Abstract

Global virtual teamwork has emerged as a cornerstone of collaborative teamwork in today’s work landscape, characterized by cross-border collaboration using digital technologies. Although research has started to investigate the mechanisms underlying effective teamwork through information and communication technologies (ICTs), an often-neglected dimension is the pivotal social context within which this collaboration unfolds. To address this research gap, this study adopts a social capital lens on teamwork in proposing social capital as a multidimensional mediator between the usage of ICTs and team effectiveness. The research model was tested using questionnaire data from 271 technologically sensitive and aware companies in the DACH region (Germany, Austria, Switzerland). The findings reveal that using multiple ICTs alone does not suffice for effective global virtual teamwork. Therefore, this study highlights the significance of social capital for the effectiveness of global virtual teamwork, which holds significant implications for theory and practice.

Introduction

Advances in information and communication technologies (ICTs), coupled with the increasing prevalence of globalization, have driven organizations to rely on digital technologies for globally distributed collaboration—so-called global virtual teamwork ( Tavoletti & Taras, 2023 ). Due to several benefits, such as time and cost savings, talent pooling, and increased team diversity, global virtual teams were already well established in today’s workplace before the COVID-19 pandemic ( Jimenez, Boehe, Taras, & Caprar, 2017 ; Kozlowski & Ilgen, 2006 ; Tavoletti & Taras, 2023 ). However, global virtual teamwork also presents significant challenges that can threaten team effectiveness. Reliance on ICTs reduces media richness and delays feedback, including an increased risk of miscommunication and misunderstanding ( Bilotta et al., 2021 ; Klitmøller & Lauring, 2013 ; Mangla, 2021 ).

Based on the premise that a single medium cannot support the two fundamental communication processes of conveyance (i.e., sharing new information) and convergence (i.e., reaching a common understanding), we introduce the concept of ICT usage, which refers to the frequent and strategic use of multiple ICTs ( Dennis, Fuller, & Valacich, 2008 ; Niinimäki, Piri, Lassenius, & Paasivaara, 2012 ). We propose that high levels of ICT usage will increase team effectiveness, which consists of performance, longevity, and satisfaction ( Dennis et al., 2008 ; Hackman, 1987 ).

Conclusion

The confluence of digitalization and globalization has had an enduring influence on utilizing virtual and global collaboration in the workplace. Due to their embeddedness in a social context, teams are a focal point of multiple levels, linking the individual team members and the organizational system holistically ( Kozlowski & Bell, 2013 ). This requires a profound comprehension of the social context, which can enhance the uptake of ICTs and the effectiveness of global virtual teamwork. To fill this research gap, we conceptualized social capital as a multidimensional mediator, encompassing structural, cognitive, and relational social capital ( Nahapiet & Ghoshal, 1998 ), between the adoption of ICTs and team effectiveness composed of performance, satisfaction, and longevity.

In summary, this study provides a solid foundation for integrating social capital in global virtual teamwork. The results of our study indicate that frequent use of multiple ICTs does not influence the effectiveness of global virtual teams. Instead, a significant positive relationship was found between ICT use and social capital. Regarding the individual dimensions of social capital, this relationship was only found for structural social capital. Similar results were obtained for the mediator hypotheses, with both total and structural social capital acting as strengthening mediators between ICT usage and team effectiveness. Conversely, cognitive and relational social capital did not show significant mediating effects. The present research context makes developing cognitive and relational social capital difficult, although their importance cannot be denied, as both social capital and its individual dimensions show a positive relationship with team effectiveness. Possible ways to build cognitive and relational social capital include allocating sufficient time for team interactions and creating shared physical spaces, such as face-to-face meetings, to balance them. Overall, our study provides insights into the extent to which the integration of social capital enhances team effectiveness in today’s digitized and globalized world.

Abstract

Previous studies have addressed social media adoption in business-to-business (B2B) contexts, but limited research has focused on understanding social media marketing assimilation in the B2B context. Using an integrative model, this study examines how top management participation influences the assimilation of social media in the key marketing areas of product development, pricing decision-making, channel management, and promotion. Furthermore, it examines the resulting impact of these assimilations on B2B firms’ performance, particularly in respect of sales performance and relationship development. The study also examines the moderating impact of absorptive capacity on top management participation in the assimilation processes. The findings reveal that top management participation strongly influences social media assimilation into marketing functions. However, the impact of social media assimilation on B2B firms’ performance is varied. While assimilation for channel management positively impacts both sales performance and relationship development, assimilation for product development is only positively related to relationship development. Conversely, assimilation into the functions of pricing decisions and promotion activities shows no significant impact on either sales performance or relationship development. Finally, absorptive capacity positively moderates top management participation in social media assimilation into all key marketing functions except one. The theoretical and managerial implications of these findings are discussed.

Introduction

Social media have emerged as a “game-changer” (Kumar, 2015, p. 5), playing a leading role in the revolutions taking place in business-to-consumer (B2C) and business-to-business (B2B) marketing. B2C marketing uses social media marketing widely, and B2B operations have begun embracing it to streamline their internal marketing communication, generate leads, drive sales, and strengthen customer relationships. Chaffey (2018) found that 79% of B2B marketing specialists rated social media as the most effective marketing channel, with a further 38% indicating that, if they had surplus in their budget for marketing in the following year, they would devote it to social media marketing. Similarly, Elevation Marketing (2020) noted that 90% of B2B buyers use social media to engage with industry thought leaders, with a further 72% indicating that they use social media channels to research solutions.

