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Agile project teams are self-managing and self-organizing teams, and these two characteristics are pivotal attributes of emergent leadership. Emergent leadership is thus common in agile teams – however, how these (informal) emergent leaders can be identified in teams remains far from understood. The purpose of this research is to uncover techniques that enable top management to identify emergent agile leaders.

We approached six agile teams from four organizations. We employ social network analysis (SNA) and aggregation approaches to identify emergent agile leaders.

We approached six agile teams from four organizations. We employ SNA and aggregation approaches to identify emergent agile leaders.

Seven emergent leaders are identified using the SNA and aggregation approaches. The same leaders are also identified using the KeyPlayer algorithms. One emergent leader is identified from each of the five teams, for a total of five emergent leaders from the five teams. However, two emergent leaders are identified for the remaining sixth team.

Emergent leadership is a relatively new phenomenon where leaders emerge from within teams without having a formal leadership assigned role. A challenge remains as to how such leaders can be identified without any formal leadership status. We contribute by showing how network analysis and aggregation approaches are suitable for the identification of emergent leadership talent within teams. In addition, we help advance leadership research by describing the network behaviors of emergent leaders and offering a way forward to identify more than one emergent leader in a team. We also show some limitations of the approaches used and offer some useful insights.

Despite some academic recognition that leadership is particularly significant in reshaping the oil and gas industry’s contributions to global economic development and the sustainability of global energy supplies at affordable prices (Sharma et al., 2022), the attendant problem of how the industry’s leadership contributes towards the preservation of global environment and the maintenance of ecosystems’ balance, among other sustainability challenges, remains an academic lag. This calls for the urgent need for oil and gas companies to practice effective sustainable leadership approach at multiple organisational levels to address global environmental, economic and social challenges.

This paper adopts an interpretivist/constructivist philosophical stance, where findings have been extracted from in-depth thick descriptive qualitative research in Bahrain oil and gas industry. Companies operating within Bahrain oil and gas industry were identified as the unit of analysis. Empirical data are gathered through semi-structured interviews from senior management and analysed using thematic analysis. This paper is structured as follows: introduction, contextualisation of the UNSDGs in the oil and gas industry, literature on network leadership, research methods used to gather and analyse data from Bahrain oil and gas industry findings, contributions, limitations and trajectories for further studies.

The study’s participants argued that the emergence of transformational and Sustainable Network Leadership is essential to successfully and sustainably implementing the UN SDGs. In other words, the Sustainable Network Leadership is a contribution to the single, leadership competences approach of previous scholarship (Weber et al., 2022; Kumalo and Scheepers, 2021) partly because it evolves around the notion of positioning different network and change actors based on their capacity to lead, exchange their knowledge, effectively communicate the need to comply with SDGs and the skills to establish high density within a complex network of actors.

This study recognises its limitations in the sense that it is based on the single context of Bahrain oil and gas, and data were collected from senior management and executives only. Gathering data from a broader swathe of employees may have provided greater levels of leadership and organisational member nuances in both single and collective differences of leadership attributes.

Transformational and Sustainable Network Leadership provides a new construct in the perception (the “what”), instrumentalisation (the “how”) and theoretical re-conceptualisation of leadership within organisational change settings needing radical rethink for sustainable and successful change. The practical implications of transformational and Sustainable Network Leadership expose the way a variety of highly challenged organisational change contexts are interconnected to highlight not only their different sets of challenges but also their opportunities and the resolution mechanisms they present for organisational leaders and staff alike.

This paper identified the single, leadership competence approach as the dominant discourse in organisational change, leadership studies and presented an alternative collective set of leaders’ attributes. The less utilised network leadership concept was drawn upon to contribute network leadership attributes as a way of addressing the challenges faced by Bahraini oil and gas company leaders. Therefore, this study contributes to both network leadership, transformational leadership by expanding both domains to include organisational transformation and the leadership-of-risky change. This was done by identifying attributes and characteristics of Sustainable Network Leadership, then showing their significance as an approach to successfully and sustainably implementing the UNSDGs.

In the literature, while designing broadband matching networks, transducer power gain (TPG) is used to measure the transferred power. Generally, in TPG expressions, load and back-end impedances of the matching network are used. This study aims to derive a new quality factor-based TPG expression.

In deriving the new expression, narrowband L type-matching network design approach is used and the new expression in terms of back-end quality factor, load quality factor and output port quality factor is obtained. Then, a broadband-matching network design approach using the derived TPG expression is proposed.

Two broadband double-matching networks are designed by using the proposed design approach using the derived TPG expression. Performances of the designed-matching networks are compared with the performances of the matching networks designed by means of simplified real frequency technique which is a well-known technique in the literature, and it is shown that they are nearly the same.

In broadband-matching problems, generally an impedance-based TPG expression is used, and it must be satisfied by the designed broadband-matching networks. But, in the literature, there is no quality factor-based TPG expression that can be used in broadband-matching problems. So, this gap in the literature has been filled by this paper.

The first purpose is to assess the quality of containerized multimodal export and the second is to develop and demonstrate the design of a service network with quality approach.

