Introduction

The pattern of relationships between network actors is referred to as structural capital (Adler & Kwon, 2002). It refers to the entire network of relationships between network members and the structure of that network (Liao & Welsch, 2003; Sardo et al., 2018). The sources of these relationships are generated from the daily employees' routine interactions and knowledge sharing (Krause et al., 2007), which form formal and informal ties among them. These ties make it easier for network actors to generate, acquire, and transmit knowledge and new ideas (Ordonez de Pablos, 2004; Li, 2006; Sardo et al., 2018). They also foster strong social and interpersonal relationships among network members, fostering closeness and establishing a framework for social interactions (Nahapiet & Ghoshal, 1998; Butler & Purchase, 2008; Lee, 2008; He et al., 2009; Ahmad et al., 2019). Mehrabian & Russell (1974) described social interactions as a sequence of personal exchanges. It encompasses a range of acts such as small-group interactions, friendship creation, involvement, and assistance (Bitner, 1992; Zemke & Shoemaker, 2007). According to Kilpatrick (2002), social interactions enable employees to use their knowledge and abilities, as well as the knowledge and skills of others, to improve internal functioning through informal social networks. Employees can also get to know one another through social interactions and share resources and information, making it easier for them to do their jobs (Ramström, 2008). Moreover, social interactions would boost innovative behaviour, thus enabling the growth of new forms of relationships (e.g. friendships) and innovativeness (Putman, 1993; Liao & Welsch, 2003). It also helps employees understand execution-oriented managerial activities, which improves customer satisfaction (Krause et al., 2007).

According to the literature review, structural capital has three main dimensions: the strength of actor-to-actor network ties (Nahapiet & Ghoshal, 1998; Kumar & Worm, 2003; Montazemi et al., 2008; Yan & Guan, 2018 ), network configuration or how the employees are organized in the organization (Kumar & Worm, 2003; Inkpen & Tsang, 2005; Lee, 2008; Polyviou et al., 2019) and network stability or how stable of the information flow in the company (Liao & Welsch, 2003; Inkpen & Tsang, 2005; Butler & Purchase, 2008; Allameh, 2018). Despite playing important roles in improving employees innovative behaviour and enhancing customer satisfaction, there are scarce studies that link social capital with innovative performance and customer satisfaction, the current study is one of the first studies that investigate the direct impact of structural capital three sub-dimensions (network ties, configuration, and stability) on customer satisfaction, and the role of innovative behaviour as a mediating variable.

Theoretical Background and Hypotheses Development

Network Ties

Network ties can be defined as the strength of associations between the network main actors (Nahapiet & Ghoshal, 1998; Lee, 2008; Yan & Guan, 2018) that form the relationships between the network members (Chiu et al., 2006; Fliaster and Spiess, 2008) and advance social interactions inside the organisation (Ramström, 2008). They simplify social interactions and help knowledge exchange and sharing between organization members (Inkpen & Tsang, 2005; Polyviou et al., 2019), facilitating access to the entire organization's resources (Haythornthwaite, 2002) and allowing employees to combine and exchange knowledge (Chiu et al., 2006; Yan & Guan, 2018).

Employees with strong ties are more likely to share their best practices and experience (Tiwana & McLean, 2005), as well as to welcome new ideas and engage in innovative behavior (He et al., 2009). People with strong social links are more inclined to assist in the transfer of information, experience, and competence inside an organization (He et al., 2009). They create a high degree of conformity among employees that encourages individuals’ ability to listen to the advice and help not only of their colleagues but also to organization customers (Hossain & de Silva, 2009). In conclusion, strong social network ties have a positive effect on both innovative behaviour and customer satisfaction. Thus, we suggest the following hypotheses:

H1: Network ties positively impact employees' innovation behaviour.

H2: Network ties positively impacts customer satisfaction.

Network Configuration

Network social interactions are influenced by the system in which network performers are configured or constructed (Nahapiet & Ghoshal, 1998; Chow and Chan, 2008; Han et al., 2020). As a result of these social interactions and integrative structure emerges (Janz & Prasarnphanich, 2003). Within an organization, the integrative structure replicates a high level of connectedness, closure (Costenbader & Valente, 2003; Hossain & Wu, 2009; Polyviou et al., 2019), and simple access to essential information (Nahapiet & Ghoshal, 1998; Butler & Purchase, 2008). The integrated structure improves employee connectivity and makes it easier to access any database within an organization. It promotes employee learning and collaboration, as well as the development of coordination and communication channels for the sharing of appropriate expertise, knowledge, and new ideas. It also encourages interrelation and integration among organisational departments and subunits (Germain, 1996; Sciulli, 1998), to improve customer satisfaction.

