Dynamic Capabilities and Organizational Resilience of Manufacturing Firms in Nigeria

Abstract

The study examined the relationship between dynamic capabilities and organizational resilience of manufacturing firms in Nigeria. The dynamic capability was decomposed into sensing capability and reconfiguration capability, while organizational resilience had adaptability and agility as it measures. A cross-sectional survey research design was followed, while primary data was collected via the administration of a structured questionnaire. Copies of the questionnaire were randomly handed over to 86 respondents, comprising foremen, supervisors and managers in 11 manufacturing firms that are clustered around Trans Amadi Industrial Area, Port Harcourt. Two research objectives with a corresponding number of research questions and research hypotheses were formulated. Descriptive statistics were analysed with the aid of the Statistical Package for Social Sciences version 25.0, while the Partial Least Squares Structural Equation Modeling (PLS-SEM) was deployed to test the hypothesized relationships via SmartPLS 3.2.7. The results of the analyses affirmed the alternate hypotheses, which stated that dynamic capabilities are positively correlated with the measures of organizational resilience. This shows that dynamic capabilities amplify the manufacturing firms’ resilience. It was recommended that managers of the firms should encourage quick response to environmental changes, by enhancing their employees’ capability to detect, monitor and respond to environmental volatilities. Likewise, management of the manufacturing firms should strategically position the firms to be among the first to identify and acquire external knowledge about their market trends, technology and industry. This will help the firm to adapt quickly to disturbances from the environment and be more resilient. It was suggested that similar studies be conducted in other sectors of the economy.

Keywords

Adaptability Agility Dynamic Capabilities Organizational Resilience Reconfiguration Sensing Capability

Introduction

Strategic and operations management literature are awash with studies about the concept of organizational resilience and its importance (Ahiauzu & Eketu, 2015; Ikirobo et al., 2017; Ofoegbu & Onuoha, 2018; Teece, 2007). Consequently, organizational resilience is considered an important organizational variable, which enables organizations to adapt to the dynamism in their operating environment (Sylva & Ojiabo, 2018). The dynamic and turbulent nature of the corporate environment has compelled strategic managers to continuously engage in the development of capabilities to cope with the dynamism from the environment (Chaskin, 2008; Sylva & Ojiabo, 2018).

Generally, organizational resilience promotes firm’s ability to predict changes in the environment before it occurs and enable the firm to thrive despite divergent threats and changes in the operating environment (Asghar et al., 2015). Thus, organizational resilience is essential to the survival of every organization. Specifically, Umoh et al. (2013) noted that organizational resilience amplifies firms’ performance in the long run, notwithstanding the type of products or services the firm renders. Furthermore, resilience improves the level of effectiveness and overall efficiency of the firm (Umoh et al., 2013).

Again, the dynamic capability is an indispensable component among the success factors of organizations, which operates in a competitive and dizzying environment (Winter, 2003). Dynamic capability is a strategic driving force that enhances corporate performance (Eriksson, 2014; Wang & Ahmed, 2007) and propels innovativeness through the creation of novel products while elevating the firm’s competitive advantage (Ofoegbu & Onuoha, 2018). Last, dynamic capabilities ensure a firm’s survival (Gathungu & Mwangi, 2012).

The relationship between dynamic capabilities and organizational resilience has been investigated by notable scholars (MacInerney-May, 2012; Teece, 2007). Dynamic capabilities have also been studied in relationship with other organizational outcomes such as competitive advantage (Ofoegbu & Onuoha, 2018), organizational performance (Gathungu & Mwangi, 2012; Zott, 2003). However, it appears that very little has been done on the relationship between these constructs in the Nigerian business environment, especially in the manufacturing sector.

The manufacturing sector is an important sector in the most developed economies because it contributes immensely to their economic growth and development. Also, it generates employment opportunities for the youths and earns foreign exchange for the country. The scenario is, however, different in the Nigerian manufacturing sector.

Manifestations of lack of resilience among manufacturing firms in Nigeria can be seen in the increasing number of manufacturing plants shut down in the country over the last couple of years. Example of such company is Michelin, which closed down its manufacturing plants in Port Harcourt, Nigeria and moved to Ghana. Since these signs are commonly found among Nigerian manufacturing firms, it, therefore, shows a lack of resilience among the manufacturing firms in the country. Management literature exposes the significant of dynamic capabilities in enhancing organizational resilience (e.g.,Fainshmidt, 2014; Kurtz & Varvakis, 2016). Dynamic capabilities are essential factors that help in identifying opportunities and threats (sensing capability); these capabilities transform existing strategies, structures and technologies (reconfiguration capability) to seize the opportunities or withstand the threat, therefore amplifying organizational resilience (Kurtz & Varvakis, 2016). Thus, the lack of resilience among Nigerian manufacturing firms may be a result of the lack of dynamic capabilities among the firms. Hence, this study investigates the relationship between dynamic capabilities and organizational resilience of manufacturing firms in Nigeria.

Literature Review

Theoretical Framework and Hypotheses Development

The concepts of dynamic capabilities and organizational resilience are rooted and obtained relevance from several theories. However, in this work, we focus on two of the most prominent theories in strategic and operations management. These are resource-based view and dynamic capability theories.

The Theory of Dynamic Capabilities

Dynamic capabilities theory recognizes the fact that when circumstances change, new configurations of resources will gain favour and strength of current asset positions can disappear. The dynamic capability view (DCV) is an answer to these critiques. Initially, dynamic capabilities are coined as a firm’s ability to ‘integrate, build and reconfigure internal and external competencies to address rapidly changing environments’ (Teece et al., 1997). DCV believes that the overall performance of the firm relies on its capabilities to renew, restructure and reposition itself.

