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The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

This study aims to focused on conducting a comprehensive assessment of the technology readiness level (TRL) of Iran’s oil field intelligence compared to other countries with similar oil reservoirs. The ultimate objective is to optimize oil extraction from this field by leveraging intelligent technology. Incorporating intelligent technology in oil fields can significantly simplify operations, especially in challenging-to-access areas and increase oil production, thereby generating higher income and profits for the field owner.

This study evaluates the level of maturity of present oil field technologies from the perspective of an intelligent oil field by using criteria for measuring the readiness of technologies. A questionnaire was designed and distributed to 18 competent oil industry professionals. Using weighted criteria, a mean estimate of oil field technical maturity was derived from the responses of respondents. Researchers evaluated the level of technological readiness for Brunei, Kuwait and Saudi Arabia’s oil fields using scientific studies.

None of the respondents believe that the intelligent oil field in Iran is highly developed and has a TRL 9 readiness level. The bulk of experts believed that intelligent technologies in the Iran oil industry have only reached TRL 2 and 1, or are merely in the transfer phase of fundamental and applied research. Clearly, Brunei, Kuwait and Saudi Arabia have the most developed oil fields in the world. In Iran, academics and executive and contracting firms in the field of intelligent oil fields are working to intelligently develop young oil fields.

This study explores the level of maturity of intelligent technology in one of Iran’s oil fields. It compares it to the level of maturity of intelligent technology in several other intelligent oil fields throughout the globe. Increasing intelligent oil fields TRL enables better reservoir management and causes more profit and oil recovery.

Technological advancements are reshaping the tourism industry, necessitating the adaptation of business models through digital technology utilisation for intelligent, sustainable and inclusive tourism offerings. The diverse nature of tourism businesses, encompassing size, technology access, risk aversion, labour intensity, and more, presents a spectrum of challenges and opportunities for business model innovation (BMI) and digital transformation (DT) to maintain competitiveness. This study focuses on the core aspects of DT and BMI within the tourism sector, offering pivotal insights to aid tourism companies embarking on the intricate journey of DT and BMI in this evolving landscape.

The study conducts an extensive literature review to identify critical issues and pathways for tourism businesses pursuing DT and BMI. The review is focused on the challenges, opportunities, risks and imperatives that tourism organisations have to navigate in the current DT landscape to renew their business model.

The findings underscore the pressing need for tourism businesses to undergo a holistic DT. While digital technologies are reshaping the essence of tourism value chains, the transformation extends beyond technology adoption to encompass a profound renewal of organisational culture, competencies, structure, leadership and operational models. This paradigm shift is indispensable for crafting more innovative, sustainable and more inclusive tourism development. The paper also provides strategic recommendations and outlines future research directions to fortify the transformational journey of the tourism sector.

The paper provides key insights into supporting DT and BMI in tourism businesses, advancing a nuanced understanding of the multifaceted challenges and opportunities tourism organisations face in the digital age.

Given its ability to improve user interaction and labor productivity, ease human workloads and cut maintenance costs, public sectors are using AI-based robotic technology (AI-RT) at an accelerated rate. There is, however, little knowledge about the variables affecting citizens' participation when services backed by AI-RT are offered. In order to better understand the elements that influence AI-RT citizens' involvement and the moderating function of trusts in governmental organizations, this article draws on ideas from the Consumer Value Theory.

Out of 500 survey forms that were distributed to Indonesian people who had experience in using AI-RT devices in public service hall (e.g. airport’s Auto Gate), 367 returned the completed feedbacks. Data analysis used a step-by-step hierarchical moderated regression examination using SPSS 24 version.

Citizens’ involvement is positively correlated with esthetics and customization and adversely correlated with period expended using the AI-RT. Additionally, the findings imply that citizens who have greater levels of faith in governmental institutions are more likely to benefit favorably from the customization and esthetics of AI-RT.