B2B social media research tracks the increase in social media use in this business interface. Previous studies have addressed several aspects of social media use in the context of B2B, including salespersons’ social media use in B2B relationships (Bill, Feurer, & Klarmann, 2020; Bowen, Lai-Bennejean, Haas, & Rangarajan, 2021; Fraccastoro, Gabrielsson, & Pullins, 2021), B2B social media adoption (Dwivedi, Ismagilova, Rana, & Raman, 2021; Lashgari, Sutton-Brady, Søilen, & Ulfvengren, 2018; Siamagka, Christodoulides, Michaelidou, & Valvi, 2015; Veldeman, Van Praet, & Mechant, 2017), and B2B social media marketing and branding (Brennan & Croft, 2012; Juntunen, Ismagilova, & Oikarinen, 2020). In addition, social media in B2B entails several unexplored issues worth investigating. Although previous studies have addressed social media adoption in a B2B context, the current body of research shows an observable dearth of producing a nuanced understanding of the factors underlying the assimilation of social media in firms’ marketing functions and how they improve their marketing performance. The assimilation phase occurs after the initial implementation of the innovation. It involves the ongoing integration and use of innovation in the firm’s day-to-day operations. In the context of social media marketing, this phase focuses on leveraging social media platforms to support the attainment of the marketing strategy related to product development, pricing decision-making, channel management, and promotion activities, and on realising the anticipated benefits.

Conclusion

In many instances, technological innovations are introduced into firms with great enthusiasm, which may be occasioned by widespread initial acquisition. Nevertheless, these innovations may fail to be completely deployed in the operations of the acquiring firms. Research has shown that, unless innovations are assimilated, firms do not benefit fully from them. Social media are one innovation that B2B firms are adopting for their marketing activities. While social media adoption in B2B firms has received some research attention, a limited number of studies have focused on social media marketing assimilation in B2B firms, the conditions that enhance this assimilation, and how the assimilation contributes to firms’ performance. In this study we examined how top management participation influences the assimilation of social media in the key marketing functional areas of product development, pricing decision-making, channel management, and promotion activities. We also examined how the existing absorptive capacity of B2B firms moderates the influence of top management participation on the assimilation of social media in the key marketing functional areas. Finally, we analysed how social media marketing assimilation into these marketing functional areas improves a firm’s performance outcomes of sales and relationship development. The study’s results show that top management participation in the social media marketing assimilation processes has a strong and positive impact on the assimilation of social media for key marketing functional areas, namely product development decisions, pricing decision-making, channel management, and promotion activities. Furthermore, the findings indicate that top management participation in the assimilation of social media in the functional areas, except in the case of promotion activities, is positively moderated by the existing absorptive capacity of the firm. The results also show that social media marketing assimilation for product development has a significant impact on sales performance, while its assimilation for channel management has a significant impact on both sales performance and relationship development.

Abstract

Although artificial intelligence can contribute to decision-making processes, many industry players lag behind pioneering companies in utilizing artificial intelligence-driven technologies, which is a significant problem. Explainable artificial intelligence can be a viable solution to mitigate this problem. This paper proposes a research model to address how explainable artificial intelligence can impact decision-making processes. Using an experimental design, empirical data is collected to test the research model. This paper is one of the pioneer papers providing empirical evidence about the impact of explainable artificial intelligence on supply chain decision-making processes. We propose a serial mediation path, which includes transparency and agile decision-making. Findings reveal that explainable artificial intelligence enhances transparency, thereby significantly contributing to agile decision-making for improving cyber resilience during supply chain cyberattacks. Moreover, we conduct a post hoc analysis using text analysis to explore the themes present in tweets discussing explainable artificial intelligence in decision support systems. The results indicate a predominantly positive attitude towards explainable artificial intelligence within these systems. Furthermore, the text analysis reveals two main themes that emphasize the importance of transparency, explainability, and interpretability in explainable artificial intelligence.

Introduction

Automotive industry leaders, such as Tesla, have made substantial investments in artificial intelligence (AI) to expedite the introduction of self-driving vehicles to the market, enhancing their competitive capabilities. The integration of AI in supply chain operations has played a crucial role in enabling Tesla to optimize its operational costs [64] while simultaneously facilitating the establishment of a Gigafactory in China [78]. We are witnessing a rapid digital transformation driven by the integration of AI in supply chain management. The COVID-19 pandemic forced companies and organizations to expedite the digitalization of their operations [6]. To enhance their competitive edge, prominent companies, including Amazon, Walmart, Alibaba, Siemens, and Toyota have embraced AI-based technologies to automate and digitalize their operations and supply chain activities [1,32]. Digital transformation also introduces new possibilities for potential cyberattacks. However, using AI-based technologies for decision-making during cyberattacks (e.g., American Express monitoring; [75]) offers significant advantages that outweigh the potential losses incurred.

Although AI-based technologies can contribute to decision-making processes in operations and supply chain management, many industry players are lagging behind pioneering companies in utilizing AI-driven technologies, which is a main problem. Therefore, the adoption of AI-based technologies in decision-making processes, particularly during sensitive situations such as cyberattacks, may encounter potential barriers that delay their usage. Due to its vital advantages, AI has received much attention from decision-makers to address resilient case studies [45] and other sensitive problems such as healthcare [69]. The lack of explanations of the underlying AI processes leads to the rejection of AI in decision support systems [60]. Leveraging AI-powered decision-making platforms can significantly facilitate and expedite the decision-making process, which results in improved overall performance. For instance, the Colonial Pipeline, a U.S. oil supplier, faced a cyberattack and, after a week of deliberation, opted to pay around $4.4 million to solve the issue [21]. A quick decision on the first day through agile decision-making could have saved them money and enabled the uninterrupted continuation of their operations with stockholders.

Results

We employed regression analysis to test the hypothesized relationship between research model variables. We conducted required tests relating to the experimental design along with method assumptions and bias checks.

