#IER-06-03-01-ppXXX--1985-Lin,Ayegba 2020, Vol. 6, No. 3 10.15678/IER.2020.0603.01 Mediating factors influencing the capacities of enterprise network performance Zhou Lu Lin, James Onuche Ayegba A B S T R A C T Objective: The study examined the mediating factors influencing the capacities and performance of enterprise network using some food and beverages enterprises in Lagos, Nigeria. Research Design & Methods: Primary source of data was employed in the study. The data collected from five hundred and thirty nine (539) respondents was analysed with the use of factor analysis which brings out the beauty and reality of the study. Six hun- dred and fifty seven (657) middle and top level management staff of six food enter- prises and beverage enterprises particularly the manufacturing ones through a purpos- ive sampling technique. Findings: When the original ten variables were analyzed, four variables were extracted from the analysis with eigenvalues greater or equal to 1, which explained 36.414 per- cent of the entire variance. Hence, the mediating factor influencing the capacities of enterprise network performance is Strategic Decision-Making Capacity (SDMC), Tech- nological Capacity (TC), Efficiency Enterprise Capacity (EEC), and Complexity in Technol- ogy or Technological Turbulence (TT). Contribution & Value Added: Finally, factors influencing the capacities of network per- formance are Strategic Decision Making Capacity (SDMC), Technological Capacity (TC), Efficiency Enterprise Capacity (EEC), and Complexity in Technology or Technological Turbulence (TT). The result of the study is essential because of its significant contribu- tion to the body of knowledge and literature regarding strategic management. Article type: research paper Keywords: mediating factors; capacities; enterprise network; performance; foods and beverages JEL codes: P24, P44, D22, D23, D2 Article received: 21 July 2020 Article accepted: 15 September 2020 Suggested citation: Lin, Z.L., & Ayegba, J.O. (2020). Mediating factors influencing the capacities of enterprise network performance. International Entrepreneurship Review (previously published as International Entrepreneurship | Przedsiębiorczość Międzynarodowa), 6(3), 7-19. https://doi.org/10.15678/IER.2020.0603.01 International Entrepreneurship Review RI E 8 | Zhou Lu Lin, James Onuche Ayegba INTRODUCTION The situation of business enterprises in the past years has been exceptional, transforma- tive, highly competitive, and in a highly technological environment which is tumultuous. These changes spring up because of technology dynamics, market dynamics, management dynamic, business operational dynamics (demand and supply ends), and others which have direct and indirect connotation on the performance of enterprises. Ahmad et al. (2014) noted that enterprises are facing these and more challenges because of the term “dynamics” which affects the overall business performance. Food enterprises and beverage enterprises are not exempted from the sweeping transformation as they have experienced increasing levels of competition during the last one decade, which create significant issues to maintaining economic balance. It was re- vealed in the study that there are more challenges posed which seems to be higher than enterprise survival. This challenge is experienced with the tendency at which enterprises thrive during the situation of increased competition. In fact, in the high level of competi- tion that is predominant among all enterprises and challenging situation of the Nigerian economy are crucial factors that rooted enterprise networks to be of essence in realizing enterprise revitalization (Zahra et al., 2006). Ahmad and Pirzada (2014) also discovered that enterprise networks have significant roles in bringing a level of buoyancy to a nation’s economic situations, which is laudable in fulfilling economic development. Because of this importance, there is a need for enter- prises to form a formidable network (Hashim et al., 2018), which will fortify them to stand against all economic odds (Zhu et al., 2013). The study was purposed to examine the me- diating factors influencing the capacities of enterprise network performance in some se- lected food and beverages industry in Lagos, Nigeria using factor analysis. LITERATURE REVIEW The position of enterprises in the world development cannot be far-fetched from or be- yond the major purpose of realizing economic development. Realizing the performance of enterprises, there is need for the enterprise owners themselves to form a formidable net- work. Obasi (2013) noted that enterprise networks are essential to achieve development and industrialization. For an enterprise to be transformed and rooted into the unforesee- able future, its