Unlocking knowledge transfer dynamics across borders: key drivers in international strategic alliances

Abstract

Purpose

In today’s swiftly evolving and intensely competitive business landscape, organisations increasingly recognise the significance of cross-border collaborative partnerships. International Strategic Alliances (ISAs) have emerged as effective platforms to foster innovation and gain a competitive advantage. Within the context of the hotel industry, which epitomises international operations, this study aims to investigate the pivotal role of knowledge transfer (KT) in the performance of ISAs.

Design/methodology/approach

The research framework draws on the influence of technological drivers (TD), organisational drivers (OD) and individual drivers (ID) on successful KT within ISAs. By analysing data from managers and owners of international hotel businesses using Confirmatory Factor Analysis (CFA), this study empirically examines the relationships between these drivers and KT dynamics.

Findings

Findings highlight the direct impact of these drivers on KT and subsequent alliance performance. However, among these drivers, factors related to TDs, such as Web 2.0, knowledge management systems and IT infrastructure, generally received the highest values.

Originality/value

This study contributes to international business and knowledge management and sheds light on the intricate interactions between the drivers of KT and ISAs. The insights derived from this study provide a foundation for enhancing strategic alliance practices in a global context. By embracing KT mechanisms, organisations can harness collaborative potential, drive innovation and achieve sustainable growth.

Citation

Rezaei, M. (2024), "Unlocking knowledge transfer dynamics across borders: key drivers in international strategic alliances", Journal of Knowledge Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JKM-12-2023-1188

1. Introduction

In today’s rapidly changing and highly competitive business environment, organisations increasingly recognise the value of collaboration and strategic partnerships in driving innovation and enhancing their competitive advantage (Inkpen, 2005). One practical approach to fostering cooperation and leveraging shared knowledge is through international strategic alliances (ISAs). These alliances bring together organisations from different countries and cultures, enabling them to pool resources, expertise and market insights to achieve mutual benefits (Chan et al., 1997). By facilitating the transfer of knowledge between alliance partners, these collaborations have the potential to spark innovation and create new business opportunities (Brouthers et al., 1995).

Strategic alliances are a type of inter-company cooperation and voluntary agreement between two or more companies with independent identities (Koza and Lewin, 1998). These alliances aim to exchange and share resources to supply products and services (Elmuti and Kathawala, 2001). In the current global economy, characterised by regional trade agreements, financial crises, global recession, the emergence of multinational companies and market liberalisation aimed at attracting foreign capital, the business landscape has become exceedingly intricate and disorganised (Michailova and Bernhard Nielsen, 2006). To establish a formidable presence in the global arena, business enterprises must adopt efficient strategies and contemplate structural models such as ISAs. Notably, the hotel industry is one of the main domains for implementing ISAs, given its inherent international nature.

A robust and thriving hotel industry is paramount for the socioeconomic development of developing countries. As an integral part of the broader tourism industry, the hotel sector is pivotal in driving economic growth, generating employment opportunities, attracting foreign investment and promoting cultural exchange and international cooperation (Gündüz et al., 2024). A strong hotel industry stimulates domestic economic activities and serves as a catalyst for foreign exchange earnings and overall economic diversification. However, this industry faces many challenges related to various issues, including the supply of raw materials, high maintenance costs and limited adoption of new technologies owing to tough competition in the international market (Nam et al., 2021). In the face of these challenges, activists in this industry, managers and owners endeavour to expand their strategic alliances beyond their traditional domestic boundaries, seeking empowered partners to strengthen and broaden their presence on the international stage.

ISAs have emerged as pivotal methods for fostering industrial development at the global level. Thus, managers and owners of hotel businesses must consider establishing ISAs. These alliances provide a unique platform for organisations to engage in collaborative endeavours by combining their strengths and capabilities to create innovative solutions and capitalise on emerging market opportunities. An essential factor for enhancing the profitability of organisations through such alliances lies in fostering the exchange and transfer of knowledge and experiences (Santoso and Wahyuni, 2018; Simonin, 2004). Knowledge transfer (KT) is crucial in facilitating the exchange of valuable insights, skills, and technologies between partnering organisations. This knowledge exchange enables mutual learning and the integration of diverse perspectives, enhancing innovation and problem-solving capabilities (Simonin, 1999b).

Moreover, KT fosters a collaborative and synergistic environment in which the strength of each partner can be leveraged for collective growth. By promoting an efficient flow of knowledge, ISAs can attain greater competitiveness, sustainable development and successful expansion into new territories (Simonin, 1999a). Consequently, nurturing effective KT mechanisms has become a cornerstone for fostering successful and enduring partnerships in the global business landscape. Therefore, this remarkable role underscores the importance of understanding and recognising the factors contributing to productive transfer processes within these alliances.

Given the importance of this issue, it is necessary to investigate the impact of influential KT factors on performance in this industry. Therefore, this exploratory study aims to examine these issues. The statistical population comprised managers and owners of international hotels. A five-point Likert scale was employed to measure the participants’ perspectives on the investigated variables. The data were analysed using Confirmatory Factor Analysis (CFA) with Lisrel software, which enabled the exploration of complex relationships between multiple variables and generated meaningful insights.

The results of this study provide valuable insights into the factors that influence KT in ISAs. The findings reveal that drivers related to new technology, organisational features and personality attributes significantly influence KT in these alliances by directly affecting performance. This finding highlights the importance of partner selection, alliance formation and ongoing management practices in transferring knowledge to achieve optimal outcomes within an alliance.

