Event management business continues to gain more attention among scholars owing to the exponential growth witnessed in the events industry in the recent past (Duncan, Scot & Baum, 2013). It is argued that the dynamic nature of the events sector, characterized by the ever-changing trends and tastes among customers calls for more research in the industry (Getz, 2012; Pappas & Bregoli, 2016). Jago and Deery (2010) contend that event management, defined in the extant literature as the planning and execution of events such as conferences, festivals, concerts and weddings among others (Ramsbog, Miller, Breiter, Reed & Rushing, 2008) requires increased innovation.
Indeed, Ramsbog et al., (2008) agree that the rigor required by event management activities such as brand study, target audience identification, event concept formulation, coordination of technical aspects, and actual launch of events is contingent upon outsourcing that has hitherto been a common phenomenon in events management literature (Gunjan, 2015; Reed, 2012). Suffice to say however that outsourcing no doubt requires good relationships that are often outcomes of thriving networks among entrepreneurs offering complementary products or services. Evidence does suggest that entrepreneurs’ ability to start and grow businesses is a function of embeddedness in social networks (Birley as cited in Stam, 2010, p. 625). The argument is that besides exposing entrepreneurs to business opportunities (Ozgen & Baron, 2007), networks also enable them to source for financial and human resources (Shane & Cable as cited in Stam, 2010, p. 625).
The networks theory has previously been exploited to show that positive relationships exist between networking and venture performance (Allan, 2003; Roininen, 2006; Ylinenpaa, 2009). Moreover, networking has also been found to be a springboard upon which new ventures have been formed or authenticated (Cooper, 2002). Facets of networking such as collaborations and alliances have also been found to minimize financial and human resource constraints that startup entrepreneurs have often experienced (Stam & Elfring, 2008). Having said this though, more recent studies hoping to exploit the networks theory have pointed to some nascent gaps.
Witt (2007) for instance, examined how entrepreneurial networks affect success of enterprises that are starting up. Buoyed by social embeddedness, Witt concluded that existing network studies hardly achieved significant results due to their failure to have a consistent approach in defining dependent and independent variables, and also ignoring the need to control for unobserved variables. Truth is said, if indeed the study variables are not explicitly defined, then measurement of such variables remains only conjectures.
Hochberg, Ljungqvist, and Lu (2007) analyzed venture capital networks and investment performance. Building on the premise that strong relationships and networks characterize many financial markets, Hochberg, and his colleagues found out that when venture capital firms have better networks, their financial performance tended to be significantly better. They also established that ventures with better networks were likely to see survival of their portfolios. Although such findings by Hochberg et al. (2007) contribute to existing literature on theory and practice of networks, it assumes that venture performance is only be measured from a financial perspective.
Baoshan, Hisrich, and Dong (2009) analyzed the effect of networking and resource allocation on the performance of SMEs in China. Their motivation was the argument that most firms have tended to lean towards the resource-based view (RBV) at the expense of the firms’ entrepreneurial networks. Using network range and intensity as indicators of networking, Baoshan and colleagues concluded that networks influence resource allocation and by extension, firms performance. Such findings by Baoshan no doubt bring to the fore the importance of network range and intensity in overall business relations and firm performance. The study, however, falls short of defining the performance goals that network range and intensity is targeting. In so doing, the study by Baoshan et al (2009), confirms fears by Witt (2007) that study variables are not given the appropriate definition in existing studies.
Adomako, Danso, Boso, and Narteh (2017) assessed entrepreneurial alertness and performance of new ventures in Ghana. Buoyed by assertions that variations in venture performance could be a function of entrepreneurial alertness and networking capabilities (Samo & Hashim, 2016), Adomako and colleagues established that networking capabilities tended to amplify the potency of entrepreneurial alertness among entrepreneurs raising chances of venture success. The study conducted by Adomako et al (2017) tries to fill the gap of variable definition by zeroing in on networks capability as a dimension of networking. However, use of networks capability as a moderating factor on the relationship between entrepreneurship alertness and venture success fails to answer the question of how networks capability would directly influence venture success.