The article used the structural equation model to develop a model to measure the quality of multimodal transportation for containerized exports and finalized the model with an alternative approach. The evolutionary algorithm had been used to design a service network based on quality.

Provided factors affecting quality of multimodal transportation and reverse to one hypothesis, the construct variation in cost, time shape and quantity did not affect the quality of multimodal transportation for containerized exports. The model without variation construct was finalized by exploring causality.

This research had scope till container loading onto the vessel and assessed the quality for containerized cargo only, and second research purpose is limited by assumed values of fitness function and the limited number of nodes, in service network design demonstration.

This research provided a tool to measure the quality of multimodal transportation for containerized exports and demonstrated the field application of the model developed in service network design. This approach included all factors applicable across the container movement. The integrated approach of the article provided an organized method to design a service network for containerized exports.

This work provided the tool to assess the quality of multimodal transportation for containerized exports and developed an approach to design a service network of multimodal transportation based on quality. This approach has considered the factors of multimodal transportation comprehensively in contrast to the optimization approaches based on operation research techniques.

The aim of this research is to elucidate the correlation between open innovation, digital strategies and networking in enhancing agricultural enterprises within the new perspective of Agrifood 5.0. As such, it contributes to making businesses more competitive, especially in the Italian agricultural sector, where small and medium-sized enterprises are highly fragmented. Numerous studies have asserted that the competitiveness of actors operating within a specific territory is closely linked to local identity and image enhancement. Agricultural organizations are undergoing a profound transformation, with technological assets emerging as catalysts for new synergies. Advanced technologies such as robotics, the Internet of Things (IoT) and automation (AI) are emerging as differentiating elements capable of further advancing the agricultural sector, transitioning it from Agrifood 4.0 to Agrifood 5.0. The empirical analysis of the research shows a positive correlation between a collaborative attitude and a propensity for innovation. Indeed, the data demonstrated that digital strategies and open innovation positively influence competitiveness in agricultural SMEs.

The methodology employed in this study is mixed, incorporating both qualitative and quantitative approaches. The quantitative aspect involves analysis of the dataset from the Italian Statistical Institute (ISTAT) through logistic regression, while the qualitative component entails analysis of semi-structured interviews conducted with a sample of 174 agricultural cooperatives in southern Italian regions (Campania). This approach allows for a comprehensive understanding of the research topic, capturing both numerical trends and nuanced insights from interviews.

After analyzing the data from the 7th General Census of Agriculture conducted by ISTAT, a clear understanding of the sector has emerged, revealing several potential research avenues. It is evident that innovation in the agricultural sector is often driven by the largest and best-capitalized production entities, primarily located in Italy. Conversely, smaller agricultural entities can benefit from networking as new technological assets act as catalysts for new synergies, innovation and competitiveness.

Enhancing the relational contribution within the network and humanizing a fragmented sector are crucial elements for promoting open innovation. Network structuring facilitates the transmission of managerial knowledge, contributing to an overall increase in the intellectual and relational capital of the agricultural sector. These factors, combined with open innovation, enhance the competitiveness of individual firms and elevate the brand of the entire sector, creating a conducive environment for transitioning toward Agrifood 5.0. This transition is characterized by increased interconnection, continuous innovation and overall prosperity. Specific studies on this topic are lacking in Italy, particularly in the southern regions. Therefore, this contribution focuses on investigating the Campania region.

The novelty of this study lies in its investigation of the relationship between agricultural enterprises and innovation in the context of enterprises networking strategies (i.e. associationism and/or cooperation), promoting competitiveness. The limitations of this study are related to the dimension of the sample selected and its relationship with other productive sectors.

This paper aims to investigate which network relationships foster the early development of a sustainable new venture (SNV) and how sustainability as the core characteristic of the new venture shapes those network relationships.

This paper relies on a qualitative approach. The primary data source is 25 interviews with 18 key informants of 15 Italian SNVs. The fashion industry is the empirical setting due to its negative environmental and social impacts and shifts toward sustainability during the past decade.

The paper identifies six types of network relationships that affect the development of fashion SNVs. It proposes sustainability-enhanced and sustainability-enabled network relationships and relates them to trust and legitimation in the network.

The study enriches the theoretical debate on networks, new ventures and sustainability by dealing with the case of SNVs in a traditional sector. This paper presents managerial implications for entrepreneurs and policymakers.

This paper contributes to the debate on society’s sustainable development by emphasizing how networks can affect the growth of SNVs.

This paper fills a research gap in a novel manner. The paper contributes to the recent debate on new ventures and sustainability from the market as network approach. It identifies relevant networks, their contribution and the role of sustainability. The study refers to SNVs in traditional nontechnological industries.

The purpose of this paper was to report on the evaluation of the network and resources for violence prevention and reduction in the chosen area of focus. This area had experiences deprivation, significant implications due to Covid-19 restrictions and a lack of outdoor recreation space.

Network analysis methodologies are increasingly being used in criminological research and evaluations to assess the structures of social and economic networks. This study explored, using a mixed-methods network analysis methodology, the nature of the established violence reduction network in a specific geographical location in West Midlands.