By examining the intricate dynamics of KT within ISAs, this study contributes to the existing body of knowledge on international business and knowledge management (KM). These findings have significant implications for organisations operating in a global context, offering valuable guidance to enhance their strategic alliance formation and management practices. Understanding and leveraging the determinants of successful KT can empower organisations to unlock their potential and gain a competitive edge in the dynamic global marketplace. In conclusion, this study serves as a stepping stone for further exploration of the complex relationship between KT and strategic alliances. This provides a foundation for future studies to delve deeper into the specific aspects of KT, explore additional moderating or mediating factors and examine different industry contexts. By advancing the understanding of KT within ISAs, organisations can harness the power of collaboration, enhance innovation capabilities and achieve sustainable growth in an increasingly interconnected world.

2. Literature review

2.1 Knowledge transfer

KT represents the cornerstone process through which insights, expertise and best practices permeate organisations, fostering a culture of learning and innovation (Garavelli et al., 2002). It embodies the transmission of tacit and explicit knowledge across individuals, teams and departments, facilitating the exchange of valuable insights and experiences (Nonaka and Takeuchi, 2007). KT catalyses organisational growth and adaptability, empowering organisations to leverage existing knowledge assets to navigate challenges and seize opportunities within a competitive landscape (Vrontis et al., 2017; Rezaei et al., 2024b). A robust KT enhances organisational agility, responsiveness, and resilience by equipping individuals and teams with the necessary insights and capabilities to navigate complex and rapidly changing environments (Rezaei et al., 2024b). KT promotes collaboration, innovation, and continuous improvement by enabling the seamless flow of knowledge across organisational boundaries, thereby driving enhanced performance and strategic alignment (Asrar-ul-Haq and Anwar, 2016).

On the other hand, according to Rezaei et al. (2023a, 2023b), KT involves a series of “conveyance behaviours” driven by individual, organisational, and technological factors which elucidate the strengths and weaknesses of transferring practices. Motivated individuals, propelled by a shared sense of purpose, curiosity and commitment to continuous learning, catalyse knowledge sharing (KS) within organisations (Rezaei et al., 2023a). Organisational structures, policies and incentives that prioritise open communication, knowledge exchange platforms and communities of practice create an environment conducive to collaboration and innovation (Rezaei et al., 2023a). Technological tools, including knowledge management systems (KMS) and artificial intelligence (AI), further amplify the potential for knowledge dissemination and exchange, transcending geographical and temporal barriers (Argote and Fahrenkopf, 2016). Advancements in promoting KT processes hinge on the tangible and intangible factors associated with these drivers (Popkova et al., 2021; Lin, 2013). However, challenges to KT exist alongside positive impacts, necessitating a multifaceted approach to address barriers associated with these drivers (Taskin and Bridoux, 2010). Investing in strategies that foster a culture of trust, transparency, and inclusivity enables organisations to unlock the full potential of their collective intelligence. This drives the evolution of knowledge ecosystems that cultivate creativity, resilience and sustainable growth (Rezaei et al., 2024a). Ultimately, by championing the KT process and nurturing a culture of continuous learning, organisations position themselves as agile, adaptive and forward-thinking entities poised to thrive in an ever-evolving business landscape (Rezaei et al., 2024a).

2.2 International strategic alliances

ISAs have become crucial in today’s competitive business environment, in which companies engage in value-creation activities that span part production, final assembly, technology development, research and development and marketing (Owen and Yawson, 2013). These alliances enable companies to extend their reach beyond borders by combining resources and capabilities, resulting in “value creation” beyond what a single company can achieve (Aggarwal and Kapoor, 2018; Oliveira et al., 2023). The formation of strategic alliances is necessary when dealing with complex and diverse activities that require continuous interaction and coordination (Nielsen and Gudergan, 2012).

Generally, strategic alliances involve collaboration between two or more partners, each bringing their unique organisational culture, operating styles and core competencies to the partnership (Li et al., 2013). Through this collaboration, they leveraged distinct yet complementary resources. Strategic alliances include investments, licencing agreements, supply and distribution agreements, research and development partnerships, joint production agreements, franchising and technical exchanges (Nielsen and Nielsen, 2009).

These alliances primarily aim to generate economic profit through different approaches, such as franchising, licencing, research and development partnerships, joint investments, production cooperation agreements, outsourcing and integration (López-Duarte et al., 2016). By entering ISAs, companies seek economic benefits and gain access to new knowledge and expertise. Partners in these alliances, representing different nationalities, engage in global knowledge exchanges and enhance their collective learning capabilities (Santoso and Wahyuni, 2018).

ISAs serve as a means for firms to expand their knowledge bases and tap into new markets. They provide opportunities for cross-border collaboration and facilitate the exchange of ideas, technologies and best practices (Santoso and Wahyuni, 2018). Partner companies can leverage their strengths, mitigate risks, and explore new growth avenues in foreign markets by joining forces (Bodnaruk et al., 2016). The global nature of these alliances enables partners to access diverse perspectives, market insights and innovative approaches, thus fostering continuous learning and adaptation (Kedia and Lahiri, 2007). ISA is vital in enabling companies to navigate the complexities of the global business landscape. It can empower them to combine their resources, capabilities, and knowledge by forging collaborative partnerships across borders to create value and achieve a competitive advantage (Li et al., 2013). These alliances facilitate the exchange of ideas, promote learning, and unlock opportunities for growth in new markets (Beamish and Lupton, 2016). In an era of increasing globalisation, ISAs have become indispensable for companies seeking to expand their reach, drive innovation, and secure sustainable success (Vendrell-Herrero et al., 2018).