4.1. Design check
To check the design, the scenario test, the realism test, the validity test, and the manipulation test were performed. We conducted the scenario check to measure the participants’ learning process regarding the provided information. The results showed that participants’ learning was strong enough to support the scenario design (in the first scenario, the Mean = 5.3 out of 7, with a Standard Deviation = 0.99, and in the second scenario, the Mean = 5.5 out of 7, with a Standard Deviation = 0.85). The realism check was conducted through a face validity process and tested using the quantitative approach proposed by Thomas et al. [65]. Participants supported the realism check by agreeing that the scenarios were close enough to reality (Mean = 4.08 out of 5; Standard Deviation = 0.44). Similarly, the validity check supported the scenario design (Mean = 4.07 out of 5; Standard Deviation = 0.43). The manipulation test supported the manipulation by showing a significant difference between the two manipulated levels (p-value <0.001, the first level, the low level of XAI: Mean = 2.68 out of 7; Standard Deviation = 0.12; and the second level, the high level of XAI: Mean = 5.9 out of 7; Standard Deviation = 0.10).

Abstract

Using a unique dataset comprising 6313 firm-year observations for Chinese listed firms between 2008 and 2017, we investigate the impact of CEO social capital on cost of debt. Our results show that CEO social capital is negatively related to cost of debt, and the impact of CEO social capital in environments with a low degree of marketization or social trust is more pronounced than in environments with a high degree of marketization or social trust. Moreover, our results reveal that two potential mechanisms, discretionary accruals and information disclosure quality, mediate the impact of CEO social capital on cost of debt.

Introduction

Research into social capital in the area of social economics emerged late last century (e.g., Coleman, 1988 ; Putnam, 1993 , Putnam, 1995 ; Knack and Keefer, 1997 ; Uzzi, 1997 ; Portes, 1998 ; Woolcock, 1998 ; Dasgupta, 2005 ; Fafchamps, 2006 ). Prior studies have acknowledged the significance of the personal social capital embedded in social networks for corporate commercial activities, including corporate financing ( Engelberg et al., 2012 ; Ferris et al., 2017 ; Huang and Shang, 2019 ; Jin et al., 2019 ), corporate investment ( Faleye et al., 2014 ; Fracassi, 2017 ), mergers and acquisitions ( Fracassi and Tate, 2012 ; El-Khatib et al., 2015 ), executive employment and compensation policies ( Hwang and Kim, 2009 ; Fracassi and Tate, 2012 ; Nguyen, 2012 ; Engelberg et al., 2013 ; Ferris et al., 2020 ), risk-taking and accounting practices ( Ferris et al., 2017 , Ferris et al., 2019 ; Panta, 2020 ), and litigation risk ( Zhang et al., 2023 ). Personal social capital embedded in social networks, especially the social capital of corporate executives, has a significant influence on corporate financing. Social capital enhances the trust between borrowers and lenders and increases risk-taking behavior as social capital is seen as an informal insurance. As invisible social credit and a conduit for the exchange of information, CEO social capital benefits corporate financing activities.

We are motivated to investigate the connect between CEO social capital and cost of debt due to the complex institutional background of network and the importance of debt financing in the context of China. Despite China’s remarkable economic growth in recent decades, its financial and legal environments remain underdeveloped. Allen et al. (2005) suggest that in order to sustain this growth, China requires alternative governance mechanisms and informal institutional arrangements. In the Chinese context, China is characterized as a relationship-based society, where social capital embedded in social networks, known as “Guan Xi,” holds significant prevalence and complexity. These networks provide Chinese additional social resources such as information and status ( Song et al., 2012 ; Bian, 2019 ). For example, the CEOs and chairmen of Chinese non-state-owned firms use bank loans to raise funds through their political contacts ( Yu and Pan, 2008 ). Loan approvals and covenants for Chinese corporates are influenced by CEO social capital ( Shen et al., 2009 ; Xu and Li, 2016 ). CEO social capital is negatively associated with firm leverage and short-term debt ratio, and social capital lowers the need for corporate bank borrowing ( Huang and Shang, 2019 ), and the social capital benefits improving the quality of corporate loans ( Jin et al., 2019 ). These studies only reveal the impact of managerial social capital on corporate financing, while the research into the impact of CEO social capital on the cost of debt is relatively scarce, particularly in emerging markets such as China.

Conclusion

This study investigates the relationship between CEO’s social capital and cost of debt. We posit that CEO’s social capital offers incentives for CEOs to provide enhanced information transparency and quality, which lowers the monitoring costs for the borrowers, inducing reduced cost of debt. Additionally, CEO’s social capital creates a sense of trust between the lenders and the borrowers due to the informal tie that social capital creates among the networks. Using a unique dataset consisting with 6313 firm-year observations from the firms listed on the Shanghai and Shenzhen Stock Exchange markets from 2008 to 2017, our results suggest that CEO’s social capital reduces cost of debt in the context of China, and such effect is more pronounced when there is a lack of information verification from the regulators (i.e., marketization) and societal norm (i.e., social trust). Moreover, we find that CEO’s social capital reduces cost of debt through the path of reduced discretionary accruals and information disclosure quality. Our results hold after a battery of robustness checks, including the three individual proxies (betweenness centrality, closeness centrality and degree centrality) for CEO social capital, the two alternative proxies for cost of debt, sample without political capital, excess social capital, the consideration of family business characteristics, the interactive effect of state-owned enterprises and endogeneity concerns.

Abstract

Previous research has explored the impact of telework on homes and offices separately, but it is necessary to examine these domains together, as energy savings in one may be offset by increased consumption in the other. Therefore, the present study aims to fill this gap by quantifying the impact of telework on the energy use of homes and offices simultaneously. Using a medium office building reference model and four home models, the present study simulates telework scenarios from 0 % teleworking to 100 % teleworking in 20 % increments in six different Canadian climate zones in EnergyPlus. The results show homes and offices with technologies that adapt to occupancy levels (adaptable) consume less energy and produce less emissions compared to inadaptable ones. However, energy use associated with homes increases slightly due to longer hours of occupancy. Emissions associated with telework depend on the impact of telework on internal heat gains, climate zones, and sources of energy (emission factors). The results demonstrate that the increase in energy consumption associated with various teleworking scenarios ranges from approximately 0.6 %–6.1 %. Similarly, the increase in emissions varies, ranging from nearly 0.5 % to 7.6 %. The present study is the first comprehensive study that considers the home type and size and other statistical data for different Canadian climate zones. The results have major implications for employers and policymakers aiming to adopt telework as a sustainable practice. The results of this study create the foundation for future comprehensive studies on teleworker behavior, transportation, and the internet use associated with telework.

