Impact of Dynamic Capacities on the Performance of Food and Beverage Enterprises in Lagos, Nigeria

doi:10.21203/rs.3.rs-25941/v2

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Impact of Dynamic Capacities on the Performance of Food and Beverage Enterprises in Lagos, Nigeria

https://doi.org/10.21203/rs.3.rs-25941/v2

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This study examined the impact of dynamic capacities on the performance of food and beverage enterprise in Lagos, Nigeria. The following sub-variables (strategic decision-making capacity, product innovation capacity, strategic flexibility, competitive intensity, technological turbulence, and technological capability) were employed to represent the variable of dynamic capacity. Also, the following sub-variables (sales growth, enterprise survival, enterprise efficiency and competitive advantage) were employed to represent the variable of enterprise performance. Primary data was used to achieve descriptive and inferential statistics, and the statistics is estimated by PLS-SEM method which was calibrated on Lisrel 8.70 software.

This study found that product innovation, competitive intensity and technological turbulence, technological capability and competitive intensity, and strategic flexibility are critical sub-variables in determining the robustness of dynamic capacities, as they adequately improve increasing sales growth, survival and sustenance of enterprise into the unforeseeable future, efficiency of enterprise, and competitive advantage of food and beverage manufacturing enterprises respectively, particularly in this trying period that is evidenced with technological change, competition, among others.

Entrepreneurship

dynamic capacities

enterprise

performance

SEM

It was revealed in the study that product innovation is the singular variable that can adequately improve increasing sales growth.
Competitive intensity and Technological turbulence are the dual variables that can adequately improve the survival and sustenance of enterprise into the unforeseeable future.
Technological capability and Competitive intensity are the dual variable that could improve the efficiency of enterprise.
Strategic flexibility is the singular variable that could adequately improve competitive advantage of food and beverage enterprises.

Over the years, business enterprises have been witnessing unparalleled and exceptional degree of transformation, conversion, intense and forceful competition, and tumultuous environment globally. These transformations emanate as a result of market fragmentation, changing technology, dynamic management, the convergence of different business enterprises, supplier attitudes, changing customer demands, changing customer expectations, reduced lifecycle of product, etc. which have direct and indirect implication on business performance. According to Ahmad, Othman and Lazim (2014), business enterprises are encountering significant challenges because of the dynamic attributes of business enterprise itself, the market, and environmental circumstances. In fact, in the current and widespread competition and challenging economic circumstances, a practicable, feasible, dynamic, and forceful enterprise networks is vital to the economic improvement of a particular country (Zahra, Sapienza and Davidsson, 2006). In the same vein, Ahmad and Pirzada (2014) noted that enterprise networks are known to be the mechanism of growth and development which has significant roles in the establishment of laudable economic development. In the study of Hashim, Raza and Minai (2018), it was revealed that majority of the countries often reply on the performance of enterprise networks for the growth and fortification of the economy. Zhu, Cordeiro and Sarkis (2013) noted that however, enterprise networks are faced with many challenges and uncertainties in the competitive business environment and inauspicious circumstances in recent years. These have resulted into the challenges of superior and improved business performance.

According to Kayode (1989) enterprise networks are the heart of any economy. Its importance to the growth and survival of the economy cannot be overemphasized. Enterprise networks play a key and noticeable role in the economy of most countries. In the study, it was stated that the proceeds are oftentimes products offered to customers that result into profit making when pooled together by the owners of enterprises networks. Obisi (2013) in his study added that the enterprise networks all over the world would continue to be the engine of development and industrialization.

Nigeria is among the developing nations in the world that is competing in the intensity of global market particularly in food and beverage manufacturing, and as a result of that, many business enterprises are springing up, especially those associated with large organizations. However, they need to maintain and catalyse local market for economic stability and potency. It should be of importance to note that the survival of any enterprise into the unforeseeable future, its ability to extend tentacles, and its tendency of producing significant outputs is the goal of any enterprise in particular.

The ecosystem of enterprises in Nigeria is quite competitive and challenging, and the enterprise environment is neither perfect nor encouraging for small enterprises (Beugré, 2016). Small network of enterprises has to implement the significant strategy of resources available to ensure the sustainable growth of enterprise networks in a difficult and competitive environment. The hostile conditions give confidence to the enterprise networks for the purpose of surmounting the injurious business environment when realizing sustainable intensification of enterprises (Messeni, Petruzzelli, Ardito and Savino, 2018).

Springing from the potential breakthrough in the African market with Nigeria at the core stage, several manufacturing enterprises most especially the food and beverage manufacturing enterprises manufacturing are well-known are giving room for forceful competition for precincts, market share and clients. The food and beverage manufacturing enterprises enterprise that are present and domesticated in the FMCGs sector has encountered different challenges from the time when there was economic meltdown, which is in furtherance to powerful, ferocious and increasing competition in the business enterprise and other macroeconomic indices such as decreasing oil prices, devaluing Naira currency, workers’ salaries that are not paid, all these result in reduced spending (Industrial Report, 2016).

At the global level, food and beverage manufacturing enterprises are known to be the bedrock and engine of nation’s development. According to Okere (2012), the food and beverage manufacturing enterprises are noted to be the main producers of foods and beverages that are consumed in Nigeria and the biggest sub-sector in the Nigerian manufacturing that were placed on the Nigerian Stock Exchange. Osundina (2014) further revealed that the enterprise is a dynamic and growing subsector of the Nigerian manufacturing industry which is witnessing unbendable and ferocious competition (KPMG, 2015).

According to Akpan, Ikon, Chukwunonye and Nneka (2016), the frequent and persistent changing of Nigerian food and beverage manufacturing enterprises opens both opportunities and threats. In fact, the overall lapses of the food and beverage manufacturer are the pitiable operating environment in Nigeria and the high cost of production and operation. This has resulted to reduced output when compared with their counterparts in other developing nations. All these challenging issues have grave impacts on the performance of food and beverage manufacturing enterprises.

In the view of Sola, Obamuyi, Adekunjo, and Ogunleye (2013), varieties of approaches have been adopted by the Nigerian government towards the improvement of efficiency, productivity, and output of the enterprise networks so as to improve economic growth and development. In the report of CBN (2003), the import substitution industrialization strategy took off and was adopted by the Nigerian nation during the First National Development Plan (1962-1968) with the aim of plunging the degree of finished products that were imported and the improvement of foreign exchange savings by producing locally some of the imported consumer goods. In the same vein, the Second National Development Plan period (1970-1974) signifies the consolidation of Nigeria’s import substitution industrialization strategy during the era of oil boom. Sola et al., (2013) noted that during the wake of world oil market that was collapsed in the beginning of 1980s, there was a rigorous/severe decline in the earnings accrued from oil exportation, which further lead to the inability of the nation to uphold the emerged import-dependent industrial arrangement because of the enormous import bills.

In order to rescue the abovementioned economic challenges, a variety of policy measures in terms of context and contents were employed, which seems unsuccessful. Among the policy measures were the 1982 stabilization policy, the 1984 restrictive monetary policy and stringent exchange control. The collapse of the policy measures resulted to the acceptance and implementation of 1986 Structural Adjustment Programme (SAP) (CBN, 2003). In order to reduce the over reliance of the nation’s economy on crude oil which is the major foreign means of getting income, SAP was established by endorsing non-oil exports, most especially the enterprise networks. Sola et al. (2013) noted that the performance of the enterprise networks has been worrying the government in spite of a variety of efforts they were exerted.

Accordingly, the Federal Government of Nigeria (FGN) begin with an economic programme referred to as NEEDS (National Economic Empowerment and Development Strategy) in 2003, this was to promote private sector participation in growth strategies (Essien and Bello, 2007). Though the policy document of NEEDS tends to be more entrepreneurial, the idea was basically useful for the large-scale industries. Additionally, it was identified in the policy document that there was “ineffective nexus between industry and the research institute/universities” and “lack of engineering and technical capacity to translate and decode research results into finished products and maintain existing machinery as well as low level of entrepreneurial capacity, technological support, and paucity of trained artisan skills”. These are major impediments to the development of enterprise networks (Essien and Bello, 2007). The document was proffering solution to the shortage of technological capabilities associated with enterprise networks which are needed to influence the level efficiency.