2.3 Organisational drivers and knowledge transfer

Organisational drivers (ODs) refer to the factors and practices within an organisation that facilitate and support KT, which include leadership support, organisational culture, organisational structure collaboration mechanisms, resource allocation and KM processes (Rezaei et al., 2022c). These factors include all personal or environmental attributes inside organisations arising from managers’ policies and behaviours, leadership style and workplace atmosphere (Mahdiraji et al., 2021).

As a factor in OD, leadership support is crucial for driving KT within strategic alliances. Strong leadership commitment fosters a culture that values and prioritises KT, encourages risk-taking, and provides the necessary resources and incentives for innovation (Muhammed and Zaim, 2020). Leaders who champion KT initiatives create an environment conducive to collaborative learning and innovation (Donate et al., 2015). Moreover, organisational culture is pivotal in facilitating KT and driving innovation performance (Rezaei, 2022). A culture that encourages open communication, trust, and mutual respect among alliance partners fosters the willingness to share knowledge and collaborate on innovative endeavours (Rezaei et al., 2022a). A culture that promotes learning, experimentation and KT practices enhances innovation performance within an alliance (Jelavic and Ogilvie, 2010).

Financial and non-financial rewards also play a pivotal role in influencing KT, serving as powerful incentives for individuals and teams to share their expertise, experiences, and best practices with partner organisations (Rezaei et al., 2022a). Financial rewards such as bonuses, profit-sharing and performance-based incentives can motivate employees to participate actively in KT activities (Wang et al., 2022). According to Bartol and Srivastava (2002), when individuals perceive a direct link between their contributions to KT and tangible financial gains, they are more likely to engage in such endeavours.

On the other hand, non-financial rewards, such as recognition, career advancement opportunities and access to unique training programs, can also foster a culture of knowledge exchange (Rezaei et al., 2024a). The recognition of employees’ efforts to share knowledge can boost their morale and strengthen their commitment to collaborative efforts. Furthermore, offering career advancement opportunities based on knowledge contributions can encourage employees to invest time and effort into sharing their expertise (Rezaei et al., 2022a). According to some results (Wang et al., 2022; Wickramasinghe and Widyaratne, 2012), the combination of financial and non-financial rewards can create a conducive environment for KT in co-partner organisations in an alliance, fostering collaboration, innovation and mutual learning among partner organisations. Ultimately, these rewards can contribute significantly to the success of external strategic cooperation.

Organisational culture (OC) is a pivotal factor in the workplace environment that drives employees’ performance effectiveness and encompasses diverse elements, including values, beliefs, ideologies, symbols and expectations, all of which play crucial roles in facilitating and promoting KT (Lyu, and Zhang, 2017). One noteworthy aspect of OC lies in its distinctive nature within each collective structure, reflecting the unique governing identity of the community, such as an organisation (Al‐Alawi et al., 2007). OC significantly shapes individuals’ perspectives on KT and is crucial to employees’ final decisions to engage or refuse to exchange valuable information (Casimir et al., 2012). According to Rezaei et al. (2022a), describing the organisation as a ’social body’ with defined cultural and social values, such as supportive behaviour and a collaborative atmosphere, profoundly impacts employees’ connectional behaviour. In this vein, Martínez et al. (2016) emphasise that any weaknesses in OC components, such as norms, values and objectives or a perceived lack of benefits, can lead to decreased efficiency in KT.

Some authors have highlighted another critical element of OC, the learning culture, which profoundly influences the implementation of exchange processes (Joo, 2010; Rezaei et al., 2022a, 2022b, 2022c). KT becomes more achievable in organisations where members consistently embrace learning processes. By fostering continuous learning, organisations encourage employees to acquire new information, experience, and skills from others. This process, which involves exchanging specialised work concepts, educates them on the essential nature of learning as an indirect practice for KT (Mojtaba, 2022).

Additionally, the collaborative culture within an organisation holds significant importance. According to Flinchbaugh et al. (2016), a teamwork-oriented culture enhances interaction and communication and fosters employee learning and creativity, thus providing substantial advantages for KT. Moreover, Costa et al. (2018) note that a teamwork culture encompassing both organisational and social aspects promotes positive employee relationships, encourages cooperative behaviours, and ultimately enhances KT. Furthermore, organisations that embrace a culture of trial and error empower employees to engage in teamwork, promote collective behaviours, and, as a result, increase the likelihood of knowledge exchange (Zhang et al., 2017).

Organisational structure (OS) is another crucial factor in KT (Sengupta and Ray, 2017; Rezaei et al, 2022a). The way in which the participating organisations are organised and how they manage information flow can significantly impact the effectiveness of sharing and transfer processes (Rezaei et al., 2020). According to some studies (Brescia et al., 2016; Kelloway et al., 2012), a centralised and hierarchical structure hinders KT, as decision-making processes and communication channels are slow and bureaucratic. On the other hand, a decentralised and flat OS can promote quicker and more direct exchanges of knowledge, fostering collaboration and innovation. Meanwhile, the presence of cross-functional teams and open communication lines in the structure of organisations can enhance the dissemination of expertise and best practices (Harris, 2004; Rezaei et al., 2023b).