The AI-RT must be capable of customizing the distribution of the appropriate materials to the appropriate individual at the appropriate moment, and public managers should guarantee that it is esthetically pleasing. Additionally, they ought to place a high priority on winning the trust of the populace in order to increase citizens’ involvement.

This paper was among the initial efforts that discover the determinants of citizens’ involvement in the AI-RT and the moderating effect of trusts in governmental organizations on the links between predictors and predicted variable, especially in an emerging country such as Indonesia.

This study aims to develop a talent selection model for learning organizations capable of connecting two groups, candidates in a talent hiring process and managers of the hiring company, in a reliable process, promoting organizational learning and increasing employee satisfaction.

This paper integrates egalitarian principles, an artificial intelligence mechanism founded on stable matching algorithms, and evaluating critical soft skills to enhance recruitment practices within learning organizations. The authors conduct a numerical real-world application in Python to showcase the model’s effectiveness. Five candidates were evaluated for five job positions. Moreover, 26 soft skills were analyzed by the five company leaders, relating them to the requirements of each job position and by all candidates, as a self-assessment process.

The model promoted egalitarian talent management because it motivates the candidates to choose the preferred position in a company, and the employers hire the best candidate. It is satisfactory for all participants in a company’s hiring process if the parties intend to be fair and egalitarian. The benefits of the process can be considered isolated (parties’ satisfaction) or a part of a company’s effort to stimulate an egalitarian culture in organizational values.

The information generated by the model is used to refine its selection process and improve its understanding of the job requirements and candidate profiles of the company. The model supports this idea, using the concepts of indifference, stability, egalitarianism and the soft skills required and identified to be more effective and learn about themselves.

This paper discusses an egalitarian point of view in the recruitment process. It is satisfactory for all participants in a company’s hiring process if the parties intend to be fair and egalitarian. The process’s benefits can be considered part of a company’s effort to stimulate an egalitarian culture in organizational values.

This paper brings an excellent future perspective and points to the company’s development of talent retention. The model simultaneously solves the evolution of talent management processes through new technologies and soft skills emerging in the postpandemic scenario.

In view of the increasing exploration and scattered literature on digital leadership, this study aims to clarify the concept, characteristics and structure of digital leadership, identify its focus research themes and further determine future research directions in this field.

A systematic literature review is adopted in this study using 53 academic documents published in peer-reviewed journals and conference proceedings, which comprehensively combs and analyzes the research achievements.

The key characteristics of digital leadership are vision motivation, digital empowerment, innovation and entrepreneurship, cross-boundary collaboration and dynamic adaptation. Digital strategic thinking, digital insight, digital change and digital talent development are the core dimensions of digital leadership. Besides, there are three main research topics: the application of digital leadership in certain contexts including school education, government governance and business management; the development of digital leadership scale within diverse contexts; and the effectiveness of digital leadership at the organizational, team and individual levels. Digital leadership has a positive role in organizational change and innovation, team effectiveness, and employee creativity and innovation behavior.

This study helps to clarify the connotation of digital leadership and its research progress, status and gaps, which contributes to the thorough knowledge system in the digital leadership area and the promising avenues for future research.

Small- and medium-sized enterprises (SMEs) are key players in economic activities. Yet little research focuses on their management innovation’s effect on innovation performance, this paper explores the impact of three sub-dimensions of management innovation (new organizational structure, new processes and new practices) on innovation performance (innovation outcome performance and innovation process performance) of SMEs in the manufacturing industry. It elucidates the mediating role of external knowledge search (search depth and search breadth) and identifies the moderating effect of digital capability.

By collecting primary data through a questionnaire survey, this paper employs Mplus and SPSS to analyze data.

This paper finds that management innovation positively affects innovation performance, with a greater impact on innovation outcome performance. Search breadth partially mediates the relationship between new organizational structure and innovation performance. Digital capability positively moderates the positive relationship between management innovation and external knowledge search.