The performance of enterprises in their business networks is quite affected by several factors such as reduced sales, high cost of production, reduced capital utilization, shortage of foreign exchange to procure the needed inputs, pitiable and unstable power supply, reduced quality of goods and services, incessant taxation among others (Adeoye and Elegunde, 2012). Among other issues associated with enterprise networks are high import dependency, political instability, deceitful governance, and political bias with resource distribution, decentralization practices, high cost of funds, weak, defect, and unsound policies formulation and implementation, high level of fake and counterfeited goods that are imported, micro-economic instability, deformed business atmosphere, invisible governance, etc. According to Olamade, Oyebisi and Egbetokun, (2013), the penalties of these issues on the national economy include among others, the loss of enterprises ‘pull effect’ on other sectors of the economy, and the loss of chances to partake in global economy when participating in the value chains at international level.

In many of the enterprise networks, there is an understanding that the world’s economy is passing through a period of colossal transformation alongside improbability that is full of uncertainties. In fact, incremental transformations are activated in many of the cost structure, supply chains and business models of enterprises (Dobbs, 2012). One of the chief issues that are often encountered in enterprise networks is the dimension of creating value and achieving competitive advantage in the respective industry sector. These concepts of value creation and competitive advantage are germane to business strategy. The quest and reality of creating value and competitive advantage are at the basis of organizational performance and hence the indulgence of sources to the sustenance of value creation and competitive advantage is now a key and noticeable area and dimension of study when it comes to strategic management (Barney, 1991; Porter, 1991).

Global competition has revealed the level of technological changes and the dynamics of customers demand for superior quality products/services at reduced prices (Dirisu, Iyiola and Ibidunni, 2013). As competitive advantage is becoming less valuable, the performance of Nigerian enterprise networks is highly influenced as many networks are facing drastic and unexpected transformations, emanating from technological advancement, change in customer demand, new regulations and diffusion of new practices (Wilden, Gudergen and Lings, 2007; Helfat, Finkelstein, Mitchell, Peteraf, Singh, Teece, and Winter, 2007; Rhema and Saeed, 2015; Akpan, 2016). It is obvious in the literature review and practices of strategic management that meagre control of resources and capabilities are insufficient to maintain competitive advantage.

The tumultuous nature of enterprise networks’ environment results to a large number of new challenges which must be examined with the use of dynamic capabilities. Attention must be centred on the inimitable resources that may lose their inimitability in the dynamic environment. Consequently, uninterrupted organizational rejuvenation recommends only valuable and effectual mechanism for competitive advantage by erecting on dynamic capabilities. A number of values created and related to dynamic capabilities have become known in strategic management, such as product development, strategic decision-making, knowledge creation, product innovation capability, technological capability, top managers capability, alliance and acquisition and strategic flexibility (Zhang, 2007; Ibidunni, Iyiola and Ibidunni, 2014; Oghojafor, Owolabi, Ogunkoya and Shobayo, 2014).

In order to sustain and thrive in the unendingly dynamic and changing environment, the enterprise networks must constantly develop new resources and capabilities that will deal with the new demands (Teece et al., 1997; Eisenhardt and Martins, 2000; Rindova and Kotha, 2001). The dynamic enterprise network environment with its capricious fast changing environments (internal and external) provides privileges for growth and development, and value/ wealth creation wealth which often poses some level or form of threats to the most networks (Obiwuru, Oluwalaye and Okwu, 2011). The view on dynamic capabilities has risen to deal with the rigorous issue militating against the sustainable competitive advantage in the dynamic environment (Teece, Pisano and Shuen, 1997; Eisenhardt and Martin, 2000).

The formation, rations and victuals of value created and competitive advantage is realized when enterprise networks are able to recognize new and further opportunities, resources and capabilities that are in line with recognized opportunities and change (Teece, 2009). The dominant competitions have obligated several enterprise networks for new approaches to arrive at a competitive edge. What was previously referred to as strategies has been modified in the modern era (Chirico and Salvato, 2008). Dynamic capability, according to Rehman and Saeed (2015) is a mainstay for any organization to thrive in the present dynamic environment. Enterprises that are responsive in product innovation and capability to effectively coordinate and redeploy competencies from within and without would be able to improve business performance. Such enterprises will have the power to build, join together, and renew their competencies to acclimatize to the transforming market needs (Wong, 2013).

Enterprise networks place more emphasis on the aim of providing and securing competitive advantages by reaching sustainable business intensification (Seung, 2014), and creating value. It is unfortunate that enterprise networks often face high level and predominant collapse worldwide. Past literatures revealed that about 40 percent of enterprises often encounter collapse during the first two years of start-ups in different countries of the world (Hashim, Raza and Minai 2018). In the same vein, scholars have emphatically argued that the rate of failure or collapse of small network enterprise is much higher in the developing countries than in the developed countries (Sherazi, Iqbal, Asif, Rehman and Shah, 2013).

Enterprise networks are strategic resources that are essential for permitting the growth of businesses in a dynamic and competitive business environment (Machirori and Fatoki, 2013). The authors further states that enterprise networks consist of small business owners and with other businesses to acquire and share information and resources. Enterprise networks according to Hedvall, Jagstedt and Dubois (2019); Guo, Xu, Tang, Liu-Thompkins, Guo and Dong (2018) are set up to strengthen their dynamic capabilities in a competitive business environment. The survival of enterprises depends on the performance of enterprises which could be strengthening as a result of cordial networks that is associated with its economic growth (Rodrigo-Alarcón, García-Villaverde, Ruiz-Ortega and Parra-Requena, 2018).

In order to deal with the drastic transformations in market dynamics, technologies, and competition, top managers mostly rely on the strategic decision-making ability to deal with the changing external factors that could aid the survival of transforming environment. Enterprise networks have been able to accomplish competitive advantage over time in the course of embracing technological innovation as a strategic drive to realizing competitive advantage and creating value (Oghojaforet al., 2014). In due course, enterprise owners and managers will make strategic decisions and engage in thoughts that are innovative so as to cope with the changing dynamics of environment to realize the successfulness of enterprise networks (Ibidunni and Inelo, 2015).

According to Dreyer and Gronhaug (2004), in order for enterprise networks in a fiercely competitive environment to operate effectively in an increasingly complex environment, growing demands from customers, changes in regulatory frameworks and technological encroachment, will catalyse strategic managers to be flexible in dealings, most especially in a complex contemporary business environment that requires a short product life cycles, swift changing preferences and increasing demand of customers, technological progression and others (Shimizu and Hitt 2004).

For this reason, it is against this background that this study examined the impact of dynamic capacities on enterprise performance using food enterprises and beverage enterprises in Lagos, Nigeria as case studies. It is pertinent to note that the interconnections between the attributes or constructs of dynamic capacities were tested on the attributes of enterprise performance. In this study, dynamic capacities were also captured with respect to strategic decision-making capacity, product innovation capacity, strategic flexibility, competitive intensity, technological turbulence, and technological capability. The performance of enterprise was captured with sales growth, enterprise survival, enterprise efficiency and competitive advantage.

Dynamic Capacities

The study was tending to determine the connection between dynamic capacities and enterprise performance. Dynamic capacities make organizations capable of such innovation by planning out suitable measures and finding out their effect on the accomplishment of the enterprise’s goals (Sola et al., 2013). In the global world of competition, sustainable competitive advantage has been found to be of great importance and need in both marketing and strategic management of a business. Other systems are being focussed on other works conducted on management and marketing of companies that tend to prove the worth of dynamic capacities within the enterprise (Ali et al.,2010). Propositions in regard to the use of DC in enterprises with respect to environmental dynamism, product innovation, and enterprise performance are being developed continually toward achievement of the enterprise’s goals.(James and Zhou, 2020).

DC facilitate a enterprise in its improved performances, its innovativeness in terms of enterprise networking, products and advanced use of technology as well as prepare a enterprise to survive in the ever-changing business environment. Based on the advantages of DC in management of any organization, different propositions are developing towards the greater understanding of the needs to integrate dynamic capacities in organizations for the betterment of performances. (Zahra and Hayton, 2008): Ayegba and Lin, (2020).