Exchange and cooperative actions are facilitated by the integration of organizational departments and subunits (Butler & Purchase, 2008). Employees are more likely to tolerate other points of view and establish common interests as a result of cooperative behaviors, which encourage resource sharing (Tsai & Ghoshal, 1998; Tsai, 2002). These cooperative acts promote information flow among all touchpoints inside the organization (Burt, 2000) and supply relevant personnel with the information they require when they need it (Berger et al., 2002). This can aid in the resolution of consumer complaints and the improvement of customer satisfaction.

They also provide front-office support in the form of a customer data repository and software that assists in responding to consumer needs (Berger et al., 2002; Jayachandran et al., 2005) and reduce the barriers in the intra-functional communication (Chen and Quester, 2009) and allows employees successfully deal with customer complains (Seibert et al., 2001). Consequently, we put forward the following hypotheses:

H3: Network configuration positively impacts employees' innovative behaviour.

H4: Network configuration positively impacts customer satisfaction.

Network Stability

The constancy of information flow inside organizations is referred to as network stability (Yan & Guan, 2018; Inkpen & Tsang, 2005; Stanko et al., 2006). Information flow between organizational units is valuable, according to Deeter-Schmelz (1997), because it comes from multiple functional areas and reflects different professional perspectives. Furthermore, according to Walker et al. (1997), consistent information flow within organizations leads to the rapid establishment of cooperative norms, which eliminates the structural gaps in an organization (Eng, 2006).

The consistency of information flow promotes coordination between organizational units, which improves the internal functions of the organization (Szymanski & Henard, 2001; Ismail et al., 2007), improve employees' readiness to share technical, operational, strategic information, and new ideas (Richards & Jones, 2008; Yan & Guan, 2018). Employees have more faith in the reliability and integrity of the network when it is stable (Morgan & Hunt, 1994), this stability inspires information exchange (Stanko et al., 2006), creates long and lasting relations among employees, thus, promotes reciprocity among them (Newcomb, 1961; Chowdhury et al., 2019). Deeter-Schmelz (1997) also points out that the constancy of information flow reinforces the linkages between organisation departments and improves the direction and developments between these units to improve customer service and satisfaction. Based on these discussions, the below two hypotheses are suggested:

H5: Network stability positively impacts employees' innovation behaviour.

H6: Network stability positively impacts customer satisfaction.

Innovation Behaviour and Customer Satisfaction

Quality of customer experience and satisfaction are depending on Employee-customer interaction (Chand, 2010). In this sense, there is a scarcity of studies on how innovation and consumer happiness are linked in the hospitality industry. Although there are a few research papers that explored the relationship between innovation and non-financial measures of organization performance (i.e., Storey & Easingwood, 1998; Tsai, 2007), the majority of studies tested the impacts of innovation on some financial and efficiency aspects such as sales (Nicolau & Santa-Mara, 2013) and hotel profitability (Mattsson & OrfilaSintes, 2014).

For a variety of reasons, customer satisfaction should be investigated (Wikhamn, 2019). First and foremost, customer satisfaction is a good predictor of non-financial measures of performance. Hotels will be unable to thrive unless their customers are satisfied. Second, customer satisfaction did not appear in the hotel's financial reports, disparate other financial indicators that are recurrently and negatively influenced by a substantial investment in innovation.

Third, customer satisfaction is a barometer of a hotel's competitive market share. Fourth, customer satisfaction reflects hotel image and reputation among competitors. People typically look at online ratings and reviews before making a reservation.

Hotels come up with new ideas and innovative behaviour to deal with the dynamic and changing environment. Naturally, hotels think about how their customers would react to new ideas. An important concern is that enhancements should not have a detrimental impact on consumer satisfaction.