Introduction

1.1. Teleworking and occupancy
Partial occupancy in offices has become a more common global phenomenon after the COVID-19 pandemic, as many employers started offering flexible schedules with telework options [[1], [2], [3], [4]]. Teleworking has different definitions but it can be generally defined as an umbrella term referring to working outside of a traditional office by using the internet [5]. Telework is often considered a sustainable alternative to traditional work arrangements as it can potentially reduce commute or the need for office space [4]. However, recent studies have shown that the potential sustainable benefits of teleworking significantly rely on teleworkers’ behaviors and decisions for four domains of homes, offices, transportation, and the internet [4,6,7]. Among all the four domains, the relationship between homes and offices has been less studied – especially quantitatively. Thus, the literature is inconclusive about the impact of telework on homes and offices concurrently.

Offices and homes are not typically designed to adapt to partial occupancy, meaning building systems such as HVAC systems are designed for full capacity. For instance, actual occupancy pre-COVID was usually between 50 % and 70 % in office buildings while office buildings are assumed to be operating under near-full occupancy [8,9]. Consequently, studies have shown building systems normally operate at their full capacity without considering occupancy [10,11]. The disproportionate decreases in the energy use of office buildings compared to occupancy during the COVID-19 pandemic are evidence of this phenomenon [[12], [13], [14], [15], [16], [17]]. Meanwhile, home energy use may increase significantly as a result of the use of more comfortable temperature setpoints or even the purchase of new HVAC equipment, like air conditioning. In part-time teleworking scenarios, such equipment may be operated every day, regardless of occupancy [6,18,19]. Therefore, it is important to have buildings that can adapt to partial occupancy [20]. As a result, there is an urgent need to quantify the impact of telework and partial occupancy on two main building types, homes and offices, under different teleworking scenarios.

Results

In this section, energy use and emissions associated with offices, homes, and their integrated net are described in Sections 3.1 Offices, 3.2 Homes, 3.3 Combined emissions of homes and offices. The results presented in the Offices and Homes sections are applicable to their respective domains.

3.1. Offices
3.1.1. Internal gains and inadaptable and adaptable offices
Fig. 12 shows the internal heat gain in W for adaptable and inadaptable offices in six different climate zones. While previous studies pointed to the impact of internal gains [22], the present study quantifies the impact. Fig. 12 shows reduced heat gains from people, equipment, lights, hot water equipment, gas equipment, and other equipment. The reduction in heat gains causes more demand for heating in buildings.

Abstract

Fintech revolutionized the traditional banking business models in emerging countries. The effect of fintech on banks’ operating efficiency and risk-taking behavior is still inconclusive. The study is aimed at exploring the effect of fintech products on banks’ operating efficiency and risk-taking behavior. The study used a quantitative research approach by collecting secondary data from annual reports of 50 commercial banks from emerging countries, namely, China, India, Pakistan, and Bangladesh, for the period 2014 to 2021. The study used panel data for path analysis and structural equation modeling (SEM) to test the theoretical mediation model by using STATA. The results show that the fintech product reduces the bank’s risk-taking behavior by enhancing the bank’s operating efficiency. The path analysis results show that operating efficiency mediates the relationship between fintech products and bank risk-taking behavior in emerging countries. The paper offers useful recommendations for central bank and commercial bank policymakers. The study is also beneficial for commercial banks that use fintech solutions to increase operational effectiveness and reduce risk. The study is the first empirical investigation into the connection between the growth of fintech products, bank operational effectiveness, and risk-taking behavior in developing nations.

Introduction

The global financial industry has been rapidly changing with the advancement of financial technology over the last few years. New emerging technologies such as Internet finance, blockchain, and mobile banking are penetrating, forming the fintech that revolutionized the whole financial industry. It offered lower-cost services with more choices for consumers to buy financial services that shift their financial behavior to digitalization. The rise of financial technology (fintech) has brought about significant changes in the financial industry, including the way banks manage risk. Fintech has introduced new tools and technologies that enable banks to more accurately assess and manage risk, as well as new business models that challenge traditional approaches to risk-taking.

The pace of fintech development in developed and emerging economies countries is different. In developed countries such as the UK, USA, and Europe, fintech extended the financing transaction methods through algorithmic trading, helping the manager to build better compliance systems to manage risk, and it also improves the electronic payment systems for both domestic and cross-border payments [1]. However, in developing countries, fintech adoption has been slow despite having growth and expansion opportunities in the digital economy (Zhang et al., 2020). Fintech has expanded financial services digitally across countries, particularly during the COVID-19 pandemic. During the pandemic, the usage of fintech services and products has increased tremendously as identified in research [2].

Conclusion and Recommendations/Implications of the Study

The evolution of digital technology has changed the world drastically. The COVID-19 pandemic changed the whole business structure. The customers’ increasing demand for fintech products increases the competition in the financial industry. To retain their customers and remain competitive in the industry, traditional banks adopted fintech products. So that they can deliver efficient, accessible, and low-cost financial services to their customers with the help of technology. Fintech has facilitated the banking industry with better user experience and lower costs by using technological advancement.

The primary objective of the study is to explore the relationship between fintech products and bank risk-taking behavior with mediating role of a bank’s operating efficiency using a sample of commercial banks from China, India, Pakistan, and Bangladesh. The empirical evidence shows that the bank’s risk-taking behavior decreases, and bank stability is improved with the development of fintech products. Fintech products improve the bank’s operating efficiency. The effect of fintech products on bank risk-taking is more pronounced in countries such as China and India because they are extensively using fintech products in their operations. Banks are adopting fintech products such as artificial intelligence blockchain, mobile banking, and Internet banking. The results show that the banks adopting fintech products in their initial phase increase the bank risk-taking behavior, and then later on, it decreases the bank risktaking behavior by improving their operating efficiency.