Dynamic capacities have also been found to be a pertinent part in the product innovations and advanced uses of technology in different organizations. It can be realized that the focuses of dynamic capability on the changing needs of both the company as well as its customers and prepares an enterprise accordingly to face the challenges encountered due to the changing business environment. In other words, the concepts of DC help enterprises to adapt to the changes in the business environment. When dynamic capacities (DC) are involved in product technological and innovation changes, the enterprises also get assisted through the solutions available to technical problems and implementation of new processes and techniques (Thiel, 2010).

Environment

The greater need and importance of dynamic capacities arise owing to the constant changes that are prevailing in the business environment. Marketing capacities have been found to have a great positive influence on the economic performance of an organization in the global market. Kuo-Wei and Kai-Ping (2012) conducted a study on the significance of the dynamic capacities as observed in relation to company performances, enterprise networking and accomplishments of objectives and goals, it may be drawn as a conclusion that DC have a major role to play in the organizational management where the managers and leaders are facilitated in their decision making towards organizational success. DC can be observed as focusing on various aspects of an enterprise dealing with technological advances and product innovations, handling and facing challenges from the changing business environment as well as improving the performance of the enterprise as a whole.

The development of different propositions also reflects the increasing importance of dynamic capacities in organizations that managers are integrating in their strategic management practices all the more from before. While it can be realized that negative effects may occur on the performances and success of a company, this study reveals that the availability of dynamic capacities imply that a enterprise may be protected from the negative impacts through planned measures strategically intended to uncertainties and encounter challenges and yet perform to the utmost level and succeed in achieving organizational objectives and goals.

Performance

Earlier studies such as van Duren et al. (2003) noted that specific managerial factors were pointed by managers to influence enterprise profitability. Schumacher and Boland (2005); Pendell and Boland (2005) revealed that enterprise resources are dominant in explaining enterprise performance. Despite the helpfulness of these studies, they do not render the challenge impotent. Van Duren et al. adopted a case study procedure by conducting interview on five enterprises. Small numbers of observations were revealed to limit the potency of the study when making generalizations to the food industry.

The study conducted by Pendell and Boland (2005) employed regression analysis to identify the return of investment on assets. Nonetheless, the sets of data were limited to secondary data without any primary data. Also, the study identified specific resources employed by the enterprises to realize better performance. Despite the fact that the similar studies being carried out were able to observe the pertinence of factors driving the efficiency of enterprise, further analysis should be carried out to shed more light in the study. Further understanding can be exposed regarding the creation of competitive advantage, product innovation, and the performance of food enterprises and beverage enterprises in a highly competitive market as validated by recent studies conducted byAyegba, and Lin (2020). Furthermore, there are no earlier studies that have employed structural equation model in the Nigerian food and beverage industry. Also, few studies have been able to achieve direct connection between variables quantitatively, and be able to achieve the test of hypothesis.

Numerous studies have been carried out and have analyzed the antecedent variables that affect the nexus between dynamic capabilities and enterprise performance. Among are the studies of Vermeulen, De Jong, and O'shaughnessy (2005); Chang, Hughes, and Hotho (2011); Alegre and Chiva (2013), but there are some gaps to be filled because of country specific studies which open a prospect to observe the different variables that have suspect of influencing this nexus. To embark on this, this study determines whether dynamic capabilities have influence on enterprise performance.

Competitiveness

The studies carried out by Eisenhardt and Martin(2000); Helfat and Peteraf(2003); Winter(2003) focused more on the nexus between dynamic capabilities and competitive advantage of the enterprise networks.Gopalakrishnan, Kessler, and Scillitoe (2010) focused on competitive advantage and business performance. Keuppet al. (2012) focused on environmental factors and business performance. Gundayet al. (2011); Jegedeet al.(2012); Hassan et al. (2013); Al-Shuaibiet al. (2016):James, and Zhou (2020)focused on product innovation and enterprise networks in Turkey and noticeable. It is pertinent to note that the indices that were employed to capture dynamic capabilities and business performance may not be enough.

Also, previous studies were limited to the attainment of business networks, network density and network resources, and the existing body of academic and scientific literature may not have delved into the examination of the connection between dynamic capabilities of enterprise networks and enterprise performance. This proposed study aims to address and fill the lacuna.

This study seeks to examine the impact of dynamic capacities on enterprise performance using food enterprises and beverage enterprises in Lagos, Nigeria as case studies. It is pertinent to note that the interconnections between the attributes or constructs of dynamic capacities will be tested on the attributes of enterprise performance. The following sub-variables (strategic decision-making capacity, product innovation capacity, strategic flexibility, competitive intensity, technological turbulence, and technological capability) were employed to represent the variable of dynamic capacity. Also, the following sub-variables (sales growth, enterprise survival, enterprise efficiency and competitive advantage) were employed to represent the variable of enterprise performance. It is believed that this approach is capable of giving more plausible result.

Research, Philosophy, approach, and strategy

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. The survey design and the quantitative nature are essential because the questionnaire is a closed-ended type.

Philosophy

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 enterprises. Among the 14 companies, 6 companies were tagged as foods and beverages enterprises, but 6 enterprises will be selected for this study because of the ease of get-ting information as earlier explained by (Udemba, 2015; Akpan et al., 2016).

Approach

From previous studies of Akpan et al. (2016); Udemba (2015), it was revealed that the other eight companies were difficult to soliciting information from them hence, the used of questionnaire instrument was adopted to collect the primary data. The questions comprise ofrating scale which ranges from 5 = Very High to 1 = Very Low. The usefulness of data collection method was identified in the study of Shammot (2014) and Carlos and Miguel (2013). Obrimah (2014) stated that data collection method in a study usually provide answers to the research questions. This study employed close-ended questions of Likert type five-point scale which was modified. The data collection exercise lasted for approximately three months (October to December 2019). According to Zikmund (2003), the various error allowances will be determined and the suitable one will be chosen based on the discretion of the researcher. Administration of the questionnaire was performed using the assistance of four research assistants who were postgraduate students at a State university in Lagos State (LASU). The research assistants received training prior to the administration of the questionnaires. The training focused on data collection procedures such as the purpose of the study, administration of questionnaires and ethical issues. A covering letter was attached to the questionnaire to highlight the purpose of the study. Before participating in the survey, respondents were requested to read through the letter with the help of the research assistants. Confidentiality of all respondents was noted. Participation in the study was strictly on a voluntary basis, and respondents could withdraw at any time during the research without any fear of victimisation/ discrimination. The resultant data were subjected to descriptive statistical analysis, f-statistics, correlation and regression analysis.

Strategy

The chosen error allowance of 0.04 will be employed to establish the sample size as shown in the equation below:

The formulae for achieving sample size

where;

n = Sample size;

Z = Z score for the confidence interval (2.05);

E = Error allowance (0.04)

When inserted into the formula, Sample Size will be 656.6406, and approximately 657. It is therefore key and noticeable that the questionnaire distribution will target six hundred and fifty-seven respondents whom are middle and top managers in the six foods and beverage manufacturing companies.

Modelling

Structural Equation Modelling (SEM) of Partial Least Square (PLS) was adopted to determine the connection that exists between the variables. It is a multivariate statistical technique that is employed for modelling complex connections between directly and indirectly observed (latent) variables. It is a general framework which simultaneously solves the systems of linear equations and encompasses other techniques through the incorporation and integration of regression, factor analysis, path analysis, and latent growth curve modelling (Bollen, 1989; Catherine, Nathan, and Nora, 2012). SEM is used to estimate a system of linear equations to test the fit of a hypothesized “causal” model.

The first step in SEM deals with the visualization of “path diagram” or hypothesized model which is usually based on prior knowledge of established theories. In path diagrams, rectangles typically represent observed or directly measured variables, and circles or ovals typically represent unobserved or latent constructs which are defined by measured variables. Unidirectional arrows represent causal paths, where one variable influence another directly, and double-headed arrows represent correlations between variables. As shown in the studies of McDonald and Ho (2002); Pearl (2000), the term “arc” was preferred than “causal path”.