capability to expand and broaden horizons for significant outputs must be on the key objectives if not as the organization aim. FMCGs and CMGs are sectors in Nigeria that have experienced and still experiencing various issues, such as reduced quality of products, less customer satisfaction on products, and many more which are caused as a result of economic imbalances, increasing compe- tition, importation of similar products, increasing petroleum prices, naira devaluation (In- dustrial Report, 2016). This has created a form of tumultuous environment for most en- terprises. Significant numbers of value creation and dynamic capabilities have be identi- fied in strategic management, among are strategic decision-making, product and knowledge creation, technological capability, product innovation capability, top managers alliance, and strategic flexibility (Ibidunni et al., 2014; Oghojafor et al., 2014; Zhang, 2007; Ibidunni & Inelo, 2004). Mediating factors influencing the capacities of enterprise network performance | 9 The enterprise networks must continuously develop dynamics to new resources that will enhance their sustenance in the unendingly environment (Eisenhardt & Martins, 2000; Oladele et al., 2010; Rindova & Kotha, 2001; Teece et al., 1997). The creation of value and competitive advantage becomes realistic when enterprises are able to form networks for making best use of resources, opportunities, and capabilities (Teece, 2009). This will en- hance highly competitive advantage among the group of enterprises that are able to team up for network. This is highly needed in the modern era (Chirico &Salvato, 2008). In the views of Rehman and Saeed (2015) and (Wong, 2013), dynamic capability is a form of facility for any organization to flourish in the present dynamic environment. Competitive advantages are secured when intensifying business sustainable options (Seung, 2014), and creating value (Hedvall et al., 2019; Hashim et al., 2018; Guo et al., 2018; Rodrigo-Alarcón et al., 2018; Oghojafor et al., 2014; Sherazi et al., 2013; Machirori & Fatoki, 2013) Ibidunni and Inelo (2004) noted that sooner, the managers and owners of enterprises will engage in strategic and innovative thinking to sustain the increasing business dynamics and to enhance success of enterprise networks. In the perspective of Eisenhardt et al. (2010), for enterprise to sustain in the ferocious competitive environment, there is a need to develop strategies that will enhance more customer demand, changing the legal frame- works and implementing more technology solutions (Shimizu & Hitt 2004). Studies of Stock & Wennberg (2009); Oluwale et al. (2013) have shown that product innovation is a value- addition activity for enterprises, and Kemper et al. (2013) noted that it has been an ap- proach for realizing competitive advantage. Though some in some studies, it was theoretically revealed that product innovation is a factor that determines enterprise survival (Damanpour & Wischnevsky, 2006; Daniel & Wilson, 2003; Damanpour & Gopalakrishnan, 1999; Stock& Wennberg, 2009). Rosen- buschet al. (2011) noted that empirical results of many studies are contradictory, espe- cially those that treated small and medium-sized enterprises (SMEs). Some empirical re- searches reveal a positive and significant nexus between product innovation and enter- prise survival (Alegre &Chiva, 2013, while other researches reveals negative nexus (Grewal & Tansuhaj (2001). From the result obtained in various studies, there were suggestions that other factors may be affecting the dynamic relationship that existed between inno- vation on products and the survival of enterprises. In view of this, there is thus a need to embark on the study based on selected foods and beverages enterprises. Based on this background, the study is set to examine the factors mediating between the influencing the capacities of enterprise network performance employing some selected foods and bever- ages enterprises in Lagos, Nigeria. The research covers 6 foods and beverage enterprises that are quoted in the Nigeria Stock Exchange (NSE) of the manufacturing sector. This study was carried out because of the alarm- ing report of the Manufacturing Association of Nigeria (MAN) that about 60 percent of the manufacturing company in Nigeria is not functioning well, while 30 percent have gone on co- matose, and only 10 percent are operating at a sustainable level (Olamade et al., 2013). Food and beverage sector was among the large-scale quoted food and beverages enterprises, from which this study select six (Honeywell Flour Mills Nigeria, 7-Up Bottling Company, Nestle Ni- geria, Flour Mills Nigeria, Dangote Flour Mill Nigeria, and Unilever Nigeria) in Lagos State, Ni- geria. All are listed in the Nigeria Stock Exchange (NSE). The study will cover Lagos state be- cause the state is currently the industrial, commercial and financial hub of Nigeria. 