Abstract

     Technology is pivotal in the rapid growth of services and intensifying the quality of life. Recent technology, like the Internet of Things (IoT), demonstrates an impressive performance in fast-forward development. Intrusion Detection System (IDS) is used as a lifeline to prevent attacks by classifying the activities as normal and suspicious. In this paper, we propose a two-phase IDS for IoT. In the first phase, we categorize data into four sections according to the data types (i.e., nominal, integer, binary, and float). We then classify them using different versions of the Naive Bayes classifier. After that, we use majority voting to choose the final result of the classification. In the second phase, we pass those data which behave normally or are benign in the first phase and classify them using an unsupervised elliptic envelope. We validated our work using the standard NSL-KDD, UNSW_NB15, and CIC-IDS2017 datasets. We found the proposed method more efficient than existing IDS techniques and achieved reasonable accuracy in the first phase. Furthermore, the benign data is sent to the second phase of the analysis. After the second phase, we achieved a 97% accuracy in the NSL-KDD dataset, 86.9% in the UNSW_NB15 dataset, and 98.59% accuracy in the CIC-IDS2017 dataset.

Introduction

     In the evolutionary era, the Internet has always been performing a most significant role. Globally, the total estimate of Internet users is projected to increase from 3.9 billion in 2018 to 5.3 billion by 2023, as stated by Cisco Annual Internet Report [1]. Furthermore, the Internet of Things (IoT) is becoming increasingly widespread. IoT integrates many heterogeneous objects (such as in a smart home: intelligent bulbs, refrigerators, fans, air conditioners, automated doors, and TVs.) with various connecting technologies such as Bluetooth Low Energy (BLE), WiFi, and ZigBee. There are also other domains and applications in which the IoT can play an important role and enhance our lives quality. These applications include smart transportation, industrial automation, agriculture, and healthcare [2].

     The IoT model [3] has been emerging towards formulating a cyber–physical environment where everything can be found, operated, investigated, and modernized. Because of being connected, the chances of attacks on the network increase. Many attacks and malicious incidents can affect different layers of the IoT architecture, creating security concerns. Makhdoom et al. [4] discussed the commonly known attacks on different layers, depending on the anatomy of the malware, and IoT-enabled cyber-attacks are also illustrated in a survey [5]. Similarly, Zarpel et al. [6] elaborated on intrusion detection systems in IoT. They have classified IDS based on placement strategies, detection methods, security threats, and validation strategies. Zargar et al. [7] explained in detail about Distributed Denial of Service (DDoS) attacks and also classified the countermeasures.

Conclusion

     This paper presents a machine learning-based two-phase IDS. Firstly, we categorize data into four sections according to the data types (e.g., nominal, integer, binary, and float). Then classify them using different versions of the Naive Bayes classifier. After that, with the help of majority voting, we choose the final result of the classification. In the second phase, we pass those data which behave like normal in the first stage, and these data are classified using an unsupervised elliptic envelope. It draws an imaginary envelope and assigns value 1, which lies inside the Envelope, and −1 outside the Envelope. Our proposed model is also performing very well in the imbalanced distribution of the data by providing the weight initialization to each class. Finally, we got an overall 97% accuracy with a meager false positive rate. The drawback of this model is that it does not work pretty in multiclass classification.

     In future work, we will improve the multiclass classification and feature engineering techniques model, expand this procedure in real-time for network traffic analysis, and evaluate performance. We will further attempt to capture network data by deploying IoT devices in the real world. Moreover, the IDS system will detect malicious incidents in real-time and immediately take appropriate action to prevent damage.

Abstract

This study examines the impact of administrative management and information technology on e-government success with the mediation role of knowledge management practices. For this purpose, data was gathered from 163 public sector participants in the Al-Jouf region of Saudi Arabia. This study uses partial least squares structural equation modeling (PLS-SEM) to test and validate the research model and proposed hypotheses. The findings show that information technology and administrative management significantly increase e-government success and knowledge management practices. Moreover, the findings revealed that knowledge management practices play a vital mediating role between information technology and administrative management to make e-government success more successful and to reduce risk. Furthermore, this study strengthens the theoretical and practical implications of e-government by developing a link with socio-technical systems theory as well as with Fayol’s administrative management theory. In the end, the study recommends that policymakers and managers take serious steps to attain advantages in the application of technologies in e-government to fulfill the renewable and changing needs of beneficiaries.

Introduction

Technology has invaded all fields of life, and the importance of e-government has been increasing worldwide, not only for citizens’ ease of use but also for transparency and to eliminate corruption (Máchová et al., Citation2018; Sadik-Zada et al., Citation2022). E-government electronically provides enhancements to different stakeholders such as employees, citizens, local governments, and businesses (Chohan & Hu, Citation2022; Ciesielska et al., Citation2022). However, the actual implementation of e-governance is an extraordinary task. The administration faces multiple challenges, such as psychological resistance from citizens, the country’s digital infrastructure (websites, apps, etc.), and the legal substructure. As such, governments cannot resort to e-governance without giving due regard to challenges and objections from specific stakeholders (Bamufleh et al., Citation2021; Turban et al., Citation2021).

Successful implementation of e-governments in different countries has necessitated that administrations become online savvy. Policymakers, society, and researchers are analyzing this dramatic change (traditional to digital) in government service more closely, trying to find more appropriate implementation within the limitations of the country’s resources. Therefore, successful implementation depends on aspects such as administrative management, the use of information technology, and knowledge management practices (Abu-Shanab & Shehabat, Citation2018; Sulistiawaty et al., Citation2021).