Figure 1 illustrates an example SEM model. The system of equations can be written as a number of separate equations or with a general matrix notation.

SEMs comprises of two sub-models, they are:

1) The measurement model estimates connections between the observed variables, also referred to as indicator variables, and latent variables; this is the same framework used in factor analysis. In regression and other statistical theories, “indicator variable” implies a binary yes/no sort of variable. Here, as is customary for SEM, “indicator variable” refers to a variable that is directly associated with a latent variable such that differences in the values of the latent variable mirror differences in the value of the indicator (Bollen, 2001).

2) The structural model develops the connections between the latent variables. For clarity of presentation, the system of equations will be described. The measurement model consists of the following equations, using standard notation used by Bollen (1989):

x₁ = λ₁ζ₁+ δ₁ y₁ = λ₃ƞ₁+ Ɛ₁

x₂ = λ₂ζ₂+ δ₂ y₂ = λ₄ƞ₁+ Ɛ₂

x₃ = λ₃ζ₃+ δ₃ y₃ = λ₅ƞ₁+ Ɛ₃

Where the x’^s and y’^s are observed indicators for latent variables, ζ’^s and ƞ’^s are latent variables, the λ’^s are the factor loadings, and Ɛ’^s and δ’^s are the errors or disturbance terms. In general matrix notation, the measurement model is written as

x = Ʌ_x ζ + δ

y= Ʌy ƞ + Ɛ

From the path diagram, the arrows point to the x’^s and y’^s, so they are modelled as dependent variables. Also, the factor loadings for x₁ and y₁ can be set to 1, which can be done for two reasons:

The model is identifiable; and
The latent variable is on the same statistical scale as the observed variables.

Model identification for SEM can also be achieved in other ways, such as setting the variance for the latent variable to 1. Generally, the indicator with factor loading set to 1 is chosen based on
what the analyst deems is the best descriptor of the latent construct, but can be arbitrary. Finally, in respect to model specification for SEM, exogenous variables have been differentiated from endogenous variables. Exogenous variables have no directed arcs ending on them, while endogenous variables have at least 1 arc ending on them. The structural model consists of the following equations:

ƞ₁ = γ₁₁ξ₁ + ζ₁

ƞ₂ = β₂₁ξ₂ + ζ₂

where the γ and β terms are factor loadings for the latent variables and ζ’^s are error terms. Here, the causal connections between unobserved variables can be evaluated. In general, the structural model may be rewritten in matrix form as the following:

ƞ = α + B ƞ + гξ + ζ

where ƞ is m * 1 vector of latent endogenous variables, ξ is an n * 1 vector of latent exogenous variables, α is an n * 1 vector of intercept terms, B is an m * m matrix of coefficients that give the influence of ƞon each other, г is an m * n matrix of the coefficients of the effect of ξ on ƞ, and ζ is m * 1 vector of disturbances that contain the explained parts of the ƞ’^s. Though it may appear counterintuitive to regress on ƞon itself, each variable in ƞ_iis influenced by other variables in ƞ_i, so this represents connections between latent variables and not necessarily feedback loops. It was assumed that Ɛ, δ, and ζ are mutually uncorrelated.

Regression

Conventional regression approaches are robust to measurement errors in the outcome but not in the predictors. Also, univariate regression approaches cannot model the correlation between error terms for two different outcomes. SEM allows model measurement error for both the predictor and the outcome, and it allows a high degree of flexibility in modelling the correlation between the various error terms. In this study, the indicators were the constructs of dynamic capacities and enterprise performance, the analyst could model the correlation between one construct separately from another construct. Also, the SEM allows for the decomposition of effects if the direct and indirect effect of variables on the outcome is of interest.

For instance, the direct effect of ƞ₁ on ƞ₂ is estimated by β₂₁, and the indirect effect of ζ₁ on ƞ₂ is estimated by γ₁₁. Alternatively, one could model the direct effect of ζ₁ on ƞ₂ with the model depicted in Figure 2, with corresponding coefficient γ₁₂.

These models are estimated using the variance-covariance matrix of the data. Usually, maximum likelihood estimation fitting functions are used to fit the system of equations to the data, but this method requires that the data be normally distributed and the observations be independent. Variations that relax the assumption of multivariate normality have been developed, including the robust weighted least squares estimator (WLSMV), which allows for binary and categorical dependent variables (Muthe ´n, 1984). To assess the overall model fit, there are a number of fit statistics, including the root mean squared error (RMSEA) and comparative fit index (CFI) (Bollen, 1989), and for categorical data, the weighted root mean square residual (WRMR) is appropriate (Hancock and Mueller, 2006). Hu and Bentler (1999) categorize these fit statistics as “comparative” or “absolute.” One could also compare nested models, as is done with traditional regression models and segregation analysis models, using a likelihood ratio test (LRT) and non-nested models using Akaike’s AIC; by contrast, the aforementioned fit statistics (RMSEA, CFI, WRMR, etc.) do not require the models being compared to be nested.

Fursova (2016) stated that researchers typically integrate the smart-PLS-SEM technique for developing theory in investigative research. The main applications of smart-PLS-SEM are path analysis, confirmatory factor analysis, regression models, covariance structure models, second-order factor analysis and correlation models as revealed by Hall, Müller, and Saarinen (2008). The approach of structural equation modelling (SEM) improves the analysis of linear connection between the manifest variables and latent constructs.

The Partial Least Squares (PLS-SEM) method is a multivariate statistical technique for evaluating a measurement model concurrently such as, the connection between the four constructs in this study and its indicators with a structural model to point out the connection between the constructs; this is evidenced in the study of Hair and Hult (2013). In addition to the connection, it could also manifest obtainable parameter estimates to determine the connection between unobserved variables.

Typically, the SEM technique gives way for numerous associations to test and compute at once in the single proposed models with several associations instead of examining each connection individually. This present study will adopt the partial least squares (PLS-SEM) technique to scrutinize and analyze the collected data, as it will improves the evaluation of the conceptual model empirically. PLS-SEM, according to Vinzi, Chin, Henseler and Wang (2010); Astrachan, Patel and Wanzenried (2014) is a path for modelling statistical technique, and a complex multivariate analysis for examining the connections between latent variables.

In the same vein, the PLS-SEM research approach is a flexible, superior and a robust technique to design or build a plausible statistical model (Ringle, Sinkovics and Henseler, 2009; Lowry and Gaskin, 2014; Hair, Sarstedt, Ringle and Gudergan, 2017) and the PLS-SEM features improves the achievement of the stated objective. Peterson and Kim (2013); Astrachan, Patel and Wanzenried (2014) made emphasis that reliable and valid confirmatory factor analysis can be properly achieved with the use of PLS-SEM path modelling.

PLS-SEM is a statistical tool that has been adopted in different fields including engineering and technology, social sciences, (Ringle, Sinkovics and Henseler, 2009). The technique is well applicable for analysis that deals with non-normal data because of its assumed flexibility when concerned with the distribution and normality of variables (Lowry and Gaskin, 2014). According to Lowry and Gaskin (2014), the PLS-SEM method improves the test of complex models that have multi-stage effects, for example, mediating role and other complex models’ variables connections. In line with the various evidences rooted in the body of scientific literatures, this study will adopt the partial-least-squares (PLS-SEM) technique for testing and examining the designed conceptual model and achieving the hypothetical statements.

By so doing, it will examine the connection between the constructs of dynamic capacities and enterprise performance, and the conforming constructs’ indicators with a structural model. This study will incorporate the PLS-SEM for data screening, analysis and the underline assumptions will be taken critically in order to compute loadings, path coefficients and weights, the study will also employ the bootstrapping method to determine the significance levels as evidenced in the study of Hair, Hult, Ringle and Sarstedt (2013).