10 | Zhou Lu Lin, James Onuche Ayegba MATERIAL AND METHODS This section critically elucidates the approaches that were adopted in realizing the aim of this research. This study will employ quantitative approach that entails a form of survey research as research design for the purpose of exploring the observable fact, and presents a well robust explanation to the identified problems that the study seeks to address. Research Design This study adopts a survey research design which will assist in pointing out challenges and managerial dynamics and issues relating to operations in Food and Beverages sector particularly in Nigerian in addition to considering the dimensions of enterprise networks in the form of enterprise supplier interactions, enterprise customer connections and en- terprise competitors’ interactions, and to moderate the consequence of environmental dynamics in the connection between Dynamic Capacities and Enterprise Networks on Company’s Performance. Sampling The sample for this study was achieved based on the 14 companies that were listed in the Nigerian Stock Exchange (NSE) bulletin of 2014 as indigenous and multinational enter- prises. Among the 14 companies, 6 companies were tagged as foods and beverages enter- prises, but 6 enterprises will be selected for this study because of the ease of getting in- formation as earlier explained by (Udemba, 2015; Akpan et al., 2016). According to Zikmund (2003), the various error allowances was determined and the suitable one was chosen based on the discretion of the researcher. The chosen error al- lowance of 0.04 was employed to establish the sample size as shown in the equation: n = Z2/4E2; n is denoted as the Sample size; Z is denoted as the Z score (confidence interval which is 2.05; E is denoted as the Error allowance which is 0.04. Based on the sample size formula, the number of sample size was 656.6406, which is approximately 657. On this note, 657 questionnaires will be distributed to respondents whom are middle and top managers in the listed foods and beverage companies. Regarding the recommendations of the sample size for factor analysis, the recommen- dations are more often than not stated with respect to either the least sample size (N) or the least ratio of N to the number of variables, i.e., the number of survey items that is being subjected to factor analysis (Adeniran, Stephens & Akinsehinwa, 2020). Gorsuch (1983) recommended a minimum of 100 sample size for factor analysis, Guilford (1954) argued that sample size should be at least 200, while Cattell et al. (1970) made recom- mendation with a minimum of 250 number of sample size. Also, the following guidance was provided with respect to the determination of sample size adequacy Comrey and Lee (1992), such that the sample size of hundred is poor; sample size of two hundred is fair; sample size of three hundred is good; sample size of five hundred is very good; and sample size of one thousand or more is excellent. In this study, the sample size of six hundred and fifty seven (657) is adequate for factor analysis and reporting as rooted in earlier studies. Mediating factors influencing the capacities of enterprise network performance | 11 Primary data was extracted through a structured questionnaire that was distributed to top and middle management officers responsible for the strategic decision and direc- tion of the companies. This study employed close-ended questions of Likert type five points scale which was modified Model Specification for Factor Analysis Adeniran et al. (2020) posits that in the situation where by the observed variables are X1, X2…. Xn, the dominant factors are F1, F2…Fm and the exclusive factors are U1, U2…Un, the variables may be expressed as linear functions of the factors: X1= a11F1+ a12F2+ a13F3+ … + a1mFm+ a1U1 X2= a21F1+ a22F2+ a23F3+ … + a2mFm+ a2U2 … Xn = an1F1+ an2F2+ an3F3+ … + anmFm+ anUn Every equation that is represented is known to be a regression equation; the coeffi- cients a11, a12…anm were identified with factor analysis which suitably replicated the ob- served variables from the factors. RESULTS The targeted participants in the investigation were approved to voluntarily take part in the exercise. In furtherance to that, the aim of the study was made comprehensible to them. Intensive and joint effort was ensured to realize confidentiality, secrecy and anonymity of information given by the respondents; also, they were assured that all information elicited from them was used solely for the rationale of this work. Research assistants were edu- cated regarding the etiquettes in research for the