According to Orfila-Sintes & Mattsson (2009), not all innovations have a direct (and positive) impact on performance. Lin and lin (2013), for example, claim that service quality mediates the link between service innovation and a company's financial performance. Employees' commitment is positively influenced by high-performance human resource strategies (Dhar, 2015), which has a beneficial impact on employee innovative behaviour. As a result, the following theory is put forth:

H7: Employees innovative behaviour positively impacts customer satisfaction.

As shows in Figure 1.

Figure 1: Research Framework and Hypotheses

Methods

Measures and Instrument Development

The field data were collected using a quantitative research approach. A structured questionnaire was designed to target respondents without any help from the authors. All of the study's measures were derived from a thorough review of the literature and operationalized on a multi-dimensional scale and showed good reliability. Structural capital was measured by three dimensions: network ties (5 items, a=0.945), network configuration (5 items, a=0.980), and network stability (5 items, a=0.956), the three dimensions were developed by Mohamed (2011) and adopted from Nahapiet & Ghoshal (1998); Kumar & Worm (2003) Inkpen & Tsang (2005); Butler & Purchase (2008). Employee innovative behavior was measured using a six-item scale (a=0.974) developed by Hu et al. (2009) and adapted from Scott and Bruce's (1994) employee innovative behavior scale for hospitality organizations. Sample items include "At work, I propose my own creative ideas and convince others". Finally, perception of customer satisfaction was measured by a modified 5 items scale (a=0.977) derived from Al Yahia et al, (2020) (See Table 1).

The questionnaire content was validated by translated the original version from English to Arabic and then back. The questionnaire was pre-tested by twelve academics and ten professionals to determine its content validity and provide feedback. To purify and refine the scale, a few remarks on questionnaire words were updated.

Sampling and Data Collection Process

All employees (managerial level) of travel agencies (category A) and five-star hotels in Greater Cairo, Egypt, construct the study population. To collect the required data from the target population, 30 faculty-student enumerators were recruited and well-trained to start the process of data collection. This technique was preferred to avoid the low response rate of traditional data collection methods (postal and/or online data collection procedures), as well as people's aversion to taking part in anonymous surveys. Respondents had to sign a consent letter before participation. Enumerators were trained to read the questionnaire and write down the responses from respondents.

The paper targeted 750 managerial-level employees. As a result, 800 questionnaires were read out by enumerators to the targeted participant, and 700 replies were collected and valid for further analysis, with a response rate of 87.5 %. Data were collected during the first three weeks of June 2021. To test if the mean score of early and late respondents differed, a t-test was conducted. The results showed no significant discrepancies in the data, implying that there is no non-response bias (Armstrong & Overton, 1997).

Among the 700 valid replies, 200 were females (28%) and 500 males (72%). The majority (70%) had a faculty degree and were aged between the ages of 35 and 55 (80 %). The majority (72%) were married, and more than half (59%) had worked in the field for at least three years. On a scale of one to five, the scale variables have mean values ranging from 3.30 to 3.86. The standard deviation values (see Table 1) range from 0.720 to 1.258, indicating that the data is more distributed and less tightly packed around its mean. (Bryman & Cramer, 2012).

Data Analysis and Results

Structural Equation Modeling (SEM) was used to assess the study hypotheses using Amos V.22. Anderson & Gerbing (1988) advocated a two-phase method, which was used. Using first-order confirmatory factor analysis, in the first phase to test the convergent and discriminant features of the study measures by employing Confirmatory Factor Analysis (CFA). The research hypotheses were then examined in the second phase by testing the structured model fit to data. The model fit to data was evaluated using the fit indices proposed by Hair et al., (2018), Byrne (2011) and Kline (2011), as shown in Table 1.

Measurement Model

Table 1 illustrates the output of the CFA model's results, which reveals a proper model fit to data: (χ2 (298, N=700)=645.766.058, p<0.001, normed χ2 =2.167, RMSEA=0.0417, SRMR=0.0294, CFI=0.971, TLI=0.974, NFI=0.972, PCFI=0.682 and PNFI=0.671).