Reasons for adopting PLS-SEM are:

PLS can be applied to both small and large samples;
It can be adopted in the situation whereby there is no theory or theoretical basis;
It is applicable for both probability and non-probability sampling distribution;
It allows for both reflective and formative latent variables;
It requires only the formation of indices or indicators;

The approach will be based on creating latent factors from the questionnaire based on an exploratory factor analysis. The resulting factors will then be evaluated in terms of their influence on the dependent variables in the model setup. There are four sets of equations to be included in the entire model, they are;

Measurement of equations: Equation 1 links the measurement indicators (survey items) to the latent factors;
PLS equations: Equation 2 associates the latent factors with individuals’ background characteristics;
Structural equations: Equation3 relates the explanatory and the mediator variables; and
Structural equations: Equation 4 links the mediators to the dependent variables.

𝐼_𝑟𝑛 = 𝑍^*_𝑙𝑛𝛼𝑟 + 𝜈_𝑟𝑛 and 𝜈_𝑛 ~ 𝑁(0, Σ_𝜈) for 𝑟 = 1, … , 𝑅 ………………… Equation 1

𝑍^*_𝑙𝑛= 𝑆_𝑙𝑛𝛽_𝑙 + 𝜔_𝑙𝑛 and 𝜔_𝑛 ~ 𝑁(0, Σ_𝜔) for 𝑙 = 1, … , 𝐿 ………………… Equation 2

𝑍^∗_𝑙= 𝑍_𝑖𝛽_𝑖 + 𝜑_𝑙 and 𝜑_𝑙 ~ 𝑁(0, Σ_𝜑) for 𝑙 = 1, … , 𝐿 , 𝑖 = 1, … , ………… Equation 3

𝑌_𝑖𝑛 = 𝑍^*_𝑙𝑛𝛽_𝑧 + 𝜉_𝑖𝑛 and 𝜉_𝑛 ~ 𝑁(0, Σ_𝜉) for 𝑖 = 1, … , ………………………Equation 4

where I_rnis the value of an indicator r of the latent construct Z^*_lnwill be perceived by respondent n,Z^*_lnwill be the value of latent construct l for respondent n, S_lnwill be the vector of M respondents’ observed individual characteristics, and Y_inwill be the vector of enterprise performance levels. Error terms will be presented as elements ω_ln, ν_rn, ξ_inof the vectors following a normal distribution with respective covariance matrix Σ_ω, Σ_ν, Σ_ξ, while parameters to be estimated are α_r, β_l, β_i, and β_z. Considering R indicators translates into writing R measurement equations and estimating an (R×1) vector α of parameters (i.e., one parameter is estimated for each equation), while considering L latent constructs translates into writing L structural equations and estimating an (M×L) matrix of β parameters (i.e., M parameters will be estimated for each equation).

The interconnection of dynamic capacities and enterprise performance is shown in Figure 3 below. The independent variable is Dynamic Capacities and the dependent variable is enterprise performance. The dynamic capacities is represented with strategic decision making, technological turbulence, strategic flexibility, technological capability, product innovation, and competitive intensity, while enterprise performance is represented with sales growth, enterprise survival, competitive advantage, and enterprise efficiency.

In the study, dynamic capacity was captured with respect to strategic decision-making capacity, product innovation capacity, strategic flexibility, competitive intensity, technological turbulence, and technological capability. The performance of enterprise will be captured with sales growth, enterprise survival, enterprise efficiency and competitive advantage.

PLS-SEM method is employed to estimate dynamic capacity on enterprise performance. SEM model is calibrated by using the Lisrel 8.70. The SEM models of various enterprises are adjusted based on the underlying principles of T-Value minimum and maximum. The indicators of six enterprises are shown in Table 1. According to the chi-squared test (χ2/df), the χ2 value is significant, where the lower the chi square value, the lower the difference between the definite matrix and input matrix, and the realistic the goodness of fit test will be. It is pertinent to note that the goodness of fit indexes (GFI) is satisfactory between 0.72 and 0.86, the incremental fit indexes (IFI) are satisfactory between 0.82 and 0.92, and the comparative fit indexes (CFI) are satisfactory between 0.91 and 1.00. These indicators are guided by rule that must not be more than 1.00, which gives a numerical evidence of perfect fitness. Hence, the goodness of fit test is said to be satisfactory from the holistic point of view.

Furthermore, the values of the root mean square residual indexes (RMR) ranges from 0.015 and 0.073, and the root mean square error of approximation indexes (RMSERA) ranges from 0.008 and 0.050. The values of these indicators are quite low and, can be said to be satisfactory.

Table 1. Goodness of Fit Test

List of Enterprises	χ2/df	GFI	IFI	CFI	RMR	RMSEA
1	.332	.72	.92	1.00	.047	.049
2	.395	.86	.91	1.00	.073	.018
3	.346	.78	.92	1.00	.015	.036
4	.358	.72	.86	.92	.024	.008
5	.294	.80	.91	.91	.017	.014
6	.226	.76	.82	1.00	.048	.050

Source: Lisrel 8.70 output (2020)

From the path diagram to stress the inter-connectivity of dynamic capacities and enterprise performance shown in the Figure 3, the numerical analysis for decision is shown in Table 2 below. The connections between the four latent variables of enterprise performance and the six observed variables of dynamic capacities were revealed. In discussing the level of impact, each of the enterprise performance variables were used to measure each of the variables embedded in dynamic capacity.

The factors of dynamic capacities which are Product innovation, Strategic decision-making, Technological capability, Strategic flexibility, Competitive intensity, and Technological turbulence have significant impact on enterprise performance (as captured by sales growth), the coefficients are .850, .114, .052, -.041, -.097, -.094, respectively and the significant values are .000, .003, .027, .081, .060, .080 respectively. When examining the values obtained for coefficient and significant values, it was discovered that dynamic capacity of product innovation is the singular variable that can adequately improve increasing sales growth with coefficient of 0.850 which greatly tend towards 1.00 and significant of 0.000 which is less than critical region 0.05.

Also, concerning the impact of dynamic capacities (Product innovation, Strategic decision-making, Technological capability, Strategic flexibility, Competitive intensity, and Technological turbulence) on enterprise performance (as captured by enterprise survival), the coefficients are .002, .079, -.007, .032, .585, .347, respectively and the significant values are .836, .000, .487, .002, .000, .000 respectively. When examining the values obtained for coefficient and significant values, it was discovered that dynamic capacities of Competitive intensity and Technological turbulence are the singular variables that can adequately improve the survival of enterprise or sustain the enterprise into the unforeseeable future with coefficient of 0.585 and 0.347 respectively which tend towards 1.00, and significant values of 0.000 and 0.000 respectively which is less than critical region 0.05.

In addition, concerning the impact of dynamic capacities (Product innovation, Strategic decision-making, Technological capability, Strategic flexibility, Competitive intensity, and Technological turbulence) on enterprise performance (as captured by enterprise efficiency), the coefficients are .030, .121, .263, .142, .213, -.142 respectively and the significant values are .459, .074, .000, .001, .018, .130 respectively. When examining the values obtained for coefficient and significant values, it was discovered that dynamic capacities of Technological capability and Competitive intensity are the singular variables that could improve the efficiency of enterprise with coefficient of 0.263 and 0.213 respectively which slightly tend towards 1.00, and significant values of 0.000 and 0.018 respectively which is less than critical region 0.05.

Concerning the impact of dynamic capacities (Product innovation, Strategic decision-making, Technological capability, Strategic flexibility, Competitive intensity, and Technological turbulence) on enterprise performance (as captured by enterprise competitive advantage), the coefficients are -.035, -.029, -.026, .870, .048, -.030 respectively and the significant values of .114, .419, .239, .000, .321, .547, respectively. When examining the values obtained for coefficient and significant values, it was discovered that dynamic capacity of Strategic flexibility is the singular variable that could adequately improve competitive advantage of enterprise over other enterprises with coefficient of 0.870 which greatly tend towards 1.00, and significant value of 0.000 which is less than critical region 0.05. The path coefficient table is shown in Table 2.