Interest in how networks affect firm performance have also been conducted in Kenya but have also raised questions related to the variable definition and appropriate methodology. Kiprotich, Kimosop, Kemboi, and Chepkwony (2015) assessed how social networks moderate the relationship between SME performance and entrepreneurial orientation. Their study was motivated by the desire to show how inter-firm networks influence enterprise performance. Using the explanatory design, Kiprotich et al (2015) concluded that social networking moderates the relationship between proactiveness and performance of SMEs. There is no doubt that such findings by Kiprotich and colleagues go to enrich the evolving literature on networks. Their study, however, repeats concerns such as raised by Witts (2007). The study, for instance, examines the performance of SMEs without being explicit on the sectors from which these SMEs are drawn. It has been noted that socially oriented businesses face challenges in balancing their social goals and their financial goals (Dayre & Henin, 2016). It would have therefore been prudent for Kiprotich et al., (2015) to identify the sectors from which the SMEs were drawn from, bearing in mind that social networks may have different impacts on different sectors.
Maina, Marwa, Waiguchu, and Riro (2016) examined the influence of network relationships on firm performance by focusing specifically on SMEs in the manufacturing sector in Kenya. Driven by the understanding that inter-firm networks provide the potential for survival and growth of SMEs (USAID, 2005), Maina and colleagues used multiple regressions to show that the networks measures which were network structure, network content, and network governance accounted for 62.28% of the variation in firm performance. It is notable that the study conducted by Maina et al., (2016) provides a model that may be used to predict the performance of SMEs when network structure, network content, and network governance are known. It is however incumbent upon us to note that Maina and colleagues have tended to rely on definitions of network content and governance advanced by Hoang and Autonac (2003). In consideration, that globalization has created a market dynamics that foster new competition (Britt, 2007), contemporary definitions of these constructs may not have similar implications as was the case in 2003. Another point of departure with findings by Maina et al., (2016) is the coefficient of determination valued at 0.6228 and which tends to suggest that close to 38% of variations in firm performance remains unaccounted for. This then calls for further scrutiny of measurement of the networks construct with a view to identifying more elaborate networks indicators that can explain performance more comprehensive.
Omwenga, Mukulu, and Konali (2013) analyzed the influence of business networking on the performance of enterprises that are women-led in Kenya. Using a descriptive survey design, Omwenga and colleagues established that networking is a determinant of performance of women-owned enterprises. The findings by Omwenga et al., (2013) no doubt, continue to emphasize the importance of forming business networks among entrepreneurs. It is important though to point out that their study fails to clearly define how the independent and dependent variables were measured. Besides, whereas the title of the study alludes to women-led enterprises as the focus, the findings report on women-owned enterprises. Mixing up the concepts of women-led and women-owned enterprises tends to bring into question the validity of the findings made by the three scholars.
From the foregoing background, it is apparent that prior research has concentrated more on examining the effect of business networks on the performance of ventures while ignoring customer’s individual preferences and the nature of different ventures which remain critical factors in relations formation (Ruef et al, 2003). Stam (2010) avers that events management ventures often loaded with events such as business meetings, conferences, and others have a lot of compositional heterogeneity. Such heterogeneity would, therefore, require that expected indicators for measuring the performance of these ventures should factor in customers’ preferences.
In Kenya for instance, the Kenya directory, Soft Kenya (retrieved on 8/3/2013) lists one hundred and eleven (111) registered events companies that traverse diverse fields such as business, social corporate, cultural and educational sectors. Such diversity in events management ventures often raises suspicion and cut through competition among entrepreneurs, and tends to result in unethical marketing strategies (McDonagh, 2014). The present study takes cognizance of the networks theory to argue that the heterogeneity in the events management industry is such that the robustness shown by individual entrepreneurs in terms of network structures, capability and dynamism has potential to define the direction events ventures takes in terms of performance. The goal of this study was therefore to establish the causal link between the three network dimensions of capability, structure, and dynamics and the performance of events management ventures.