All utilised latent variables' (dimensions) composite reliability (CR) and Cronbach's alphas (a) scores are satisfactory and above the required level of 0.80 (Hair et al., 2018; Fornell & Larcker, 1981), indicating solid internal reliability. The factor loadings' standardised regression weights ranged from 0.800 to 0.963, above the preferred threshold of 0.7 with the lowest t-values of 43.571 (Anderson & Gerbing, 1988). These findings show that the variables used to operationalize the research scales had positive and strong connections, implying that convergent validity was established. The Average Variance Extracted (AVE) scores for all dimensions exceeded the suggested value of 0.50 (Fornell & Larcker, 1981), indicating convergent validity. The Maximum Shared Variance (MSV) values did not exceed the AVE values, indicating that the research dimensions are discrete (Hair et al., 2018). As a result, discriminant validity was accepted (See Table 1).

Structural model outputs

According to Anderson and Gerbing (1988), the structural model depicted in figure 2 should be examined in the second phase (after CFA). The SEM findings revealed that the Goodness-of-Fit (GoF) indices were satisfactory.: χ2 (292, N=700) = 1324.22, p <0.001, normed χ2 =4.535, RMSEA = 0.046, SRMR=.0398, CFI = 0.969, TLI = 0.954, NFI = 0.960, PCFI = 0780 and PNFI = 0.773 (See Table 2). Furthermore, as the squared multiple correlations (SMCs) value equals 0.57, the tested model suggested that structural capital dimensions (network ties, network configuration, and network stability) and employees innovative behavior as exogenous variables had a satisfactory explanatory power in explaining variation on the endogenous variables (customer satisfaction).

Figure 2: Result of the Structural Model

Table 2 Result Of The Structural Model
Hypotheses
		Beta   (ß)	C-R (T-value)	SMC	Hypotheses results
H1	Network ties                         Employee innovative behavior	0.43***	9.132	----	Supported
H2	Network ties                        Customer Satisfaction	0.32***	6.262	----	Supported
H3	Network configuration                       Employee innovative behavior	0.44***	10.775	----	Supported
H4	Network configuration                         Customer satisfaction	0.30***	3.867	----	Supported
H5	Network Stability                                    Employee innovative behavior	0.47***	11.870	----	Supported
H6	Network stability                         Customer satisfaction	0.33***	5.115	----	Supported
H7	Employee innovative behavior                       Customer satisfaction	0.52***	12.411
Customer satisfaction		------------	------------	0.57	------------
“

Through the mediating role of employee innovative behaviour, Table 2 and Figure 2 show the effects of structural capital three aspects (Network ties, Network configuration, and Network stability) on customer satisfaction. The SEM findings demonstrated that Network ties (as a dimension of structural capital) have a positive and significant impact on employee innovative behaviour (β=0.42, t-value=9.132, p<0.001), as well customer satisfaction (β=0.32, t-value=6.262, p<0.001), hence supporting for hypotheses H1, and H2. Similarly, Network configuration (as a dimension of structural capital) positively and significantly impact employee innovative behaviour (β = 0.44, t-value= 10.775, p < 0.001), and customer satisfaction (β=0.30, t-value=3.867, p<0.001), supporting hypotheses H3 and H4. Likewise, Network stability (as a dimension of structural capital) positively and significantly impacts employee innovative behaviour (β = 0.47, t-value= 11.870, p < 0.001), and customer satisfaction (β = 0.33, t-value= 5.115, p < 0.001), confirming hypotheses H5 and H6. Moreover, the results show that employee innovative behaviour has a direct positive and significant impact on customer satisfaction (β = 0.52, t-value= 12.411, p < 0.001), thus supporting hypothesis H7.

Finally, employee's innovative behaviour was found to partially mediates the relationship between structural capital three dimensions and customer satisfaction as all direct and indirect paths were found to be positive and significant (Zhao et al., 2010)

Discussion

In this research paper, the relationships between structural capital as a multidimensional construct contains three sub-dimensions: (network ties, network configuration, and network stability and customer satisfaction were evaluated through the mediating role of employees' innovative behaviour. To summarize, the findings are divided into three categories. To begin with, all structural capital dimensions: network ties, network configuration, and network stability were significantly and positively impact employees’ innovative behaviour commitment. Second, network ties, network configuration, and network stability were significantly and positively improving customer satisfaction. Third, employees' innovative behaviour partially mediates the relationship between the three dimensions of structural capital and customer satisfaction.

Implications for Theory and Practice

This research contributes to the research and theory of structural capital as a new marketing field of expertise in several ways. Because structural capital marketing theory is yet underdeveloped and fragmented, and the environment is continually changing, this work can be seen as a first step toward developing a more solid theory. This research identifies six possible theoretical and practical contributions to the literature on social capital, employee innovation, and customer satisfaction.