2.4 Individual drivers and knowledge transfer

Individuals are the origins of thoughts and ideas, creators of innovations, and possessors of experience and beliefs. Rezaei et al. (2020) believe that KT is a social phenomenon affected by personal characteristics, interpersonal relationships and social interactions. According to Davenport and Völpel (2001), people are sources of experience, skills, opinions, information and thoughts; therefore, exchanges are interpreted through their attributes. Accordingly, social behaviour researchers assert that personal characteristics and inner willingness explain a vast part of engaging in inter-organisational activities and the intention to share knowledge (Lee et al., 2010). Therefore, any prediction of employee willingness to participate in KT relates to their motivation and experience (Kelloway et al., 2012). These personal objectives for KT, represented by goal-based and reason-based scales, are referred to as individual drivers (ID), which can vary from one person to another and are divided into extrinsic and intrinsic (Hung et al., 2011). According to Deci and Ryan (2000), intrinsic drivers are inherent, unaffected by any external pressure or reason, and caused by enjoyment of the task or helping others. In contrast, extrinsic incentives depend on external causes and lead to desirable outcomes such as monetary rewards and career advancement (Bock et al., 2005).

Some IDs include personal interests, sense of purpose, reputation building, social connections, personal growth, and intrinsic motivation. Research has consistently demonstrated that KT IDs positively affect the innovation performance of ISAs (Grant and Baden‐Fuller, 2004; Mowery et al., 1996). Individuals with unique features and skills that enhance the quality and relevance of knowledge transferred can provide valuable insights and ideas for an alliance, leading to improved innovation outcomes (Grant and Baden-Fuller, 2004). Moreover, research highlights the importance of individual motivation in driving KT and performance (Hwang et al., 2018; Mooradian et al., 2006; Siegel and Ruh, 1973). Motivated individuals are more likely to engage in KT activities, participate actively in collaboration, and contribute to generating innovative ideas (Nguyen et al., 2019). Motivation can be driven by factors such as personal growth, recognition, career advancement and intrinsic satisfaction of contributing to the alliance’s success (Hwang et al., 2018).

Empirical evidence also suggests that trust among individuals within an alliance positively influences KT and subsequent innovation performance (Holste and Fields, 2010). Trust fosters psychological safety, encouraging individuals to share knowledge openly, take risks and collaborate effectively (Wickramasinghe and Widyaratne, 2012). When individuals trust each other, they are more willing to share and transfer knowledge, leading to improved innovation outcomes within the alliance (Gundolf et al., 2018). Some studies have indicated that social abilities play a significant role in KT and subsequent innovation performance (Bock et al., 2005; Vajjhala et al., 2016). Strong social ties, productive communication channels and collaborative relationships facilitate the exchange of knowledge, ideas and expertise (Rezaei et al., 2023a). Well-connected individuals can leverage their social networks to access diverse knowledge sources and enhance innovation performance.

The next attribute related to IDs is reciprocity, which refers to the mutual exchange of information, expertise, or resources between individuals or entities with the expectation that both parties will benefit from the interaction (Rezaei et al., 2023a). In KT, reciprocity fosters a sense of trust, cooperation and willingness to share knowledge among individuals or teams (Swärd, 2016). When individuals perceive their contributions to KT will be reciprocated, they are more likely to share their expertise and experiences, and the belief that they will receive valuable knowledge or support in return creates a positive incentive for KT (Swärd, 2016). This reciprocal relationship can be formal, through predefined agreements or contracts, or informal, where individuals help each other based on trust and camaraderie (Rezaei et al., 2023a).

2.5 Technological drivers and knowledge transfer

Technology is another critical driver of knowledge transfer, enabling and facilitating the connection and collaboration between individuals and organisations through communication tools, collaboration platforms and KMS, which make it easier for people to share information, learn from each other and work together towards common goals (Gündüz et al., 2023; Tan and Md. Noor, 2013). Technology breaks down communication barriers and enables productive collaboration (Authors et al., 2018; Soto-Acosta et al., 2018).

Technological drivers (TDs) refer to the tools, platforms and systems that facilitate KT among partners, including collaborative platforms, KMS, communication technologies, data analytics tools and virtual workspaces (Hempell and Zwick, 2005).

Studies suggest adopting collaborative platforms and KMS positively influences KT and subsequent innovation performance (Bhatt 2001b, 2001a). Collaborative platforms provide a digital space for partners to share knowledge, collaborate on projects and exchange ideas (Randhawa et al., 2017). KMSs enable the organisation, storage and retrieval of knowledge, making it easily accessible to alliance partners (Kang and Sung, 2017). Moreover, communication technologies such as video conferencing, instant messaging and virtual meeting tools enable real-time and asynchronous communication (Khalid, 2011). These technologies overcome geographical barriers and allow efficient and effective knowledge exchanges, thus enhancing innovation performance within alliances (Walden et al., 2017).

Additionally, empirical evidence indicates that using data analytics tools significantly facilitates the processes in KT, subsequently enhancing performance (Khan and Vorley, 2017; Grover and Kar, 2017). They can extract valuable insights, patterns and trends from the vast amounts of data generated within an alliance (Rezaei et al., 2022a, 2022b, 2022c). These insights can inform decision-making, identify market opportunities and drive innovation initiatives (Rezaei, 2023). Data analytics tools support evidence-based innovation by better understanding customers’ needs, market trends and competitive dynamics (Pauleen and Wang, 2017).