Literature Review and Hypotheses Development
Events and Events Management
The need to study the dynamics and contributions of events is founded on the understanding that the events industry continues to gain more prominence in contemporary society. Lopacinska (2013) points out that the events industry has evolved into a professional area of study with potential to contribute immensely to the development of business and other ventures. According to Jago and Shaw (1998), events are so diverse that it becomes difficult to develop an all-embracing definition. Getz (as cited in Carlos & Van Der Wagner,2008, p. 4) delineates two perspectives through which events may be defined.
From the client perspective, Getz opines that events are opportunities for guests to relax and enjoy extraordinary and unusual social-cultural experiences. On the contrary, Getz defines events from an organizers perspective as activities held rarely and often not part of regular activities and programmes undertaken by the organizer. Goldbatt (2000) views events as unique moments marked by special ceremonies and rituals designed to achieve special purposes. Basing on Goldblatt’s paradigm, Getz (2005) argues that events differ both in type and size, and goes on to identify a variety of genres of events as shown in Table 1.
|Political and state||Summits|
|Arts and entertainment||Concerts|
|Business and trade||Meetings|
|consumer and trade shows|
|Educational and scientific||Conferences|
|Recreational||sport or games for fun|
Source: Getz (2005)
Morgan (2007) situates the study of events in place theory. Morgan argues that the extraordinary experiences inherent in events give event-goers a feeling of being in a space set apart for gaining an extraordinary experience. The essence then is that events management is an important function in ensuring that the organizers social and financial goals are realized.
Silvers (2004), defines event management as a process through which planning, preparation, and production of an event is realized. Event management in Silvers views therefore broadly focuses on activities such as concept, planning, economics, communication, sponsorship, human resources, promotion, marketing, monitoring and evaluation, logistics and design among others. Silvers (2004) however observes that event management in contemporary society focuses mainly on experience delivery irrespective of the size and type of the event. The present study, therefore, argues that event management ventures ought to turn to networks in order to facilitate the realization of their goals and objectives and fulfill guest’s needs and expectations.
Networking has often been viewed in literature as a reciprocal ‘grant and receives’ situation aimed at leaving all concerned partners contended (Burg, 1998). Bruderl and Preisendorfer (1998) concur that entrepreneurs who are able to make reference to a diversity of social networks and those who receive support from such networks are bound to be more successful. Johanson and Mattsson (1987) support the views by Bruderl and Preisendorfer in arguing that ventures rely on networks with other players to enjoy resources that they would have hardly been able to enjoy.
In line with the arguments made with references to the potency of networks in realization of events management ventures goals, the present study identified network capability, network structure, and network dynamics (Clegg, Josserand, Mehra & Pitsis, 2016) as dimensions of networks that could be used to explain the robustness required by events management ventures to deal with the rigors of event management and by extension lead to improved performance.
Events venture performance
Measurement of the success and performance of events management ventures remains a matter of concern among scholars (Langen & Garcia, 2009, Talwar et al, 2010). According to Langen and Garcia (2009), existing studies have failed to highlight clear methods for measuring intangible social and cultural impacts derived from the socially oriented venture. Concurring with Langen and Garcia’s views, Talwar et al (2010) argue that measurement of social impacts is further complicated by the rapid social and technological changes that have tended to occasion new dynamics in consumer choices.
Taking cognizance of the concerns by Witt (2007) regarding lack of consistency in variable definition, and bearing in mind that events management ventures are mainly socially oriented, the present study postulates that performance of these ventures could best be measured using the balanced scorecard. The balanced scorecard as cited in Margarita (2008) was developed by Robert Kaplan and David Norton in 1992 ostensibly to measure performance on more than just financial statements. The balanced scorecard theory as noted by Margarita (2008) envisages that financial performance measures are ineffective for the requirements of modern business enterprises. Consequently, the recognition by Kaplan and Norton that besides focusing on financial performance, business entities need to consider other performance perspectives such as customer satisfaction, internal processes of business processes, and growth (Margarita, 2008), becomes ever more important.