A breadth of network activity is taking place across the community; however, the network analysis highlighted gaps in terms of specialist provision for early years and support from those with lived experience. It was perceived that a lack of continuity, in terms of changes in key roles, has affected the network. Funding mechanisms were perceived ineffective, and not encouraging of development of localisation services. Relationships between network members were predominantly positive with organisations having good communication and accessing support from one another; however, identifying shared goals and better collective working would benefit the network.

This study pioneers using an innovative, mixed methods network analysis to explore a public health approach to violence prevention and reduction. Quantitative data collection and analysis allowed for assessment of the networks capacity and density, whereas qualitative data provided insights and detailed accounts of how the network functions.

This study aims to investigate the content of hotel brands’ mission statements (MSs) and their relationship with selected attributes of hotel brands.

Content analysis of hotel brands’ MSs was used to detect the MSs’ key words, which were further processed by methods of social network analysis, complemented by clustering techniques and correspondence analysis on the generalized aggregated lexical tables, a special type of correspondence analysis.

Hotel brands operating in luxurious markets more often emphasize experiences than those in midscale markets. Furthermore, hotel brands with longer traditions and those with a large number of controlled rooms communicate words in their MSs that represent a rather traditional approach to hospitality. Younger hotel brands with fewer controlled rooms chose words that indicate a more commercially oriented approach. Finally, cluster analysis revealed four dimensions of hotel brands’ MSs, instead of the nine most typically used in mission statement component models.

Understanding the frequencies and networks of keywords, and their relationship with hotel brand attributes, will help create more focussed MSs. This will strengthen hotel brands, raise their revenues and subsequently increase company performance.

The analysis provides valuable insight into MSs in the specific tourism context of hotel brands. The authors have achieved this with the use of a wide range of advanced network analytic methods. These insights can guide hotel brands to better position themselves in the competitive tourism accommodation market.

The research question is how can a company implement a circular innovation in a supply network context? Leveraging the main conceptual and interpretative models of the industrial marketing and purchasing thinking, this study aims to investigate the interplay between the process of circular innovation development and the changes in the structure and dynamics of the supply network in which innovation takes place.

This research applies a case study design focusing on participant interaction dynamics. The case relates to an industrial company producing an innovative coating solution for compostable packaging. The data used to develop the case study came from multiple sources but primarily from semistructured interviews that cover the implementation of the circular innovation and the configuration of the circular network.

The dynamics of interconnected relationships can configure a circular network that interconnects business and non business actors through vertical, horizontal and heterogeneous relationships. The network configuration is supported by the new mobilizer actor that facilitates the sharing of circular knowledge within the circular network, together with the sharing of a market orientation and entrepreneurial orientation within the supply network, through the educational learning path.

This paper aims to contribute to a new understanding of how circular innovation can be developed, adopted and diffused. In a network, when circular innovation takes place, the focal issue is not the new product or technology in itself but how such innovation is developed and implemented by and through the reconfiguration of the business and non-business relationships into circular network.

This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism.

This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism.

This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management.

This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

本研究旨在开发一种基于LSTM的强大预测模型, 用于预测仁川国际机场的日常国际游客抵达量, 结合多种预测因素, 包括汇率、WTI原油价格、韩国综合股价指数 (KOSPI) 数据和新冠疫情病例。通过利用深度学习技术和多样化数据集, 研究旨在提升旅游需求预测的准确性和可靠性, 对酒店与旅游领域的理论和实际应用有重要贡献。

本研究通过利用长短期记忆（LSTM）网络引入创新方法, 预测国际游客抵达量。这一先进方法解决了旅游管理中的复杂管理问题, 提供了更精确的预测。方法论包括四个关键步骤: (1) 收集数据集; (2) 数据预处理; (3) 训练LSTM网络; 以及 (4) 预测未来的国际游客抵达量。本文的其余部分结构如下：后续部分详细介绍了提出的LSTM模型, 呈现了实证结果, 并讨论了研究的发现、结论以及在酒店与旅游领域的理论和实际意义。

本研究首次同时使用包括国际游客抵达量、汇率、原油价格、股市数据和新冠疫情病例在内的大数据进行日常预测。研究显示, 整合汇率、原油价格、股市数据和新冠疫情病例显著增强了LSTM网络的预测精度。研究填补了现有研究在使用这些因素预测仁川国际机场国际游客抵达量的狭窄范围。此外, 研究证明了LSTM网络在处理多变量时间序列预测问题上的能力, 为其在酒店与旅游管理中的应用提供了坚实基础。

本研究首次将国际游客抵达量、汇率、WTI原油价格、KOSPI数据和新冠疫情病例整合到日常国际游客抵达量的预测中。它通过提出同时考虑多个预测因素的全面方法, 弥合了现有文献的差距。此外, 研究展示了LSTM网络在处理多变量时间序列预测问题方面的有效性, 为增强旅游需求预测提供了实用见解。通过处理这些关键因素并利用先进的深度学习技术, 本研究在旅游业预测方法的进步中做出了重要贡献, 帮助决策者进行有效的规划和资源配置。