Virtual workspaces and technological tools, such as project management platforms and document transfer systems that support virtual collaboration and coordination, can streamline communication and coordination. They enable partners in an alliance to foster cooperation even when geographically separated and facilitate the exchange of information and knowledge (Rezaei et al., 2022a, 2022b, 2022c).

3. Methodology

3.1 Industry selection

The hotel industry holds a key position in the global economy due to its substantial contributions to employment, revenue generation, and foreign exchange earnings (Zervas et al., 2017). Tourism, of which hotels are integral, is a cornerstone of economic growth for many nations (Gündüz and Atak, 2023; Espinosa et al., 2004; Toylan et al., 2020). The industry is often considered a barometer of economic health, reflecting both “domestic and international” economic conditions (Akbar and Tracogna2018). Furthermore, the hotel sector is intricately linked to other industries, such as transportation, entertainment and local businesses, creating a complex web of interdependencies (Melián-González & Bulchand-Gidumal, 2020).

On the other hand, the dynamic nature of international travel and the increasing interconnectedness of economies have caused this industry to be basically recognised with a global scope, which consequently highlights the importance of ISAs in this sector (Akbar and Tracogna, 2018).

ISAs in the hotel industry often originate from shared goals and the recognition of complementary resources (Brouthers et al., 2015). For example, hotel chains may form alliances with local boutique hotels to tap into unique offerings and regional expertise, establishing a symbiotic relationship that benefits both parties. Moreover, collaborative marketing initiatives represent a common avenue for international alliances in the hotel sector (Brouthers et al., 1995). By pooling marketing resources, partners can amplify their reach, engage diverse customer segments and enhance brand visibility, ultimately driving mutual business growth (Ferrary, 2015).

Furthermore, as technology is increasingly pivotal in the hospitality sector, alliances may centre around technological integration and innovation (Brouthers et al., 1995). Hotels may form partnerships with tech companies to implement cutting-edge solutions, enhancing guest experiences and operational efficiency (Chen et al., 2017). Finally, risk mitigation emerges as a critical driver for forming alliances in the volatile realm of international business (Choi et al., 2022). Hotels may join forces to share investments in infrastructure development, entering new markets with a reduced financial burden and enhanced resilience against market uncertainties (Tjemkes et al., 2017).

3.2 Sample size determination

The target population consisted of hotel owners and managers at various hierarchical levels, all with current or past involvement in ISAs. This population was specifically selected based on their unique positions to provide critical knowledge and insights that directly align with the study’s research objectives. By focusing on this group, the study aimed to capture informed perspectives on the strategic challenges and outcomes associated with ISAs, which are central to the research questions.

A rigorous and comprehensive screening process was implemented to identify hotels with ISA experience accurately. Multiple data sources were utilised, including hotel websites, local listings, websites of prominent international hotel chains, travel agencies, consultants, and government and tourism portals. This multifaceted screening approach was necessary to ensure the inclusion of a diverse range of hotels with varying degrees of ISA experience. The screening yielded a sample pool of 682 individuals across 262 hotels, ensuring broad representation across different types of hotel operations and strategic alliances.

A Simple Random Sampling method was employed to select participants from this identified population (n = 682). This method was applied because it offers an unbiased representation, where every eligible individual has an equal probability of being selected, reducing potential selection bias. Random sampling enhances the generalisability of the findings, making them applicable to a broader population of hotel owners and managers engaged in ISAs. Ultimately, 202 completed questionnaires were received, forming the final data set for analysis (refer to Table 1). Data collection was facilitated through a carefully constructed questionnaire consisting of 36 items designed to address the study’s key variables related to knowledge transfer in ISAs. Participants were asked to rate their responses on a five-point Likert scale, allowing for nuanced insights into factors such as organisational structure and its impact on KT within their ISA experiences.

Following data collection, Confirmatory Factor Analysis (CFA) was conducted to validate the measurement model and to examine the relationships between observed variables and latent constructs (refer to Figure 1). CFA is a robust statistical technique widely used to validate theoretical constructs, ensuring that the measurement model accurately reflects the underlying structure of the data. CFA has been extensively applied in various fields, including psychology (e.g., Fu et al., 2022; Rezaei et al., 2021a), sociology (e.g., Glevarec and Cibois, 2021; Sulphey, 2020), and business studies (e.g., Steenkamp and Maydeu-Olivares, 2023; Rezaei et al., 2021b, 2022b), demonstrating its relevance and reliability for this type of analysis. The CFA was performed using Lisrel software, known for its robust capabilities in structural equation modelling, further ensuring the robustness of the analysis.

3.3 Common method bias

Common method bias (CMB) occurs when response variations are attributed to the measurement instrument rather than the actual context of the respondents. Addressing this bias is essential to ensure that method-related factors do not distort the relationships observed in the study. These factors can undermine the integrity of structural relationships and lead to contamination of study outcomes. Harman’s Single-Factor Test was employed in SPSS to investigate the presence of CMB. This test involves loading all items that measure the latent variables onto a single common factor. If this factor accounts for more than 50% of the variance in the total variables, it suggests the presence of CMB (Kock et al., 2021). The results revealed that the identified common factor explains only 33% of the variance. Therefore, it can be confidently stated that CMB is not a concern in this research.