Table 2. Path coefficient between the latent variables and observed variables

Path relation	Standardized estimate	Standard error	T statistics	P
Sales growth> Product innovation	.850	.023	36.565	.000
Sales growth> Strategic decision-making	.114	.038	2.953	.003
Sales growth> Technological capability	.052	.023	2.217	.027
Sales growth> Strategic flexibility	-.041	.022	-1.750	.081
Sales growth> Competitive intensity	-.097	.049	-1.882	.060
Sales growth> Technological turbulence	-.094	.054	-1.754	.080

Enterprise survival > Product innovation	.002	.011	.208	.836
Enterprise survival > Strategic decision-making	.079	.017	4.525	.000
Enterprise survival > Technological capability	-.007	.011	-.696	.487
Enterprise survival > Strategic flexibility	.032	.010	3.096	.002
Enterprise survival > Competitive intensity	.585	.022	25.215	.000
Enterprise survival >Technological turbulence	.347	.025	14.308	.000

Enterprise efficiency > Product innovation	.030	.049	.741	.459
Enterprise efficiency> Strategic decision making	.121	.082	1.789	.074
Enterprise efficiency > Technological capability	.263	.050	6.413	.000
Enterprise efficiency > Strategic flexibility	.142	.048	3.494	.001
Enterprise efficiency > Competitive intensity	.213	.105	2.363	.018
Enterprise efficiency > Technological turbulence	-.142	.116	-1.516	.130

Competitive advantage> Product innovation	-.035	.023	-1.583	.114
Competitive advantage>Strategic decision making	-.029	.038	-.809	.419
Competitive advantage > Technological capability	-.026	.023	-1.178	.239
Competitive advantage > Strategic flexibility	.870	.022	39.893	.000
Competitive advantage > Competitive intensity	.048	.048	.993	.321
Competitive advantage > Technological turbulence	-.030	.053	-.603	.547

Source: Lisrel 8.70 output (2020)

This section presents the discussion of the findings that emanated from the data analysis. The Lisrel 8.70 version was adopted for analysing SEM. From the findings, it was revealed that product innovation is the singular variable that can adequately improve increasing sales growth. Competitive intensity and Technological turbulence are the dual variables that can adequately improve the survival and sustenance of enterprise into the unforeseeable future. Technological capability and Competitive intensity are the dual variable that could improve the efficiency of enterprise. Strategic flexibility is the singular variable that could adequately improve competitive advantage of food and beverage enterprises.

The findings of this study are in agreement with the findings of Rajee, (2005) which points out that product innovation is the basis for competitive advantage that will enable the innovator to implement the required and at the same time could result to an increase in the enterprise’s profits most especially at challenging times. This is also similar as evidenced in the study of Wang and Ahmed (2004) which reveals that innovation in process and products is observed as prerequisite for the survival and success of the organization. However, process and product innovations are concepts of technological development for enterprise.

It also agrees with the study of Jegede, Ilori, Sonibare, Oluwale and Siyanbola (2012) which conducted the study on factors that influences innovation and competiveness in the indigenous Nigeria’s oil and gas servicing firms. Their study discovered that innovation of product improves the increase in firms’ sales revenue and profitability. Also, they found that majority of the pertinent factors that have impact on innovation in the sub-sector are Research and Development expenditure and training.

The findings of this study also substantiate the findings of Mohd, Mohd and Yasuo (2013) which found that internal motivation for employees, employees’ promotion, and retaining of talented employees have significant influence on sales growth. This is in agreement because product innovation is usually realized by employees’ motivation, promotion, and talent.

Also, it was affirmed in the study that sales growth could be determined within the framework and trend of industry as well as local, national and regional economies. The finding also corroborates the findings of Ibidunni, Oluwole, and Ayodotun (2014) which found a significant nexus between product innovation and the survival of SMEs.

The study also agrees with the findings of Aw and Batra (1998), which found a strong positive connection between technological capability and firm efficiency of manufacturing industry in Taiwan based on total expenditure of R&D and on-the-job training. Subsequently, the study of Acha (2000) which substitute technological capability with R&D expenditure, publications and patents, found that there is a positive connection between the attributes of technological capability and firm’s operational performance. In the southwest Nigeria, Olabisi, Ilori and Egbetokun (2013) conducted a study on technological capabilities in the automobile mechanic industry. Their study found that investment capability of automobile mechanics was weak, meanwhile marketing capability was revealed to be strong especially the customers and spare parts supplies.

This study agrees with the findings of Obembeet al. (2014) which confirm that firms with superior technological capability are likely to generate innovations and become extremely competitive. This is an evidence to affirm that technological capacities have a key and noticeable effect when determining the performance of enterprises. Also, Grewal and Tarisuhaj (2001), and Woren, More and Cordina (2002) revealed that the strategic flexibility is a driver of enterprise performance which improve competitive advantage over business players in the industry.

Theoretical and practical implications

The study found that it was revealed in the study that product innovation is the singular variable that can adequately improve increasing sales growth. Competitive intensity and Technological turbulence are the dual variables that can adequately improve the survival and sustenance of enterprise into the unforeseeable future. Technological capability and Competitive intensity are the dual variable that could improve the efficiency of enterprise. Strategic flexibility is the singular variable that could adequately improve competitive advantage of food and beverage enterprises.

Implications for policy and practices

From the study, product innovation, competitive intensity and technological turbulence, technological capability and competitive intensity, and strategic flexibility are critical sub-variables in determining the robustness of dynamic capacities, as they adequately improve increasing sales growth, survival and sustenance of enterprise into the unforeseeable future, efficiency of enterprise, and competitive advantage of food and beverage manufacturing enterprises respectively, particularly in this trying period that is evidenced with technological change, competition, among others.

Managerial recommendations

When making managerial decision pertaining to increasing sales growth, dynamic capacity of product innovation should be focused. Also, decisions on enterprise survival into the unforeseeable future, dynamic capacities of competitive intensity and technological turbulence should be prioritized. Finally, regarding the decisions on competitive advantage, dynamic capacity of strategic flexibility should be the focus.

Limitations and future avenues of research

This study is limited to the impact of dynamic capacities on the performance of food and beverage enterprise in Lagos, Nigeria. The following sub-variables (strategic decision-making capacity, product innovation capacity, strategic flexibility, competitive intensity, technological turbulence, and technological capability) were employed to represent the variable of dynamic capacity. Also, the following sub-variables (sales growth, enterprise survival, enterprise efficiency and competitive advantage) were employed to represent the variable of enterprise performance.

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 enterprises across west African states (ECOWAS). 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.

SEM: Structural Equation Modelling

ECOWAS: Economics Community of West African States

PLS: Partial Least Square

NSE: Nigerian Stock Exchange

WLSMV: weighted least squares estimator

FGN: Federal Government of Nigeria

NEEDS: National Economic Empowerment and Development Strategy

LRT: likelihood ratio test

SDM: Strategic decision-making

TC Technological capability

SF: Strategic flexibility

CI: Competitive intensity

TT: Technological turbulence

RMSERA: root mean square error of approximation indexes

LASU: Lagos State University

Acknowledgement:

The authors acknowledge the support extended by (OEC) Jiangsu University, Zhenjiang, China by providing expert panels and supervisory supports.

Authors’ contributions

JOA conceived the study and recommended the parameter of the study in consultation with PMJ. The research design was finalized after the approval of ZLL. ZXJ carried out an initial search of all relevant studies pertaining to the area of research from various sources as mentioned in the manuscript for presentation and finalization by the review panel. JOA had written the introduction, findings, and conclusions of the study. JOA and ZLL carried out a detailed study of all relevant studies for inclusion in the Lisrel 8.70.(LTR). EOA, PMJ and ZXJ coordinated institutional supports and overall supervised the study and approved the write-up. The author(s) read and approved the final manuscript.

Funding

This empirical work was supported by the National Natural Science Foundation of China (NSFC),71804061.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author. but restriction applies to the availability of the data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding author upon reasonable request.

Competing interests

The authors clearly declare that they have no competing interests at all.

Adeoye, A. O.and Elegunde, A. F. (2012). Impact of external business environment on organizational performance in the Food and Beverage Industry in Nigeria. British
Journal of Arts and Social Sciences, 6 (2), 194 - 201.