Accordingly, the study argues that performance of events management ventures should seek to measure among other aspects: 1) learning and growth, captured through job satisfaction, employee turnover, levels of specialist knowledge, and training opportunities. 2) Internal business processes focusing on activities undertaken per function, process alignment, and process automation. 3) Customer satisfaction, customer retention, event delivery and event quality. 4) The financial performance captured via ROI, cash flow, and financial results.
Networks Capability and Events Management Venture Performance
Network capability is grounded in the competency-based theory advanced by Hunt and Lambe (2000). According to Hunt and Lambe (as cited in Human & Naude, 2009, p.3), the competence-based theory is an internal factors theory that strives to explain resource exploitation strategies development with a view to gaining competitive advantage. Recognizing the potential that firm capabilities have in the context of the network, Walter, Auer, and Ritter (2005) argue that as a higher order construct, network capability is more important than just having networks.
Dyer and Singh (1998) define Network Capability (NC) as the skill used in applying appropriate control mechanisms, common procedures, and spearheading any required changes with a view of creating and handling numerous connections, Walter et al., (2003) on the other hand conceptualize NC as a venture‘s ability to create and make use of external and internal relationships. The NC construct is also noted to rely on aspects such as the capability to form alliances (Kale, 2002), the capability to relate with others (Lorenzoni & Lipparini, 1999) , and more importantly, the capability to belong to networks (Anand & Khanna, 2000).
Walter et al., (as cited in Human & Naude, 2009) identify four constructs that may be used to measure network capability. According to these scholars, the first construct referred to as coordination, connects ventures with common interests for purposes of mutually supportive interactions. The second construct identified as relational skills focuses on the management of relationships among businesses. Partner knowledge, the third construct identified by Walter and colleagues brings stability to a firm‘s position within a network. The fourth construct identified as internal communication concentrates on assimilation and dissemination of more current information regarding partnerships, resources, as well as mutual agreements between partners.
Previous studies have given inconclusive findings regarding the effect of networks capability on venture performance. Human and Naude (2009) established that networks capability as a latent variable relates positively and significantly with firm performance. It is, however, necessary to point out that Human and Naude fail to identify the particular firms under consideration. Besides, the finding by the two scholars that network capability explains a mere 22.9% of the variation in firm performance calls for more scrutiny of this variable in other contexts. Mitrega, Forkmann, Zaefarian, and Henneberg (2017) provide evidence that network capability has a positive influence on firm product innovation, as well as on overall firm performance through improved supplier relationship. Such findings are however inconclusive because the study conducted by Mitrega et al., (2017) focuses on only one setting (automotive parts industry). The heterogeneity experienced in events management ventures, coupled with the inconclusive findings, therefore, led to the postulation that:
H01: Networks Capability has no significant effect on the performance of events management ventures
Network Structure and Events Management Venture Performance
The network structure variable is founded on the social network theory (Barnes, 1954) and posits that the structure of relationship has potential to affect beliefs or behaviors of an individual, group of individuals, or organization. Hoang and Antoncic (as cited in Maina et al., 2016) define network structure as the pattern of ties that binds different actors. Such ties have been noted in literature to be important in firms’ acquisition of external resources and competitive capabilities necessary for their operations (McEvily & Marcus, 2005; Zaheer & Bell, 2005). It is argued that due to the need for accountability to partner business ethics, firms ought to have in place network structures commensurate with expected company culture (Smelser & Baltes, 2001).
The success of network structure is reportedly pegged upon, resources within the network, ties between network partners, partner characteristics and type, the amount of trust manifesting in partner relationships (Marsden & Campbell, 2012). Consequently, the present study conceptualized that network structure could be aptly measured using the following constructs. 1) Type of partners drawn from among relatives, friends, institutions, and service providers. 2) Resources held by partners 3) Strength of ties, and 4) trust among partners.