purpose of ensuring absolute compli- ance to research ethics during the process of conducting the study. The following were limitations encountered by the researcher on the acceptance of the methodology: difficulty in data gathering from some of the enterprises however, it was achievable with the help of some staff; generally, the core assumptions of multiple regressions are quite limited because of the presence of normality in the stochastic dis- turbance terms (error terms), the presence of multicollinearity and homoscedasticity between any pair of independent variables and the error terms could result to unau- thentic result which is the reason for making provision for stochastic disturbance term or error term or residual in the model; and hierarchical regression analysis is employed to determine the effect of enterprise networks on the performance of the company through a reconciling task of dynamic capacities, which is made doable because of the inadequate quantification of variables; it may not be apposite if the study seeks to ex- amine more complex relationship. From the sample size calculated to about six hundred and fifty-seven (657) which equals to the total copies of questionnaires administered by the researcher targeted at the middle managerial staff and the top managerial staff of particular beverages and food enterprises in Lagos State, five hundred and thirty-nine (539) which is about 82 percent copies of questionnaire were valid and returned for data analysis and reporting. The re- maining (118) copies of questionnaire were not used in the data analysis because of dif- ferent invalidity issues. Hence, all the valid questionnaires returned were processed for data analysis, and the response was revealed in Table 1. 12 | Zhou Lu Lin, James Onuche Ayegba Table 1. Response rate of respondents Questionnaire Frequency Percentage Administered 657 Returned 539 82 Not Returned 118 18 Source: Field Survey (2020). Fadare and Adeniran (2018) posit that a response rate of fifty percent and above re- garding the copies of questionnaire returned is appropriate for data analysis. Hence, the response rate of eighty-two (82) percent copies of questionnaire returned for this study is appropriate for establishing data analysis. Factor analysis was adopted to achieve this aim. Several variables have been em- ployed to explicate the complex interconnections and interrelationships of variables. In this regard, the few fundamental variables that are germane to this study remained to be determined. The systematic relationships among these established variables are pre- sented on rank order scales data (Nimalathasan, 2009). Factor analysis is a technique adopted in achieving statistical analysis. It belongs to the family of General Linear Model (GLM) procedures. It is designed to inform the essential structure for understanding a phenomenon (Spearman, 1904). Factor analysis entails the following such as correlation matrix, communality, eigenvalues, factor rotation, factor loadings, entire variance explained and others. Table 2 below shows the correlation matrix to identify the direction, and degree of rela- tionships between the variables on five point Likert scales. Correlation matrix in Table 2 re- vealed the interconnection between the ten (10) variables: Innovation Product Capacity (IPC), Sales Growth Capacity (SGC), Strategic Decision Making Capacity (SDMC), Enterprise Survival Capacity (ESC), Efficiency Enterprise Capacity (EEC), Technological Capacity (TC), Strategic Flexibility Capacity (SFC), Competitive Advantage (CA), Competitive Intensity (CI), and Complexity in Technology or Technological Turbulence (TT). The interconnection be- tween SGC and IPC is very strong and positive at 0.840. The interconnection between ESC and SDMC is very strong and positive at 0.803. The interconnection between TC and EEC is weak and positive at 0.288. The interconnection between CA and SFC is very strong and pos- itive at 0.866. The interconnection between CI and SDMC, CI and ESC are very strong and positive at 0.769 and 0.953 respectively. The interconnection between TT and SDMC, TT and ESC, TT and CI are very strong and positive at 0.791, 0.927, and 0.886 respectively. It is pertinent to note that there are suspects of multicolinearity (high correlations) between the following interconnected variables: CI and ESC, TT and ESC which correlation values are more than 0.85. Table 3 depicts the Kaiser Meyer Olkin (KMO) test. According to Adeniran & Olorunfemi (2019), Kaiser Meyer Olkin (KMO) is used to determine the level of numerical adequacy of factor analysis that is supposed to be carried out. The determination of KMO that is between 0.9 and 0.8 is excellent, KMO that is between 0.7 and 0.6 is very good, KMO that