Ahmad, N. Othman, S. N.,andLazim, H. M. (2014). A review of technological capability and performance connection in manufacturing companies. 2014 International Symposium on Technology Management and emerging technologies (ISTMET 2014).

Akpan, P. L., Ikon, Chukwunonye and Momoh (2016). Economic environment and performance of food and beverage manufacturing enterprises sub-sector of a developing economy: Nigeria. International Journal of Recent Research in Commerce Economics and Management (IJRRCEM), 3 (3), 85-90.

Alegre, J., and Chiva, R. (2013). Linking entrepreneurial orientation and firm performance: The role of organizational learning capability and innovation performance. Journal of Small Business Management, 51(4), 491-507. doi:10.1111/jsbm.12005

Ali, S. L.D. Peters, H.W. He and F. Lettice, 2010. Market based organisational learning, dynamic, and substantive capacities: An integrative framework. Journal of Strategic Marketing, 18: 363-377.

AlShuaibi, K., Zain, M.,and Kassim, N. (2016). Performance indicators for quality, innovation, and competitiveness: A survey on the Saudi manufacturing sector. International Business Research, 9(2), 99 - 113.

Astrachan, C. B., Patel, V. K., and Wanzenried, G. (2014). A Comparative Study of CB- SEM and PLS-SEM for Theory Development in Family Firm Research. Journal of Family Business Strategy, 5, 116–128.

Aw, B. W.and Batra, G. (1998): Technological capability and firm efficiency in Taiwan (China). The World Bank Economic Review, 12(1), 59-79.

Ayegba, J.O., & Lin, Z.L. (2020) Empirical Review of Enterprise Networks on Company Performance. International Entrepreneurship Review (previously published as International Entrepreneurship / Przedsiębiorczość Międzynarodowa), 3(1) DOI: http://dx.doi.org/10.15678/PM.2017.0302.02

Barney, J. B. (2001). Is resource-based “view” a useful perspective for strategic management research? Yes. Academy of Management Review, 26(1), 41-56.

Barney, J. B,and Clark, D. N. (2007). Resource-based theory: Creating economic rents and competitive advantage. Oxford: Oxford University Press.

Bollen, K. (1989). Structural Equations with Latent Variables. John Wiley & Sons, New York.

Catherine, M. S., Nathan, J. M., and Nora, L. N. (2012). Structural Equation Modelling, Chapter 27, Pp. 495-512 in Robert C. Elstonet al. (eds.), Statistical Human Genetics: Methods and Protocols, Methods in Molecular Biology, vol. 850, DOI 10.1007/978-1-61779-555-8_2

CBN (2003). Contemporary economic policy issues in Nigeria. Abuja,Nigeria: CBN Publication.

Chirico, F.andSalvato, C. (2008). Knowledge integration and dynamic organizational adaptation in family firms. Family Business Review. 21 (2), 169-181.

Dirisu, J. I., Iyiola, O.andIbidunni, O. S. (2013). Product differentiation: A tool of competitive advantage and optimal organizational performance (A Study of Unilever Nigeria Plc). European Scientific Journal, 9(34), 258 - 282.

Dobbs, M.,and Hamilton, R. T. (2007). Small business growth: Recent evidence and new directions. International Journal of Entrepreneurial Behaviour and Research, 13(5), 296-322.

Dobni, C. B., and G. Luffman. (2003). Research notes and commentaries: Determining the scope and impact of market orientation profiles on strategy implementation and performance.” Strategic Management Journal 24(6), 577–585.

Duranton, G.,and Puga, D. (2001). Nursery cities: Urban diversity, process innovation, and the life cycle of products. American Economic Review, 91(5), 1454-1477.

Eisenhardt, K. M.,and Martins, J. A. (2000). Dynamic capabilities: What are they? StrategicManagement Journal, 21, 1105 - 1121.

Frankfort-Nachmias, C., and Nachmias, D. (2008). Research methods in the social sciences, (7^th Ed.). New York: Worth Publishers.

Fursova, J. (2016). The Business of Community Development and the Right to the City: Reflections on the Neoliberalization Processes in Urban Community Development. Community Development Journal, 53, 119–135.

Grewal, R.,andTansuhaj, P. (2001). Building organizational capabilities for managing economic crisis: The role of market orientation and strategic flexibility. Journal of Marketing, 65(2), 67-80.

Gunday, G., Ulusoy, G., Kilic, K.,and Alpkan, L. (2011). Effects of innovation types on enterprise performance. International Journal of Production Economics, 133(2), 662 -676.

Guo, H., Xu, H., Tang, C., Liu-Thompkins, Y., Guo, Z., and Dong, B. (2018). Comparing the impact of different marketing capabilities: Empirical evidence from B2B firms in China. J. Bus. Res., 93, 79–89.

Hair, J. F., Hult, G. T. M., Ringle, C., and Sarstedt, M. (2013). A Primer on Partial Least Squares Structural Equation Modeling(PLS-SEM); SAGE Publications: Thousand Oaks, CA, USA.

Hair, J. F., Sarstedt, M., Ringle, C. M., and Gudergan, S. P. (2017). Advanced Issues in Partial Least Squares Structural Equation Modelling; SAGE Publications: Thousand Oaks, CA, USA.

Hall, P. C. M., Müller, D. K., and Saarinen, P. J. (2008). Nordic Tourism: Issues and Cases; Channel View Publications: Bristol, UK.

Hashim, N. A. B., Raza, S., and Minai, M. S. (2018). Connection between Entrepreneurial Competencies and Small Firm Performance: Are Dynamic Capabilities the Missing Link? Academy Strategic Management Journal, 17, 1–10.

Hassan, M. U., Shaukat, S., Nawaz, M. S.,and Naz, S. (2013). Effects of innovation types on enterprise performance: An empirical study on Pakistan’s manufacturing sector. Pakistan Journal of Commerce and Social Sciences, 7(2), 243-262.

Hedvall, K., Jagstedt, S., and Dubois, A. (2019). Solutions in business networks: Implications of an inter-organizational perspective. J. Bus. Res., in press.

Helfat, C. E.,and Peteraf, M. A. (2003). The dynamic resource-based view: capability lifecycles. Strategic Management Journal, 24(10), 997-1010

Helfat, C. E., Finkelstein, S., Mitchell, W., Peteraf, M., Singh, H.and Winter, D. (2007). Dynamic capabilities: understanding strategic change in organizations, UK: Blackwell.

Helfat, C. E.,and Peteraf, M. A. (2009). Understanding dynamic capabilities: progress along a developmental path. Strategic Organization, 7(1), 91-102.

Ibidunni, O. S.,and Inelo, F. Market oriented strategic flexibility and market performance of the furniture industry in Southwest Nigeria under fierce competitive environment. Sky
Journal of Business Administration and Management, 3(1), 8-16.

Ibidunni, O. S., Iyiola, O.,and Ibidunni, A. S. (2014). Product innovation, a survival strategy for small and medium enterprises in Nigeria. European Scientific Journal, 10(1), 194 - 209.

James, O. A., & Zhou, L. L. (2020). A Review on Dynamic Capacities in Strategic Management. Przedsiębiorczość Międzynarodowa, 3(1),DOI:http://dx.doi.org/10.15678/PM.2017.0302.02

Jegede, O. O., Ilori, M. O., Sonibare, J. A., Oluwale, B. A.,and Siyanbola, W. O. (2012). Factors influencing innovation and competitiveness in the service sector in Nigeria: a sub-sectoral approach. Management, 2 (3): 69-79.

Kayode, M.O., (1989). Nigeria since independence: The first 25 years. Ibadan, Nigeria: Heinemann Books Ltd.

KPMG (2014). Fast-Moving Consumer Goods in Africa. A Sector Report of KMPG Africa Limited, Cayman Islands.

Kohli, A. K., and B. J. Jaworski. (1990). “Market orientation: The construct, research propositions, and managerial implications.” Journal of Marketing 54(2), 1–18.

Kohli, A. K., B. J. Jaworski, and A. Kumar. (1993). “MARKOR: A measure of market orientation.” Journal of Marketing Research 30(4), 467–477.

