BNS, i.e. the need for autonomy, need for belongingness and need for competence in the aforementioned relationship.

This research is a time-lagged survey in which three-wave data of 398 white-collar employees were collected from the service and manufacturing sector of Pakistan through convenience sampling. Each wave of data collection was two months apart. The matched responses yielded an overall response rate of 66.33%. The collected responses were duly analysed using partial least squares structural equation modeling (PLS-SEM).

Results of the study confirmed all direct and indirect hypotheses encompassing the impact of the combined BNS construct on work engagement via self-efficacy. Nonetheless, in the dimension-wise analysis, the indirect impact of the need for job autonomy on work engagement was not validated. This depicted that the need for competence and relatedness are more important predictors of work engagement through the self-efficacy path.

It has been observed that prior research on work engagement was mainly focused on the role of job demands (JDs) and personal resources; however, the role of personal demands along with personal resources has little been discussed. The authors tested the total as well as the specific impact of each component of basic need on work engagement making it possible to examine the total predicting role of basic need satisfaction and the specific contribution of satisfaction of each need on work engagement.

This study evaluates “the potential role of cultural heritage resources in tourism and community development at Musina Municipality, Limpopo, South Africa.”

Data on the local communities were collected by questionnaire surveys, focus group discussions and field observations.

The study revealed a variety of cultural and heritage resources; however, with current fewer implications of tourism welfare on the livelihoods statuses of the communities. Thus, there was a need for a potentially sound tourism strategy in cultural heritage resources to empower the local communities in the study area.

Musina Municipality has some of the most challenging impoverishment attributes within the province, defined by evident poor livelihoods. However, it remarkably possesses rich natural biodiversity and tourism destination areas. In line with the probe on the role of cultural heritage resources in tourism and community development, the study uncovers the values of attributing factors associated with the current nature of social heritage resources and their impacts on tourism and community developments. Issues of heritage and cultural resources on tourism and community development have become of main interest within the tourism industry.

This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.

The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.

Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.

While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.

This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

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.

We identify lessons from a project sponsored by a large charitable trust, which sought to build capability for end-of-life (EOL) care in Hong Kong through interdisciplinary and multi-agency collaboration.

An in-depth case study drawing on 21 in-depth interviews with diverse stakeholders was conducted. Lyman et al.’s (2018) model of organisational learning (OL) in healthcare settings was applied to analyse the relative emphasis on particular contextual factors and mechanisms, and to identify outcomes perceived to have been achieved.

Infrastructure such as materials for assessment and education received the most emphasis among the contextual factors and deliberate learning such as training sessions received the greatest attention among the mechanisms. While perceptions indicated that desired outcomes were being achieved in terms of social impact, there were relatively few mentions of “soft” factors such as enhanced motivation, leadership or OL skills among staff.

This study extends the literature on how to create valuable social impact through OL. While prior studies have examined social impact in terms of solutions for social and environmental problems, ours is one of the few that examines how improvements are made to organisations’ capability to deliver such impacts in the context of healthcare.