is between 0.6 and 0.5 is good, From Table 3, the KMO result of 0.685 is very good and acceptable for performing factor analysis, and it is signif- icant at 0.000 which implies that the data do not generate an identity matrix, the data is normal, suitable and acceptable multivariate for factor analysis. Mediating factors influencing the capacities of enterprise network performance | 13 Table 2. Correlation Matrix Correlation Matrixa IPC SGC SDMC ESC EEC TC SFC CA CI TT C o rr e la ti o n IPC 1.000 0.840 -0.023 0.016 0.018 -0.095 0.031 -0.002 0.038 -0.025 SGC 1.000 -0.051 -0.069 0.072 -0.044 -0.008 -0.005 -0.056 -0.105 SDMC 1.000 0.803 0.183 0.052 -0.012 -0.027 0.769 0.791 ESC 1.000 0.193 0.071 0.032 0.005 0.953 0.927 EEC 1.000 0.288 0.182 0.085 0.199 0.164 TC 1.000 0.145 0.102 0.066 0.090 SFC 1.000 0.866 0.007 -0.007 CA 1.000 0.001 -0.019 CI 1.000 0.886 TT 1.000 Source: SPSS Version 20 (2020). Table 3. KMO and Bartlett’s Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.685 Bartlett's Test of Sphericity Approx. Chi-Square 4486.783 Df 45 Sig. 0.000 Source: SPSS Version 20 (2020). Table 4 depict communality through Principal Component Analysis (PCA). Commu- nality is the percentage measure of a variable’s variation that is being explained by the factors. It is the portion of variance that an initial variable shared with the other varia- bles that is entailed in the analysis. From Table 4, four factors that were identified are SDMC (1), TC (2), EEC (3), and TT (4). The Technological Turbulence (TT) however shows a sign of multicolinearity. Table 5 revealed that the four variables account for 36.414 percent variance explained. Table 4. Communalities Variables Initial Extraction Hierarchy IPC 1.000 0.920 SGC 1.000 0.920 SDMC 1.000 0.785 1 ESC 1.000 0.954 EEC 1.000 0.637 3 TC 1.000 0.683 2 SFC 1.000 0.935 CA 1.000 0.935 CI 1.000 0.918 TT 1.000 0.912 4 Note: Extraction Method: Principal Component Analysis. Source: SPSS Version 20 (2020) 14 | Zhou Lu Lin, James Onuche Ayegba Table 5. Entire variance Explained C o m p o n e n t Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings T o ta l % o f V a ri a n c e C u m u la ti v e % T o ta l % o f V a ri a n c e C u m u la ti v e % T o ta l % o f V a ri a n c e C u m u la ti v e % 1 3.641 36.414 36.414 3.641 36.414 36.414 3.579 35.788 35.788 2 1.957 19.570 55.984 1.957 19.570 55.984 1.865 18.650 54.438 3 1.850 18.495 74.479 1.850 18.495 74.479 1.854 18.538 72.976 4 1.152 11.522 86.001 1.152 11.522 86.001 1.303 13.025 86.001 5 0.693 6.928 92.929 6 0.284 2.842 95.771 7 0.160 1.595 97.366 8 0.118 1.176 98.542 9 0.109 1.093 99.635 10 0.037 0.365 100.000 Note: Extraction Method: Principal Component Analysis. Source: SPSS Version 20 (2020) When the original ten variables were analysed, four variables were extracted from the analysis with eigenvalues greater or equal to 1, which accounted for 36.414 percent of the entire variance. Hence, the mediating factor influencing the capacities of enter- prise network performance are Strategic Decision Making Capacity (SDMC), Technolog- ical Capacity (TC), Efficiency Enterprise Capacity (EEC), and Complexity in Technology or Technological Turbulence (TT). This study is corroborates the findings of Ibidunni et al. (2014); Jegede et al. (2012); Mohd et al. (2013); Olamade et al. (2013); Obembe et al. (2014) which discover one or more variables as mediating factor influencing the capacities of enterprise network per- formance. Also, it is also in connection with other studies that were earlier carried out in Europe, Asia, and America. For example, Strategic Decision Making Capacity (SDMC), Tech- nological Capacity (TC), Efficiency Enterprise Capacity (EEC), and Complexity in Technology or Technological Turbulence (TT) in Malaysia can be properly traced out in the effect of government policy framework for improving business as it is circumspectly considered for enterprises (Rasiah, 2002). There seems to be focus on innovation policy in the studies conducted in the U.K. (Foreman-Peck, 2013). This is the same with the studies in Brazil on the impact of development policy on enterprises’ performance (Garoneet al., 2015). CONCLUSIONS The study examined the mediating factors influencing the capacities of enterprise net-work performance of food and beverages enterprises in Lagos, Nigeria. Six hundred and fifty seven (657) middle and top level management staff of six food enterprises and beverage enterprises particularly the manufacturing ones through a purposive sampling technique. The study made Mediating factors influencing the capacities of enterprise network performance | 15 use of primary source of data. The data collected from five hundred and thirty nine (539) re- spondents was analysed with the use of factor analysis which