Kumar, K., R. Subramanian, and K. Strandholm. (2002). “Market orientation and performance: Does organizational strategy matter?” Journal of Applied BusinessResearch18(1), 1–37.

Kumar, K., R. Subramanian, and C. Yauger. (1998). “Examining the market orientation-performance connection: A context-specific study.” Journal of Management 24(2), 201–233.

Lowry, P. B., and Gaskin, J. (2014). Partial Least Squares (PLS) Structural Equation Modeling (SEM) for Building and Testing Behavioral Causal Theory: When to Choose It and How to Use It. IEEE Transportation Professional Communication, 57, 123–146.

Machirori, T., and Fatoki, O. (2013). The Impact of Firm and Entrepreneur’s Characteristics on Networking by SMEs in South Africa. Journal of Economics, 4, 113–120.

McDonald, R. P., and Ho, M. H. (2002). Principles and Practice in Reporting Structural Equation Analyses. Psychol. Methods, 7: 64-82.

Messeni Petruzzelli, A., Ardito, L.,and Savino, T. (2018). Maturity of knowledge inputs and innovation value: The moderating effect of firm age and size. J. Bus. Res., 86, 190–201.

Mohd, F., Mohd, S., and Hoshino, Y. (2013). Sales growth, profitability and performance: empirical study of Japenese ICT industries with three Asean countries. Interdisciplinary Journal of Contemporary Research in Business, 4(11), 138-156.

Narver, J. C., and S. F. Slater. (1990). “The effect of a market orientation on business profitability.” Journal of Marketing 54(4), 20–35.

Obembe, J. J., Ojo, O. J.,and Ilori, M. O. (2014). Effects of technological capabilities, innovations and clustering on the performance of firms in the Nigerian furniture industry. International Journal of Management Technology 2(2), 19-28.

Oghojafor, B. E. A., Kuye, O. L., Ogunkoya, O. A.,and Shobayo, P. B. (2014). Competitive strategies, technological capabilities and organizational performance: Evidence from Nigerian manufacturing industry. Arabian Journal of Business and Management Review (Nigerian Chapter), 11-22.

Okere, R. I. (2012). Nigeria’s food, beverage industry riding above the storm. Guidian. Retrieved on December 3, 2014 from http://www.ngrguadiannews.com. Oktemgil&Greenley 1997. Consequences of high and low adaptive capability in UK companies. European Journal of Marketing, 31(7), 445-466.

Oladele, O. I., Akinsorotan, A. O.,andAjadi, A. A. (2010). Knowledge and utilization of job enrichment techniques among agricultural extension managers in Botswana and Nigeria.The Journal of International Social Research, 3(12), 327-334.

Olamade, O. O., Oyebisi, T. O.,and Egbetokun, S. O. (2013). Manufacturing business environment in Nigeria strategic characteristics and implications. Advances inManagement and Applied Economics, 3(6), 53-65.

Osundina, J. A. (2014). Working capital management and profitability: Evidence from Quoted food and beverage manufacturing enterprises manufacturing firms in Nigeria. Research Journal of Finance and Accounting, 5(4), 101-107.

Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press, New York, New York.

Pendell, D., and M. Boland. (2005). “Persistence of profitability in family-owned food businesses.” Paper presented at 2005 annual meetings of the American Agricultural Economics Association, Providence, RI, 25–27 July.

Peterson, R.; and Kim, Y. (2013). On the Connection between Coefficient Alpha and Composite Reliability. Journal of Applied Psychology, 98, 194

Rahrami, H. (1992). The emerging flexible organization: Perspectives from Silicon Valley. California Management Review, 34(4), 33-52.

Rajee, F. S. (2005). Sustaining product Innovation in the new economy: the case of Lever Brother Nigeria Limited. Journal of Sales and Marketing, 4(16), 21. 134

Ringle, C. M., Sinkovics, R. R., and Henseler, J. (2009). The Use of Partial Least Squares Path Modelling in International Marketing. In New Challenges to International Marketing; Emerald Group Publishing Limited: West Yorkshire, UK, Pp. 277–319.

Rodrigo-Alarcón, J., García-Villaverde, P. M., Ruiz-Ortega, M. J., Parra-Requena, G. (2018). From social capital to entrepreneurial orientation: The mediating role of dynamic capabilities. European Management Journal, 36, 195–209.

Schumacher, S., and M. Boland. (2005). “The effects of industry and firm resourceson profitability in the food economy.” Agribusiness: An International Journal 21(1), 97–108.

Seung, K. A. (2014). A study on the business ecosystem of German SMEs and its implications for Korean SMEs: Focusing on the hidden champion enterprises in Germany. Korean Journal of Economics Management, 32, 69-95.

Shimizu, K.,and Hitt, M. A. (2004). Strategic flexibility: Organizational preparedness to reverse ineffective strategic decisions. Academy of Management Executive, 18(4), 44-59.

Sola, O., Obamuyi, T. M., Adekunjo, F. O., and Ogunleye, E. O. (2013). Manufacturing performance in Nigeria: Implications for sustainable development. Asian Economic andFinancial Review, 3 (9), 1195 - 1213.

Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of sustainable enterprise performance. Strategic Management Journal, 28 (13), 1319-1350.

Treece, E. W.,andTreece, J. W. (1982). Elements of research in nursing (3rd ed.). Mosby: St. Louis.

Thilenius, P., Pahlberg, C., and Havila, V. (2016). Extending the Business Network Approach: New Territories, New Technologies, New Terms; Palgrave Macmillan: London, UK

Udemba, F. S. (2015). Understanding the food, beverages and tobacco operating environment. Manufacturing Association of Nigeria News, 17.

Vinzi, V. E., Chin, W. W., Henseler, J., and Wang, H. (2010). Handbook of Partial Least Squares: Concepts, Methods and Applications, 1^st ed.; Springer: Berlin/Heidelberg, Germany, pp. 1–798.

Wang, Y., Lo, H., Zhang, Q.,and Xue, Y. (2006). How technological capability influences business performance: An integrated framework based on the contingency approach. Journal of Technology Management in China, 1(1), 27 - 52.

Wang, C. L.,and Ahmed, P. K. (2007). Dynamic capabilities: A review and research agenda. International Journal of Management Reviews, 9(1), 31-51.

Wilden, R., Gudergan, S. P.,and Lings, I. (2007). Dynamic capabilities and organizational performance. Ph.D Thesis, University of Technology, Sydney.

Management, 49 (2), 114-136.

Winter, S. (2003). Understanding dynamic capabilities. Strategic Management Journal, 24, 991 - 995.

Word, J. (2009). Business Network Transformation: Strategies to Reconfigure Your Business Connections for Competitive Advantage, Wiley: Hoboken, NJ, USA, p. 279.

Zahra, S. A.,and Hayton, J. C. (2008). The effect of international venturing on firm performance: The moderating influence of absorptive capability. Journal of Business Venturing, 23, 195 -220.

Zahra, S. A., Saprienza, H. J.,and Davidson, P. (2006). Entrepreneurship and dynamic capabilities: A review, model and research agenda. Journal of Management Studies, 43 (4), 917 - 955.

Zhang, M. J. (2007). IS support for top managers’ dynamic capabilities, environmental dynamism, and firm performance: An empirical investigation. Journal of Business andManagement, 13(1), 57-77.

Zhu, Q., Cordeiro, J., and Sarkis, J. (2013). Institutional pressures, dynamic capabilities and environmental management systems: Investigating the ISO 9000—Environmental management system implementation linkage. J. Environ.Manag, 114, 232–242

Zikmund, W. G. (2003). Exploring Marketing Research, 8^th Edition, Thompson Learning/Southwestern, Cincinnati, OH.

Ziokowska, M. (2013). Relational resources management as source of company’s innovativeness and competitive advantage. Journal of Economic, Business and Management, 2(3), 202-208.

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Impact of Dynamic Capacities on the Performance of Food and Beverage Enterprises in Lagos, Nigeria

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Abstract

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Highlights

Introduction

Theoretical Background

Literature Review

Research Methodology

Results And Discussions

Conclusions

Abbreviations

Declarations

References

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