3.4 Multivariate normality and multicollinearity

Before proceeding with any further analysis, it is critical to assess whether the data conforms to a normal distribution. Various methods exist for this purpose, but for Likert-scale data, evaluating skewness and kurtosis provides the most suitable approach (Keller, 2015). Skewness measures the symmetry of the distribution function, whereas kurtosis indicates the degree of peakedness or flatness. According to Garson (2012), the data do not follow a normal distribution if the skewness and kurtosis values fall outside the range of (2, −2). This study’s assessment of skewness and kurtosis supports the assumption of normal distribution (see Table 5). In addition to normality, it is crucial to address the independence of the independent variables, also known as descriptive variables. To examine multicollinearity, the independent variables’ variance inflation factors (VIF) were computed. The VIF values obtained in this analysis ranged from 3.15 to 4.37, indicating that multicollinearity is not a concern with the data set (Zuur et al., 2010).

3.5 Reliability and validity

Reliability and validity are essential in research to ensure that measurement instruments or questionnaires accurately reflect the studied constructs. Various indicators, such as internal reliability, composite reliability (CR) and Average Variance Extracted (AVE), can be used to assess the reliability of the measurement model.

Internal reliability, measured using factor loading, assesses the consistency of the measurements. Typically, values exceeding 0.7 indicate satisfactory internal consistency. When the CR indicator is available, evaluating a latent construct’s reliability and internal consistency is necessary. The acceptable values for CR are typically above 0.5. Additionally, AVE measures the average percentage of variation explained by the measurement items for a latent construct, with a recommended minimum value of 0.5 (refer to Table 2 for details).

Validity, on the other hand, examines the accuracy of a measurement instrument by comparing it with relevant criteria or established measures (Field, 2013). This is crucial because inappropriate measurements can invalidate scientific study findings. Convergent validity assesses the internal correlation and alignment of the items within a category. In other words, it examines the relationship between items when representing a construct (latent variable). Convergent validity can be assessed by reviewing the AVE and CR values together, with criteria such as AVE > 0.5, CR > 0.7, and CR > AVE indicating satisfactory convergent validity (see Table 3 for details).

Overall, this study’s reliability and validity assessment involved evaluating the reliability, composite reliability and Average Variance Extracted. These measures were integral to ensuring that the measurement model delivered consistent and accurate results, reinforcing the credibility and robustness of the study’s outcomes (see Table 3 for the specific values obtained).

Ensuring the distinctiveness of the constructed measure is crucial. Discriminant validity allows researchers to assess the degree of differentiation between questions that belong to different factors. This study employed the method proposed by Henseler et al. (2016) to examine divergency, specifically using the Heterotrait-Monotrait Ratio (HTMT). This index was computed using SPSS and Excel. According to the results, for all paths, the values were less than the acceptable value (<0.9) indicated by HTMT (OD<=>ID = 0.461, OD<=>TD= 0.488, and TD<=>ID= 0.465).

3.6 Model fit assessment

Structural Equation Modelling (SEM) was employed to evaluate the proposed model’s goodness of fit. The results in Table 4 demonstrate that the model exhibits a highly satisfactory fit.

4. Results and discussion

This study investigated the main factors influencing KT in ISAs (ISAs) within the hotel industry. The hotel industry operates in a highly intricate and competitive environment, encompassing numerous small and large businesses in multi-culture, inter-culture and cross-culture settings. Due to its close association with travel and entertainment, the industry remains at the forefront of strategic alliances, providing fertile ground for innovative ideas.

The findings revealed that the factors categorised under different drivers play a pivotal role in shaping the success of KT within ISAs. However, among these drivers, factors related to TDs, such as Web 2.0, KMS and IT infrastructure, generally received higher values (Table 5).

Web 2.0, as a factor of TDs, played a significant role in fostering dynamic and interactive online communities within the alliance context. These tools bridge geographical distances, allowing alliance partners to transcend borders and collaborate in real-time. Social media platforms, video conferencing, and virtual collaboration spaces facilitate informal knowledge exchange and relationship-building. Webinars and web-based training sessions supported continuous learning, enabling partners to stay updated with industry trends and best practices and fostering camaraderie and shared purposes. KMS, another vital factor of TDs, is the backbone of KT’s endeavours within alliances. Acting as a robust repository, KMS centralises knowledge resources and ensures easy access to relevant information, reduces redundancy and prevents information silos. KMS also facilitates the capture and codification of tacit knowledge, making it accessible to all partners and safeguarding it from personnel changes. Integrating KMS as part of the KT strategy strengthened the alliance’s commitment to harnessing knowledge for mutual benefit. The findings in this driver align with prior research, emphasising the significance of technology-related factors in promoting KT within alliances (Du Plessis, 2005; Rezaei et al., 2022a, 2022b, 2022c, 2023a, 2023b).

Furthermore, the research findings emphasised the pivotal role of trust, reciprocity, and intention as influential factors in IDs in KT dynamics within cross-border alliances. These relational factors foster a cohesive and collaborative KT environment, transcending geographical and cultural barriers and fostering unity among alliance partners. Trust, as an essential element in business with a broad impact on the decision-making process regarding the micro and macro vision, such as business expansion (Heydari et al., 2023), is also fundamental in cultivating effective KT in ISAs, transcending differences, and promoting open communication. A high level of trust creates an atmosphere of psychological safety, encouraging individuals to share valuable insights and innovative ideas and paving the way for successful KT initiatives that span borders. Reciprocity complements trust, fostering a culture of mutual support and cooperation among alliance partners. Partners actively engage in collaborative learning, knowing their contributions would be valued and reciprocated, creating a foundation for sustained and meaningful KT. Intention is another crucial factor that aligns partners with their common objectives and shared goals. Shared intentions to collaborate and learn bridged gaps drive KT’s collective pursuit for the benefit of the alliance.