The theoretical contribution of this study is to supplement the research on the consequences of management innovation and to improve the theoretical relationship between management innovation and external knowledge search based on firm behavior theory.

This study explores how introducing a permissioned blockchain in a supply chain context impacts accountability relationships and the process of rendering an account. The authors explore how implementing a digital transformation impacts the governance of network transactions.

The authors mobilize 28 interviews and documentary analysis. The authors focus on early blockchain adopters to get an insight into how implementing a permissioned blockchain can transform information sharing, coordination and collaboration between business partners, now converted into network participants.

The authors suggest that implementing a permissioned blockchain impacts accountability across three levers, namely through the ledger, through the code and through the people, where these levers are interconnected. Blockchains are often valued for their ability to enable transparency through the visibility of transactions, but the authors argue that this is an incomplete view. Rather, transparency alone does not help to satisfy a duty of accountability, as it can result in selective disclosure or obfuscation.

The authors extend the conceptualizations of accountability in the blockchain literature by focusing on how accountability relationships are enacted, and accounts are rendered in a permissioned blockchain context. Additionally, the authors complement existing work on accountability and governance by suggesting an integrated model across three dimensions: ledger, code and people.

The desire of international retail brands to implement sustainable supply chain practices in the fashion value chain and improve suppliers' sustainability efforts; this research paper elucidates the relationship between blockchain technology and sustainability to impact apparel firms' triple bottom line.

For studying the impact of sustainable supply chain practices on the triple bottom line, a survey questionnaire was chosen and sent out to 500 garment companies simultaneously, of which 371 responded. The data collected is cross-sectional. The questionnaire survey was developed keeping in mind a few demographic elements such as experience, age and qualification to generalize the findings. For analysis, SmartPLS is used to run model structuring and regression analysis.

Test runs on model structure confirm the instrument's validity and reliability. Bootstrapping on the theoretical model to test developed hypotheses suggests that supply chain sustainability practices positively affect social, environmental and economic performance in a direct relationship. Further, indirect relation testing conducted to test blockchain technology's moderation influences only the constructs' relations.

The clubbing of sustainable supply chain practices and blockchain technology is a novel idea in the apparel industry; however, there are more constructs in the context of practice-based theory and supply chain which impact firm performance. Also, the research limits itself from discussing IT infrastructure and smart contract types that impact the technology's performance.

The study provides a framework for interpreting the synergetic influence of SSCP on firm social, environmental and economic performances, which is demanded both by consumers and regulators in an industry. The results suggest that managers sustainably design the production ecosystem, thus eliminating any discrepancy or slackness in the complete chain. Usually, suppliers are ignored, which are precursors in implementing SSCP.

The paper studies sustainability problems through ecological modernization theory and practical-based theory giving a unique perspective on the issue faced by the apparel industry and combining sustainable supply chain practices and blockchain.

Although it is commonly agreed that prescriptive analytics can benefit organizations by enabling better decision-making, the deployment of prescriptive analytics tools can be challenging. Previous studies have primarily focused on methodological issues rather than the organizational deployment of analytics. However, successful deployment is key to achieving the intended benefits of prescriptive analytics tools. Therefore, this study aims to identify the enablers of successful deployment of prescriptive analytics.

The authors examine the enablers for the successful deployment of prescriptive analytics through five organizational case studies. To provide a comprehensive view of the deployment process, each case includes interviews with users, managers and top management.

The findings suggest the key enablers for successful analytics deployment are strong leadership and management support, sufficient resources, user participation in development and a common dialogue between users, managers and top management. However, contrary to the existing literature, the authors found little evidence of external pressures to develop and deploy analytics. Importantly, the success of deployment in each case was related to the similarity with which different actors within the organization viewed the deployment process. Furthermore, end users tended to highlight user participation, skills and training, whereas managers and top management placed greater emphasis on the importance of organizational changes.

The results will help practitioners ensure that key enablers are in place to increase the likelihood of the successful deployment of prescriptive analytics.