Conclusion

In Saudi Arabia, Vision 2030 brought novelty to the production sector and also compelled the service sectors to convert from traditional to digital. Hence, e-government implications have become a strategic enabler for public management restructuring and expansion of the local economy. Many researchers agree that the e-government implementation projects have failed to attain their aims and deliver anticipated benefits. However, some factors can make e-government successful. Therefore, this study focuses on the influence of administrative management and information technology on e-government success with the mediation role of knowledge management practices in the public sectors of the Al-Jouf region of Saudi Arabia. Moreover, this research aimed to analyze the elements that influence e-government success in terms of knowledge management practices. Seven hypotheses were supported by the data. The findings disclosed that information technology and administrative management benefit e-government success and knowledge management practices. Furthermore, the findings of this study revealed that knowledge management practices play a vital mediating role between information technology and administrative management and e-government success to make it more successful and reduce the rate of risk. Therefore, this study strengthens the theoretical and practical implications of e-government by developing a link between socio-technical systems theory and Fayol’s administrative management theory. E-government can be utilized as a tool for implementing Fayol’s principles in practice, for example, by providing citizens with easy access to government services, increasing transparency and accountability, and enhancing the efficiency and effectiveness of government operations.

Abstract

     A fundamental expectation of the stakeholders from the Industrial Internet of Things (IIoT) is its trustworthiness and sustainability to avoid the loss of human lives in performing a critical task. A trustworthy IIoT-enabled network encompasses fundamental security characteristics, such as trust, privacy, security, reliability, resilience, and safety. The traditional security mechanisms and procedures are insufficient to protect these networks owing to protocol differences, limited update options, and older adaptations of the security mechanisms. As a result, these networks require novel approaches to increase trust-level and enhance security and privacy mechanisms. Therefore, in this article, we propose a novel approach to improve the trustworthiness of IIoT-enabled networks. We propose an accurate and reliable supervisory control and data acquisition (SCADA) network-based cyberattack detection in these networks. The proposed scheme combines the deep-learning-based pyramidal recurrent units (PRU) and decision tree (DT) with SCADA-based IIoT networks. We also use an ensemble-learning method to detect cyberattacks in SCADA-based IIoT networks. The nonlinear learning ability of PRU and the ensemble DT address the sensitivity of irrelevant features, allowing high detection rates. The proposed scheme is evaluated on 15 datasets generated from SCADA-based networks. The experimental results show that the proposed scheme outperforms traditional methods and machine learning-based detection approaches. The proposed scheme improves the security and associated measure of trustworthiness in IIoT-enabled networks.

Introduction

     THE Industrial Internet of Things (IIoT) is a pervasive network that connects a diverse set of smart appliances in the industrial environment to deliver various intelligent services. In IIoT networks, a significant amount of industrial control systems (ICSs) premised on supervisory control and data acquisition (SCADA) are linked to the corporate network through the Internet [1]. Typically, these SCADA-based IIoT networks consist of a large number of field devices [2], for instance, intelligent electronic devices, sensors, and actuators, connected to an enterprise network via heterogeneous communications [3]. This integration provides the industrial networks and systems with supervision and a lot of flexibility and agility [2]–[4], resulting in greater production and resource efficiency. On the other hand, this integration exposes SCADA-based IIoT networks to serious security threats and vulnerabilities, posing a significant danger to these networks and the trustworthiness of the systems [5]. The trustworthiness of an IIoT-enabled system ensures that it performs as expected while meeting a variety of security requirements, including trust, security, safety, reliability, resilience, and privacy [6]–[8]. Fig. 1 depicts the fundamental aspects of trustworthiness in an IIoT-enabled network. The basic goal of the IIoT-enabled system is to increase trustworthiness by safeguarding identities, data, and services, and therefore to secure SCADA-based IIoT networks from cybercriminals [8], [9].

Conclusion

     The ability to protect SCADA-based IIoT networks against cyberattacks increases their trustworthiness. The existing security methods along with machine learning algorithms were inefficient and inaccurate for protecting IIoT networks. In this article, we proposed a cyberattacks detection mechanism using enhanced deep and ensemble learning in a SCADA-based IIoT network. The proposed mechanism is reliable and accurate because an ensemble detection model was built using a combination of the PRU and the DT. The proposed method was evaluated across 15 datasets generated from a SCADA-based network, and a considerable increase in terms of classification accuracy was obtained. Compared to state-of-the-art techniques, the obtained outcomes of our method exhibited a good balance between reliability, trustworthiness, classification accuracy, and model complexity, resulting in improved performance.

     In the future, we will employ more powerful deep learning models to further improve trustworthiness by detecting cyberattacks accurately. In addition, we will try to formulate and assess its performance in real-world scenarios. Also, we will work on the selection of optimal features in scenarios when the features are not sufficient.

Abstract

     Taking the digital marketing activities carried out by female-led microenterprises as its jumping-off point, this paper aims to accomplish two main goals: firstly, to analyze the effects of social media marketing (SMM) actions on social performance (SP) and, secondly, to determine whether the qualities of female microentrepreneurs have an impact on the SMM-SP relationship. Entrepreneurship affords many female a solid work-life balance. With scarce financial resources, they often turn to creative social media strategies to secure financial sustainability beyond the short term, as well as to enhance their medium- and long-term social performance. Our regression and moderation analysis of a sample of 127 microenterprises, the data for which was collected by online surveys administered to female microentrepreneurs, reveals SMM actions’ direct effect on social performance. Our findings also show that female microentrepreneurs’ human capital directly influences SP and moderates the effects of marketing strategies on SP. The paper ends by presenting relevant conclusions for both academics and marketing practitioners.

Introduction

     The 18.5 % drop in GDP during the second quarter of 2020 foregrounded entrepreneurship as an appealing alternative for people to improve their personal economy. Data provided by the Global Entrepreneurship Monitor (GEM) for 2020 underscore three key traits of this activity in Spain:

(1) The entrepreneurship rate in Spain was lower than in the rest of Europe. Whereas 10 % of citizens in the world’s most developed countries and 7.7 % of those in the EU-28 were involved in an entrepreneurial initiative, this figure stood at 6.4 % for Spain.