Although several studies have been conducted and show evidence of positive effects of network structure on firm performance, most of them focus on supply network structure. Bellamy, Soumen, and Manpreet (2014) for instance examined the influence of supply network structure on firm innovation. Using regression techniques Bellamy and colleagues were able to contribute to supply chain research by showing that network structural characteristics tended to impact positively on firm innovation. It is however imperative to note that the heterogeneity in event management business may require other networks whose structural characteristics may be different from those of supply networks.
Network Dynamics and Events Management Venture Performance
Network dynamics is embedded in the dynamic network theory perspective. Westaby, Pfaff, and Redding (2014) contend that the dynamics theory perspective seeks to explain the influence that social networks may be having on business outcomes such as goal achievement, learning, performance, and emotional attachment. Hakansson and Waluszewski (2004) argue that dynamics are the agents of change in business oriented networks. As a consequence, dynamics contribute to observed changes in relationships within the network. Larson and Starr (as cited in Hoang & Antoncic, 2003, p.175) posit that through network dynamics, networks can be viewed from the organizational formation, structural, and network evolution perspectives.
Although network dynamics has been credited with transformative impacts in terms of knowledge exchange and innovation (Clegg, Josserand, Mehra & Pitsis, 2016), evidence suggests that challenges exist in decoding network dynamics. Easton (as cited in Hsni-Hui & Zolkiewski, 2012, p. 247) notes that decoding network dynamics is made difficult by the challenge of delimiting network boundary for purposes of research. Moreover, it is also argued that the embeddedness and connectedness of networks makes it difficult to understand the context within which to situate network dynamics, and its causal effects (Ford & Hakansson, 2006).
The study sought to establish the effect of networking dimensions on the performance of events management ventures. Consequently, the researcher assumed the positivism paradigm that advocates for organized methods to discover and confirm a set of probabilistic causal laws useful in predicting patterns of human activity through precise empirical observations of individual behavior (Neuman, 2007). In view of this positivist position, the study adopted the confirmatory research design that is covariance based and focuses on the explanation of relationships among variables (Butler, 2014).
The study targeted events management ventures specializing in catering, cake baking, floral arrangements, event planning, hiring tents, chairs, furniture and public address systems. The study population comprised of Three hundred and thirteen (313) events ventures drawn from Kisumu, Nairobi, and Uasin Gishu Counties of Kenya. Bearing in mind the number of parameters under study, and the need to avoid overcorrection of standard errors (Yu & Muthen, 2002), a total of two hundred and eighty-eight (288) ventures were sampled. The study units were proprietors of events management ventures. Study units were first stratified according to the genre of events they specialized in. Simple random sampling using the random number approach was then used to select the required units from each genre.
A self-administered proprietors’ questionnaire comprising four sections consistent with the four latent variables under study was developed and used to collect the required information. Variables were operationalized and measured as indicated in Table 2.
Variable Definition and Measurement
|Endogenous (latent)||Financial performance (FP)
Customer performance (CP)
Learning and Growth (LG)
Internal business process (IB)
5-Very Large Extent
|Network structure||Exogenous (Latent)||
|Network capability||Exogenous (Latent)||
|Network dynamics||Exogenous (Latent)||
3- Moderately Agree
5- Strongly Agree
The proposed measurement model (Fig.1) consisted of four (4) latent variables; each measured using four indicators whose reliability took note of existence of possible random errors resulting from variable measurement, and depicted by the associated error terms. Observed variables (indicators) were each regressed into their respective latent variable.
Fig.1. Proposed Measurement Model
The measurement model was validated using Analysis of Moment Structures (AMOS) version 18, which has been found suitable for covariance-based structural equation models (Butler, 2014). The criterion for model evaluation was the ‘goodness of fit’. The essence was to find out how the hypothesized measurement model fitted the sample data. Consequently, three categories of fit indexes namely; absolute, incremental, and parsimony were used to test how good the model fitted the data. The overall fit of the model was achieved by comparing the default fit indices with the following recommended indices (Cheung & Rensvold, 2009).