While personal motivation was acknowledged as a factor in ID, participants did not assign it the same level of importance as trust, reciprocity, and intention. This suggests that, while individual motivation may influence KT behaviours to some extent, relational drivers significantly impact KT dynamics within ISAs. Nonetheless, personal motivation can complement relational drivers, enhancing KT behaviours when combined with trust, reciprocity, and clear intentions. The results of this enquiry are consistent with those of previous studies, further underscoring the significance of relational elements in fostering KTs within alliances. The results for OD factors are consistent with those of previous studies, highlighting the importance of relational elements in fostering KT within alliances (Donate et al., 2015; Du Plessis, 2005; Mojtaba, 2022; Nonaka and Toyama, 2015; Rezaei et al., 2023a, 2023b).

Finally, the findings from ODs highlighted various factors’ crucial role in influencing KT within Inter-Organisational Strategic Alliances (ISAs). Leadership support has emerged as a pivotal element in fostering practices and abilities for KT. Leaders’ active championing of KT initiatives and resource allocation motivates alliance partners to exchange knowledge, emphasising the importance of sharing insights, expertise, and best practices as a collective value. Additionally, financial and non-financial rewards appeared to be significant factors driving KT within ISAs. Tangible and non-financial rewards foster a culture of recognition and collaboration, encouraging partners to invest time and effort in sharing knowledge. The impact of organisational culture on KT is also notable. A culture that values collaboration provides fertile ground for effective KT to flourish. In such an environment, alliance partners feel comfortable sharing their expertise and insights, nurturing trust, and strengthening interpersonal relationships, further enhancing their willingness to engage in knowledge exchanges.

Furthermore, the results indicated that the flexibility of the organisational structure plays a considerable role in facilitating KT within ISAs. An agile and adaptable structure allows for swift communication, decision-making, and coordination between partner organisations and promotes tacit and explicit knowledge exchange. In addition, a flexible structure encourages the development of cross-functional teams and task forces, integrates diverse perspectives, and enhances problem-solving capabilities.

These outcomes have also been aligned with findings from various fields, reaffirming the significance of organisation-related factors in smoothing KT in business alliances (Chierici et al., 2019; Conrad et al., 2019; Rezaei et al., 2022a, 2022b, 2022c, 2023a, 2023b; Robertson and O'Malley Hammersley, 2000).

5. Theoretical and managerial implications

First and foremost, this study contributes significantly to KM and strategic alliances by conceptualising KT drivers in the hotel industry context. Identifying these drivers sheds light on the essential factors that underpin successful knowledge exchanges within strategic alliances. This conceptualisation enhances the current understanding of the role of knowledge in such partnerships and offers valuable insights into the motives and drivers that support KT. Moreover, the developed scale for measuring KT drivers, including factors like performance and organisational efficiency, lays the foundation for future research in this domain.

An outstanding aspect of this study lies in the extensive involvement of diverse stakeholders, including owners, managers, founders, and industry professionals in the hotel and tourism sectors. This comprehensive participation ensures a robust and multifaceted perspective for both theoretical and empirical enhancement of research in this area. Third, this study addresses a critical gap in the existing literature, which previously lacked in-depth exploratory studies on the factors influencing KT within strategic alliances. This research opens new avenues for advancing KT research within strategic partnerships by providing a clear view of these drivers. The findings have crucial implications for management and governance policies that support strategic partnerships. Recognising KT as a vital precursor to business success, the study underscores the importance of understanding the drivers for transferring activities. The identified range of KT drivers can assist entrepreneurs in gaining a comprehensive understanding of knowledge’s role and making informed decisions to facilitate KT within potential alliances.

In addition, developing indicators enables the detection of inefficient KT practices. Hotel industry managers and owners can utilise these findings as a guide to pinpoint the causes of problems in their KT systems or strengthen their external business collaborations. Moreover, the study’s results offer insights to managers and owners in the hotel industry to understand their competitive market position and the reasons behind the superior performance of some of their business competitors. Essentially, the results serve as a roadmap for evaluating strategic alliances, enabling entrepreneurs to identify weaknesses and obstacles and improve their businesses by addressing internal shortcomings in the KT process.

6. Limitations and future research directions

This study is subject to some limitations. Firstly, the generalisability of the findings is constrained by its focus on the hotel industry. Future research could broaden the scope by investigating KT drivers across various industries and organisational sizes. Secondly, the study employed a cross-sectional research design, which does not permit the identification of causal relationships. A longitudinal approach could provide deeper insights into the temporal dynamics of KT drivers and their impact on business outcomes. Additionally, while the study relied on surveys for data collection, incorporating in-depth interviews or focus groups could further enrich the understanding of the complexities surrounding KT drivers in international strategic alliances (ISAs).