(2) Entrepreneurship has a high self-employment component. In Spain, the self-employment rate among entrepreneurs was 19 %, slightly higher than the EU-28 average of 17 %.

(3) Female entrepreneurship in Spain is growing steadily. While for the 2009–2010 period, the female TEA (Total early-stage Entrepreneurial Activity) was 3 %, during the 2019–2020 period it reached 6 %. Currently in Spain, eight women start businesses for every 10 men, thus exceeding the European average, where only six women are counted for every ten male entrepreneurs.

     Although these figures seem to suggest a narrowing gender gap, an entirely different picture comes into focus when we apply a socioeconomic perspective. Most notably, there is a severe gap in the proportion of consolidated businesses led by men (60 %) versus those led by women (40 %). That being said, men and women seem to be on equal footing when it comes to people hoping to create a business in the next three years (50.9 % of which are men and 49.1 %, women). The numbers even turn in women’s favor with respect to business abandonment, where men account for 52.6 % of such cases.

Conclusions

     The results obtained through our data analysis show that, among digital marketing strategies, those carried out on social media influence the performance of microenterprises, especially their social performance. In the current context, 2.0 technologies guarantee interactivity and quality between companies and consumers. Despite the multiple options available to companies to establish and consolidate relationships, social media appear to be the most efficient from a marketing standpoint, as people have integrated technology into their daily lives.

     The social use of such media has expanded and the number of people seeking emotional and hedonic benefits on them is increasing. People today turn to social media to stay in touch with friends and family, to partake in leisure activities or to give free rein to their creativity. For some, social media have even become a way to discover and engage in new experiences, a boundaryless place where they are free to be themselves. Social media offer users speed, convenience and, above all, security and anonymity, all qualities that encourage participation. Moreover, they greatly extend the reach of messages and content, thereby facilitating immediate and global dissemination. In short, it is easy to understand the reason for their success and why companies, aware of this, have sought to carry out SMM actions as a way to establish relationships with their customers.

Abstract

     Digital marketing has been on the epicentre of many researches. However, there is the need to make clear which are the current trends of the Digital Marketing Strategies regarding luxurious hotels. Unfortunately, the COVID-19 pandemic has seriously “damaged” the hospitality industry and it has affected the digital marketing strategies of hotels. The aim of this paper is the evaluation of the most recent applications of digital marketing and especially the social media used by luxurious hotels and their guests (such as Facebook, Trip-advisor). The outcome of the research indicates that social media play a key role on the marketing of luxurious hotels. More specifically, a hotel should take into serious consideration the reviews made on social media by customers. A review can leverage a hotel but also it can doom its reputation. For this reason, the practitioners must give special attention on the reviews that the guests post on social media. The methodology of the paper is literature review. Hence, it will rely on publications. The authors have collected publications from various academic databases, such as SCOPUS, by using the appropriate keywords. The publications were selected based on how relative are with the purpose of this paper. To sum up, the hotel industry has to face a major challenge due to COVID-19 crisis. This crisis, which is an ongoing phenomenon, is expected to change the way that hotels are copying with. One of the changes that the hotel should take into consideration is which strategy will use on social media. This paper has relied on existing theories and speculations that have been made by several authors and they have not been cross-checked with primary researches. Therefore, there is the need for more primary data coming from upcoming publications.

Introduction

     The tourist industry is important for the economy of many countries, such as Greece [1], [2], [3], [4]. An important insight of tourism is the fact that during the past years it had developed a number of different approaches as far as hospitality is concerned as well as it had developed different models of tourist development [5]. At this point, there are surveys about which tourist development model can be the most effective and it can bring wealth and sustainability for a tourist destination. For example, mass tourism has been heavily criticized for its consequences on the destinations [6], through [7] have argued that luxury-based tourism cannot generate wealth for the sum of the economy of a destination, like mass tourism can do, hence it is important for a destination to try a combination of tourist models. Nonetheless, the majority of the tourist industry experts as well as the academics conclude that luxury tourism can bring a number of benefits for a destination and also can contribute to its sustainability [8].

Conclusions

     The paper has examined the latest trends on digital marketing. As it is indicated by various researches [1], [77], [78], [75] the use of digital marketing can bring a number of benefits for a luxury hotel [79], [80]. Nonetheless, it is important to remark the fact that digital marketing, as it happens today mostly by social media, may hide a number of threats which can create a negative image of the hotel. Most of the threats derive by the negative comments on social media and the inability of the hotel’s management to convert the negative comments. Based on the findings of the above mentioned researches this is the most critical point for the digital campaign of a hotel. For this reason, there is a need for further future research which will help the academia and the practitioners to better understand the case of digital marketing and especially of the reviews made. Especially the current situation with the COVID-19 pandemic, means that digital marketing may play a crucial role for the recovery of the tourist industry [81], [82], [83]. For this reason, future research should direct to the use of social media and overall of digital marketing, in order to recover from the pandemic of COVID-19.

Abstract

     While the strategic use of social media (SM) for enhancing firm performance has attracted much attention, little is known about it through the lens of business-to-business (B2B) small and medium-sized enterprises (SMEs). Building on the market-driven view and the dynamic capabilities view of competitive strategy, we examine SM use in a framework of market-sensing and customer-linking capabilities that influence firm performance. Our research model posits that market orientation stimulates SM use to enhance market-sensing capability thereby facilitating two customer-linking capabilities, namely customer relationship management and brand management, which collectively contribute to greater marketing performance and financial performance. Our research model is empirically tested using a survey of 143 UK B2B SMEs. The findings broadly support our theorization in which the strategic use of SM, an essential part of market-sensing capability, enhances customer-linking capabilities. Interestingly, although SM use influences brand management capability, its suggested influences on both customer relationship management capability and marketing performance occur only through the mediation of brand management capability. Both customer-linking capabilities positively influence marketing performance and in turn financial performance. Our findings provide novel conceptual and empirical advancement of how market-centric B2B SMEs strategically use SM to enhance their market-sensing and in turn customer-linking capabilities, and hence firm performance.