The Structural Model
After validation of the measurement model, SEM was conducted on the structural model to test the formulated hypotheses (Fig. 2). The SEM path model was conceptualized to show that network dimensions impact directly on events management venture performance.
Fig.2. Proposed Structural Model
Validation of the structural model followed guidelines similar to the ones used to validate the measurement model. The model fit indices were obtained and compared with the recommended values. The model was then modified as suggested by the modification indices if needed. The path estimates (standardized regression weights) in the structural model and variance explained (R-square value) in each of the two endogenous variables were examined for causation and power
Analysis and Findings
Construct and Model Validation
Results of the Cronbach’s alpha reliability test presented in Table 3 indicate that all the questionnaire items developed for measuring the constructs in question had reliability coefficients above the recommended value of 0.7 (Butler, 2014). This indicates that the items were consistent in measuring the constructs.
Validation of the Measurement Model
An examination of the uni-dimensional requirements revealed that uni-dimensional was achieved for network capacity, network structure and network dynamics since all the factor loading were above 0.6 and were positive as recommended by Awang (2012) (see Fig. 3). In the case of venture performance, the FP and IB indicators had factor loading much less than 0.6. These indicators were therefore deleted from the structural model as suggested by Awang. Besides, the small values of the two indicators squared correlations suggest that for events management ventures, customer needs and learning and growth are the main indicators of performance.
Fig.3. Validated Measurement Model
Discriminant validity was achieved by eliminating any redundant items by using modification indices. Indicators such as FP and IB with high modification indices were deemed redundant and were therefore deleted from further analysis. Two indicators named customer performance (CP) and learning and growth (LG) were therefore segregated and used to measure venture performance.
Construct validity was analyzed by comparing default fitness indexes with recommended fit indices. Results shown in Table 4 indicate that the constructs in the measurement model attained construct validity with the default fitness indexes satisfying recommended values.
Fit Indexes for Measurement Model
|Fit Category||Name of Index||Level of Acceptance||Default value|
Validation of the Structural model
After establishing and confirming the measurement model, the next step involved validating the hypothesized structural model. Results of the analysis of moment structures of the initial structural model revealed that the chi-square p-value was below 0.05. However, the other fit indexes contravened the recommended values ((χ2 / df = 4.140; GFI = 0.848; AGFI = 0.793; NFI = 0.590; CFI = 0.647; TLI=0.582; RMSEA = 0.108) indicating that the initial model was a poor fit to the sample data.
In order to achieve a better model fit, the Post–hoc modification indices (MI) suggested that the model could be improved. The model was therefore modified by correlating error terms as suggested by the modification indices. The fit statistics still indicated a poor fit between the sample data and the modified model. Modification of the structural model was continued until the fit indices achieved acceptable levels. The final structural model was therefore as presented in figure 4.
Fig.4. Validated Final Structural Model
The final modified structural model fit indices indicated that the model was a good fit to the data analyzed (Table 5). The R-squared value of 0.82 indicates that the three exogenous variables explained up to 82% of the variation in venture performance.
Fit Indexes for Final Structural Model
|Fit Category||Name of Index||Level of Acceptance||Default value|
Results of hypotheses Testing
Hypothesis H01 postulated that networks capability has no significant effect on the performance of events management ventures. Regression weights shown in Table 6 indicate that network capability (NC) is a positive and significant predictor of venture performance (β = 0.451, p<0.05). The hypothesis that network capability has no significant effect on the performance of event management ventures was therefore not supported by the data. The standardized regression weight suggests that an increase of 1 standard deviation in network capability is likely to result in an increase of 0.451 standard deviations in venture performance.