Given these limitations, several avenues for future research are apparent. Firstly, researchers could conduct comparative studies across different industries to elucidate each sector’s unique KT drivers and challenges. This would contribute to a more nuanced understanding of KT dynamics in varied organisational contexts and among partners in global alliances. Secondly, extending the study to explore the repercussions of international crises, such as economic recessions, local conflicts or natural disasters, on KT drivers could yield valuable insights into the resilience and adaptability of partners in international alliances facing external shocks.

Finally, future research could employ more sophisticated statistical techniques, such as structural equation modelling or machine learning algorithms, to unravel the intricate relationships among KT drivers and their subsequent impact on business performance. This advanced analytical approach would offer more robust and nuanced insights into the factors contributing to effectual KT within ISAs.

Figures

Figure 1

Conceptual model

Table 1

Demographic characteristics of the sample

Category	Frequency	%
Gender
Male	120	60
Female	67	33
N/A	15	7
Age
20–30	20	10
31–40	39	19
41–50	41	20
51–60	56	28
>60	46	23
Tenure (Years of experience)
3>	34	17
4–8	34	17
9–14	82	40
>15	52	26

Source: Author’s own work

Table 2

Construct reliability

Indicators	Factor loading	Composite reliability (CR)	Average values extracted (AVE)	Is it established?
ID		0.808	0.602	Yes
Trust	0.867
Reciprocity	0.812
Personal motivation	0.649
Intention	0.789
OD		0.812	0.754	Yes
Organisational culture (OC)	0.813
Organisational structure (OS)	0.807
Leadership support	0.811
Financial rewards	0.805
Non-financial rewards	0.793
TD		0.801	0.667	yes
IT infrastructures	0.875
Web 2.0	0.895
Knowledge management system (KMS)	0.882

Source: Author’s own work

Table 3

Convergent validity

Items	Average variance extracted (AVE)	Composite Reliability (CR > 0.7)	Is it established? (AVE > 0.5), (CR > 0.7)
Individual drivers	0.602	0.808	Yes
Organisational drivers	0.554	0.812	Yes
Technological drivers	0.542	0.801	Yes

Source: Author’s own work

Table 4

Fitness indices

Fit indices	Reference value	Model value	Comments
χ2/df	χ2 /df < 3	1.984	Achieved
p‐value	p‐value <0.05	0.0498	Achieved
RMSEA	RMSEA < 0.05	0.049	Achieved
GFI.	More than 0.90	0.91	Achieved
AGFI	More than 0.90	0.92	Achieved
NNFI	More than 0.90	0.92	Achieved
CFI	More than 0.90	0.91	Achieved

Source: Author’s own work

Table 5

Mean, SD, skewness, kurtosis, factor loading values

Drivers	Factors	Items	Mean	SD	Skewness	Kurtosis	Factor loading
ID	Trust							0.867
		Tr1	3.855	0.74	0.382	1.134	0.678
		Tr2	3.934	0.759	0.244	1.266	0.631
	Reciprocity							0.812
		Re1	3.577	0.713	1.077	0.277	0.631
		Re2	3.671	0.773	0.845	0.857	0.648
	Personal motivation							0.649
		PM1	3.726	0.789	0.592	0.98	0.642
		PM2	3.661	0.748	0.571	0.62	0.678
		PM3	3.666	0.792	0.814	0.865	0.619
	Intention							0.789
		In1	3.548	0.699	0.907	0.095	0.778
		In2	3.756	0.783	0.634	1.129	0.701
		In3	3.711	0.76	0.728	0.949	0.731
OD	Organisational culture (OC)							0.813
		OC1	3.835	0.811	0.469	1.362	0.796
		OC2	3.968	0.686	0.143	0.916	0.752
		OC3	3.741	0.754	0.647	1.008	0.809
	Organisational structure (OS)							0.807
		OS1	3.746	0.76	0.64	1.036	0.783
		OS2	3.538	0.632	1.061	−0.004	0.819
		OS3	3.652	0.643	0.691	0.554	0.828
		OS4	3.721	0.615	0.42	0.66	0.791
	Leadership support							0.811
		LS1	3.652	0.636	0.553	0.5	0.704
		LS2	3.602	0.575	0.266	0.563	0.685
		LS3	3.661	0.603	−0.282	0.145	0.809
		LS4	3.454	0.589	−0.098	0.443	0.789
	Financial rewards							0.805
		FR1	3.567	0.678	0.018	0.202	0.815
		FR2	3.751	0.726	−0.102	0.255	0.878
		FR3	3.85	0.768	−0.498	−0.091	0.805
	Non-financial rewards							0.793
		NFR1	3.835	0.792	0.281	1.063	0.772
		NFR2	3.805	0.572	−0.987	−1.291	0.802
		NFR3	4.067	0.701	0.007	1.001	0.81
TD	IT infrastructures							0.875
		IT1	3.865	0.487	−0.394	−0.111	0.604
		IT2	3.989	0.537	−0.057	−0.332	0.596
	Web 2.0							0.895
		WB1	3.885	0.542	−0.07	0.076	0.759
		WB2	3.776	0.583	−0.103	0.173	0.705
		WB3	3.667	0.618	0.318	0.452	0.778
		WB4	3.454	0.589	0.759	−0.251	0.765
	Knowledge management system (KMS)							0.882
		KMS1	3.618	0.616	0.599	0.427	0.569
		KMS2	3.687	0.705	0.74	0.719	0.58
		KMS3	3.618	0.624	0.753	0.44	0.591

Source: Author’s own work