brings out the beauty and reality of the study. From the study, Statistical Package for Social Sciences (SPSS) version 23 was used alongside Excel (Window 10) to code, compute, and process the data. When the original ten variables were analysed, four variables were extracted from the analysis with eigenvalues greater or equal to 1, which accounted for 36.414 percent of the entire variance. Hence, the mediating factor influencing the capacities of enterprise net- work performance are Strategic Decision Making Capacity (SDMC), Technological Capacity (TC), Efficiency Enterprise Capacity (EEC), and Complexity in Technology or Technological Turbulence (TT). Finally, factor analysis revealed that the mediating factor influencing the capacities of enterprise network performance are Strategic Decision Making Capacity (SDMC), Technological Capacity (TC), Efficiency Enterprise Capacity (EEC), and Complexity in Technology or Technological Turbulence (TT). Policy Implications: The result that stemmed out of the study is essential because of its significant contribution to the body of knowledge and literature regarding strategic management. Finally, From the findings, recommendations were suggested that among the mediating factors examined on enterprise network capacities, factor analysis revealed that the mediating factor influencing the capacities of enterprise network performance are Strategic Decision Making Capacity (SDMC), Technological Capacity (TC), Efficiency En- terprise Capacity (EEC), and Complexity in Technology or Technological Turbulence (TT). It is therefore essential for the management of enterprises to prioritize those indicators for realizing better performance. Suggestions for Further Research: Since this study is limited to ten variables, and six food and beverage enterprises across Lagos, Nigeria, future studies may consider more variables that will be more robust for factor analysis. Also, samples may be drawn from enter-prises across south-western states. Comparative analysis with other countries may be conducted by the researcher in future studies. It may also be conducted by researchers that want to imitate the study. Research Limitations: Since this study is limited to ten variables, and six food and bev- erage enterprises in Lagos. These enterprises were among the large-scale quoted food and beverages enterprises (Honeywell Flour Mills Nigeria, 7-Up Bottling Company, Nestle Ni- geria, Flour Mills Nigeria., Dangote Flour Mill Nigeria, and Unilever Nigeria) in Lagos State, Nigeria. 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Thompson Learning/Southwest- ern, Cincinnati, OH. Mediating factors influencing the capacities of enterprise network performance | 19 Authors The contribution share of authors is equal and amounted to 50% each of them. Zhou Lu Lin PhD holder in Economics and lecturer at the Department of Health and Economics Management Science and Engineering, Faculty of Management and Finance Professor. Zhou, specializes in Health Economics and international, economics and actively lectures and researches since 2001. Correspondence to: Department of Health and Economics Management Science and Engineer- ing, Faculty of Management and Finance, Jiangsu University, Zhenjiang Jiangsu Province Post Code 212013 China; e-mail: zll62@ujs.edu.cn ORCID http://orcid.org/0000-0002-5266-2191 James Onuche Ayegba M.B.A holder in management and PhD candidate in Department of Managements, Faculty of Management and Finance, Jiangsu University, Zhenjiang, Jiangsu province. He specializes in small and medium-sized enterprise (SME) and economics and actively lectures as graduate teaching assistant (GTA) and researches since 2017. Correspondence to: Department of Marketing, Faculty of Management science and engineering, Jiangsu University, Zhenjiang Jiangsu province 212013 China; e-mail: 5103150216@stmail.ujs.edu.cn ORCID http://orcid.org/0000-0001-6127-4353 Acknowledgements and Financial Disclosure The authors would like to thank the anonymous reviewers for their valuable reviews, which have improved the quality of this paper the reviewers for constructive comments that contributed to the robustness of the final draft of the research article. Copyright and License This article is published under the terms of the Creative Commons Attribution – NoDerivs (CC BY-ND 4.0) License http://creativecommons.org/licenses/by-nd/4.0/ Published by Cracow University of Economics – Krakow, Poland The journal is co-financed in the years 2019-2020 by the Ministry of Sci- ence and Higher Education of the Republic of Poland in the framework of ministerial programme “Support for Scientific Journals” (WCN) on the basis of contract no. 238/WCN/2019/1 concluded on 15 August 2019. 20 | Zhou Lu Lin, James Onuche Ayegba