Hypothesis H02 posited that network structure has no significant effect on the performance of events management ventures. The regression weights revealed that network structure was a positive and significant predictor of events management venture performance (β = 0.533, p<0.05). Consequently, the hypothesis that network structure has no significant effect on the performance of events management ventures was not supported. An increase of 1 standard deviation in network structure is likely to lead to a corresponding increase of 0.533 standard deviations in venture performance.
Hypothesis H03 postulated that network dynamics has no significant effect on events management venture performance. The regression weights shown in Table 6 revealed that network dynamics positively and significantly predicts the performance of events management ventures (β = 0.630, p<0.05). The hypothesis was not supported and the researcher concluded that an increase of 1 standard deviation in networks dynamics was likely to occasion an increase of 0.630 standard deviations in events venture performance.
Regression Weights (Default Model)
Discussions and Implications
The study findings evidently show that the network dimensions of capability, structure and dynamics influences performance of events management ventures directly and in a positive way. The findings reflect and support other findings reported in extant literature. For instance, the finding that network structure is a positive and significant predictor of venture performance is consistent with findings showing that network structure is a crucial competitive strategy that can be adopted by firms to enhance flow of resources (Yan, & Liu, 2012; Goce, 2009). This in essence implies that events management ventures stand to be more competitive if they invest in trust and partnership with potential competitors with a view to sharing resources.
The finding in the present study showing that network structure has a positive effect on venture performance however contradicts the finding by Teng (2007). According to Teng, partnerships are often complicated by lack of suitable partners and complexities in decision making. The contradiction could however be due to a difference in study contexts or difference in approaches and warrants further research on the area. Patner type also emerges as a potential source of contradiction in study findings. Oviatt and McDongall (2005) argue that establishing strong ties with friends has potential to improve venture performance. However, they caution that ties with relatives may not be beneficial since they often hang around entrepreneurs and may not provide innovative ideas.
Salaff et al, (2003) argue that strong ties among ventures with similar ethnicity provide the advantage of ease of access to business networks. Indeed, ethnic affiliation has become a common feature in Kenya with entrepreneurs hoping to capitalize on the ethnic network. The implication then is that despite network structure having a positive and significant impact on venture performance the type of partnership and strength of ties will no doubt define the structure of the network and its eventual effectiveness. It is therefore incumbent upon events management entrepreneurs to interrogate such considerations when forming networks.
The finding alluding to network capability as having a positive and significant effect on events management ventures lends support to others (Human & Nande 2009, Walter, et al 2005). According to Walter and Colleagues, network capability strengthens the impact of entrepreneurial orientation on spin-off performance among university spin-off firms. The finding in the present study therefore underscores the need for events management proprietors to focus more on network capabilities in order to enhance their performance. It has been documented that network capability at firm level promotes behaviour geared towards networks orientations and can support performance of a superior nature (Kale et al, 2002; Walter et al., 2005).
Implications drawn from the finding regarding network capability is that events management ventures in Kenya should look to draw upon their ability to develop and use inter-venture relationships for purpose of gaining competitive advantage. This can further be enhanced when ventures display open communication, hone their skills in coordination and relationships, and also increase their partner knowledge. Indeed, it has been argued that good partner knowledge and relationships enhances venture pro-activity (Kim & Aldrich, 2005).
The positive and significant relationship between network dynamics and venture performance provides a new front for looking at networks dynamics in relation to firm performance. It is imperative to note that little or no evidence exists extolling impacts of network dynamics as a network dimension on performance. On the contrary, existing studies have tended to address network dynamics in the realm of the dynamic interplay between network structures and rules of engagement (Dagnino, Levanti & Mocciaro Li Destri, 2016).
The finding then that network dynamics as a construct plays a crucial role in events venture performance supports findings which show that dynamics drive exchanges within business oriented networks (Hansson & Waluszewski, 2004). The researcher therefore contends that dynamic networks targeting associations, interactions and individuals or corporate responsibility have potential to maintain their network position and in consequence, increase information acquisition and access to complementary resources.