Introduction

     With the increased digitalization of marketing activities, marketing professionals are increasingly adopting social media (SM) as a strategic tool to effectively engage with customers and broader stakeholders (e.g. Chaker, Nowlin, Pivonka, Itani, and Agnihotri, 2022; Pardo, Pagani, and Savinien, 2022); develop new products (Du, Yalcinkaya, and Bstieler, 2016); increase brand awareness (Deng, Wang, Rod, and Ji, 2021); co-create value (Itani, Krush, Agnihotri, and Trainor, 2020); and improve firm performance (Cheng, Liu, Qi, and Wan, 2021). More and more researchers suggest that firms must have an SM presence since the strategic use of SM is increasingly seen to enhance firm competitiveness (e.g. Cartwright, Liu, and Raddats, 2021; Tiwary, Kumar, Sarraf, Kumar, and Rana, 2021).

     The likely gains from SM technologies are particularly evident in business markets where customers are more informed and less dependent on interactions with salespeople (Ancillai, Terho, Cardinali, and Pascucci, 2019). For example, leading large firms such as Adobe, IBM, and Maersk Line are increasingly using digital channels and SM to facilitate effective dialogue with customers (Ancillai et al., 2019). The growing evidence suggests that SM can be strategically used to facilitate an effective dialogue with customers (Kovac, 2016), create brand awareness, or differentiate the focal brand from the competition (e.g. Deng et al., 2021; Swani, Brown, and Mudambi, 2020), provide customer care, and/or improve firm performance, among others (e.g. Salo, 2017; Tiwary et al., 2021).

Conclusion

     Overall, our study examined the much-advocated and under-investigated strategic use of SM for enhanced B2B marketing strategy. We opted to focus on the B2B SME context where it argued that due to the resource-constrained operating environment experienced by SMEs, the use of SM platforms will be particularly beneficial due to easiness of implementation, low-cost accessibility, and cost-efficiency. We promised our inquiry on the dynamic capabilities view and Day’s (1994) seminal framework of capabilities in market-driven firms and theorized that the strategic use of SM provides a meaningful link between market orientation and CLC. Overall, our research model captures how the strategic use of SM strengthens MSC to effectively seize market opportunities through (re)developing two key CLC that drive enhanced marketing performance and in turn financial performance. Overall, the strategic use of SM remains a fertile ground for further inquiry in B2B marketing strategy context.

Abstract

     Mental health research through data-driven methods has been hindered by a lack of standard typology and scarcity of adequate data. In this study, we leverage the clinical articulation of depression to build a typology for social media texts for detecting the severity of depression. It emulates the standard clinical assessment procedure Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and Patient Health Questionnaire (PHQ-9) to encompass subtle indications of depressive disorders from tweets. Along with the typology, we present a new dataset of 40191 tweets labeled by expert annotators. Each tweet is labeled as ‘non-depressed’ or ‘depressed’. Moreover, three severity levels are considered for ‘depressed’ tweets: (1) mild, (2) moderate, and (3) severe. An associated confidence score is provided with each label to validate the quality of annotation. We examine the quality of the dataset via representing summary statistics while setting strong baseline results using attention-based models like BERT and DistilBERT. Finally, we extensively address the limitations of the study to provide directions for further research.

Introduction

     Analyzing the presence of mood and psychological disorders through behavioral and linguistic cues from social media data remains a critical area of interdisciplinary research. In addition to these disorders, the last decade has seen exponentially increasing attempts to assess related symptomatology such as depressive disorders, self-harm, and severity of mental illness using non-clinical data (Bucci et al., 2019). Social media platforms and other online discussion forums have been particularly appealing to the research community for various research purposes (e.g., population-level mental health monitoring (Conway & O’Connor, 2016), personal traits detection (Marouf et al., 2020), cyberbullying spotting (Bozyiğit et al., 2021), etc.) because of the massive scale of data. This massive data flow has resulted from increasing rates of internet access and people spontaneously sharing their suffering, pain, and struggle anonymously on these platforms (Ofek et al., 2015). Recognizing the early symptoms of depressive disorder through a person’s language use can prevent many disastrous outcomes like self-harm, suicide, etc., and even help deploy effective treatment in proper time. Moreover, the outbreak of the COVID-19 pandemic is likely to have devastating impacts on the mental health of millions of individuals as lockdown in the affected areas has reported high rises in the incident rates of mood disorder, including acute stress disorder, post-traumatic stress disorder, generalized anxiety disorder, and overall sub-clinical mental health deterioration (Singh et al., 2020). The scope of mental health deterioration during the COVID-19 pandemic and the comprehensive nature of diagnosing depressive disorders have provided an unprecedented need to infer the mental states of individuals from all-inclusive resources. Recent studies have revealed that valuable insights into the impact of the pandemic on population-level mental health can be inferred from posts or comments on social media (Low et al., 2020).

Conclusions and future work

     This work introduced a new typology for diagnosing depression severities from social media texts, as well as a unique dataset of labeled tweets with a confidence score for each label. The dataset was constructed based on strong ground truths and clinical validation, and it is expected to help alleviate the scarcity of mental health data to some extent. The description of the process and challenges in creating such a dataset may motivate researchers to collect similar corpora of this scale from other social media and discussion forums. The experimental results indicated that existing state-of-the-art models often fail to understand the contextual undertone of the data samples. Developing a model that is capable of comprehending the subdued relationship and differences among depression severities can result in an even better understanding of human cognition. Moreover, analysis of the classification performance indicates that there is no distinct division of keywords among different depression severities. The same keyword might be used differently to express different emotions, rather it is more important to understand the context of the tweet to diagnose the severity of depression. Broader implications of this research may include personalizing and directing preventative and awareness messages by health professionals to the users in need.