First, this research invites scholars to look beyond the role of structural capital in creating value for an organization and consider how it might improve customer satisfaction. Through the mediation influence of employees' innovative behavior, the findings of this study give empirical support for both direct and indirect benefits of intrafirm social capital on customer satisfaction.

Second, the outcomes of this study back up the theory that structural capital has an impact on the formation of efficient interunit links within an organization. The findings solve an issue that has remained unanswered in the literature: Where do network linkages within an organization come from? Previous research looked at the significance of earlier linkages in network building but didn't go into detail about how linkages are formed (Tsai and Ghoshal, 1998; Lu and Yang, 2011). This study contributes to our understanding of the evolutionary dynamics of an organization's internal linkages by empirically analyzing how network linkages are generated within an organization. These internal connections open up new possibilities for beneficial resource sharing among organizational units. This research shows that intrafirm structural capital has a favorable impact on developing efficient internal linkages inside an organization, which can improve customer satisfaction.

Third, while previous research has shown that structural capital facilitates the generation of innovative intellectual capital (Coleman, 1988; Granovetter, 1973; Nahapiet and Ghoshal, 1998), this study shows that structural capital has a very different, but equally important, impact on the strategic formulation of customer satisfaction strategy within organizations.

Fourth, the findings show that managers who form strong, long-term personal relationships with their colleagues are more likely to encourage the development of more relational types of exchange with their customers. As a result, by offering a new level of analysis and additional explanatory factors, our findings may aid researchers in refining their understanding of service interactions. It may also assist managers in developing productive and trusted connections with their colleagues by assisting them in coordinating their efforts on both a corporate and personal level.

Fifth, the proposed model argues that managers must do more than simply support structural connections to create structural capital in organizations. Furthermore, managers in these organizations should be aware that creating structural capital necessitates not only the creation of greater social ties but also the maintenance of consistency in information flow and the creation of efficient interfirm networks inside organizations (Lesser, 2000).

Sixth, to the best of the authors' knowledge, this is the first study to use structural equation modeling (SEM) as the primary data analysis technique to investigate the mediating role of employees' innovative behaviour in the relationship between structural capital (as a multidimensional construct) and customer satisfaction. SEM may examine both direct and indirect causal links in a single model while taking measurement error into account (Byrne, 2011). In practice, using this data analysis technique can assist travel agencies and hotel executives in allocating money to structural capital dimensions that have the greatest influence on innovative behaviour and customer satisfaction.

Limitations and Suggestions for Future Research

The current research has some limitations. First, due to the study's cross-sectional design, causality between the research variables cannot be established. As a result, to prove the causality between the research variables, a longitudinal research design is required. Second, using data from a single source with self-reported respondents may raise concerns about method bias (Podsakoff et al., 2012). Future studies could use a variety of data sources.

Third, the findings are based on Egyptian managerial level employees of travel agencies and five-star hotels, theoretically restricting the generalizability of the current study's findings to other cultural perspectives. As a result, future research should look at whether cultural differences in another environment alter the hypothesized relationships.

Finally, the current study used structural capital as a multidimensional construct and looked at the direct and indirect effects of its three sub dimensions: network ties, network configuration, and network stability on innovative behaviour and customer satisfaction. However future studies can further investigate the possible two-way or three-way relationships between the three dimensions of structural capital (Elshaer & Augustyn, 2016).

Acknowledgment

The authors acknowledge the Deanship of Scientific Research at King Faisal University for the financial support under Nasher Track (Grant No. NA00059). The corresponding author for the article “Structural Capital and Customer Satisfaction: the Role of Employees Innovative Performance” Alaa M.S. Azazz, King Faisal University, Al-Hassa 31982, Saudi Arabia; aazazz@kfu.edu.sa, and Suez Canal University

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Received: 23-Dec-21, Manuscript No. ASMJ-21-9060; Editor assigned: 26-Dec-21, PreQC No. ASMJ-21-9060(PQ); Reviewed: 07-Jan-22, QC No. ASMJ-21-9060; Revised: 17-Jan-2022, Manuscript No. ASMJ-21-9060(R); Published: 24-Jan-2022