The theory of dynamic capabilities has been adopted in explaining organizational change processes in certain dimensions, including organizational resilience, innovation, entrepreneurial behaviour, organizational transformation or coping with crises (e.g., Sylva & Ojiabo, 2018; Vogel & Güttel, 2013). DCV is designed to give a theoretical framework for specifying routinized adaptation processes as well as actors’ influences on renewal (Eisenhardt & Martin, 2000; Teece et al., 1997; Teece, 2007; Zollo & Winter, 2002).

Dynamic Capabilities Defined

Dynamic capability approach was developed to explain how organizations can compete and survive in rapidly changing business environments (Teece et al., 1997). The dynamic capability was preceded by the resource-based view (Barney, 1991, 2001). The resource-based view argued that to successfully compete, businesses need to have intangible and tangible assets that are ‘valuable, rare and hard to imitate or substitute’. But the dynamic capability theorists added that in a highly dynamic environment, ambitious organizations may need the capacity to quickly redeploy resources and response to threats (Teece et al., 1997). The main thrust of the dynamic capabilities is enshrined in organizational routines and managerial and business processes.

Dynamic capabilities refer to the flexibility of a firm to utilize its resources effectively to accomplish harmoniousness with its peculiar business setting (Rugami & Aosa, 2013). Further, dynamic capabilities perspective reflects the ability of a firm to achieve new styles of competitive advantage by invigorating competences, structure and resources to realize harmony with the ever-changing business setting (Rugami & Aosa, 2013).

Wheeler (2002) defined dynamic capabilities as ‘firm processes that use resources, specifically the processes to integrate, reconfigure, gain and release resources to match and even create market change’. Also, Wang and Ahmed (2007, p. 47) see dynamic capabilities as the firm’s ‘ability to constantly integrate, reconfigure, renew and recreate its resources and capabilities, and upgrade and reconstruct its core capabilities to adapt to changing competitive environment, in order to obtain and maintain competitive advantage’. Furthermore, Ofoegbu and Onuoha (2018, p. 8) defined dynamic capability as ‘organization’s activities, procedures, and practices that enhance its competitiveness, thereby helping it to maintain a leading role in its industry’. However, in this work, dynamic capabilities are seen as the dexterity of organizations to identify areas that need a change (sensing capability), need to develop an appropriate line of actions and need to implement a course of action (reconfiguration capability). This definition corroborates the definitions of Gonza´lez et al., (2009) and Pavlou and El Sawy (2011), who defined dynamic capabilities as a firm’s capacity to readjust to rapid changes and uncertainty in its environments.

In this study, the dynamic capability is encapsulated in facets (sensing capability and reconfiguration capability). These dimensions have been extensively adopted in previous studies (e.g., MacInerney-May, 2012). As submitted by Teece (2007), sensing capability represents the firm’s inclination to use its current capability to foresee changes in its environment. That is, the sensing capability is the ‘ability to promptly recognise opportunities in the environment when it presents itself, while also, having the means to monitor threats from the environment’ (Barreto, 2010; Teece, 2007). Sensing capability alludes to the use differs information and knowledge to understand leading problems and unmet service needs before a more focused conceptualization of new service solutions follow in the seizing phase. The reconfiguration capability dimension of dynamic capability is the firm’s ability to generate new ‘capabilities to integrate current capabilities’ (Den Hertog et al., 2010). This facet of dynamic capability involves the application of response and recomposing of the current capability. Based on the understanding of the organization as a certain configuration of capabilities and resources, in this article, any type of alteration of this configuration is termed as reconfiguration. Reconfiguration is the final chain in a procedural perspective on dynamic capabilities and is extensively adopted as a nucleus element of dynamic capabilities (Eisenhardt & Martin, 2000; Teece, 2007). Also, reconfiguring capability refers to ‘organization abilities to match and manage service strategy and organizational design to achieve strategic fit’ (Teece, 2007).

Organizational Resilience

Organizational resilience is the capacity of the firm to foresee possible unfavourable occurrences and resist through the adaptation of possible measures to contain with the threats and to recover by restoring the organization or state to a stable and acceptable state as much as possible (Burnard & Bhamra, 2011; Umoh et al., 2013). The ability of organizations to soak up shock through the development of a resistance system within the face of diverse disturbances that proliferates the business setting may be a reflection of how ready the organization is towards unforeseen occurrences (Umoh, 2007; Umoh et al., 2013).

McManus et al. (2008) assert that ‘the numerous concepts that emerge from definitions of organizational resilience include knowledge of the environment, level of preparation, the anticipation of perturbations, adaptation, capacity to recover’. Likewise, Annarelli and Nonino (2016) and Annarelli et al. (2020) see organizational resilience as the firm’s strength to withstand disruptions and unplanned changes based on its strategic awareness and collaboration between internal and external capabilities.

Notwithstanding the plurality of measures of resilience, this study recognizes and adopts adaptability and agility as the measures of organizational resilience. Adaptability is described as ‘the ability of an enterprise to alter its “strategy, operations, management systems, governance structure and decision-support capabilities” to withstand perturbations and disruptions’ (Starr et al., 2003). Olsson et al. (2004) described adaptability as an attribute of a socio-ecological system that permits coping with disturbances and changes while holding important functions, formations and feedback mechanisms. Likewise, Adger (2003, p. 32) submits that adaptability is the ‘ability of a system to evolve in order to accommodate perturbations or to expand the range of variability within which it can cope’. Agility stresses speed up adaptability as the essential property of a light-footed association (Gunasekaran, 1999). A similarly essential quality of agility is a powerful reaction to change and vulnerability (Goldman et al., 1995). Others consider it to be the demonstration of reaction to change in legitimate ways and taking favourable circumstances of changes as the principal components of agility (Sharifi & Zhang, 1999; 2001). These measures (adaptability and agility) of organizational resilience were adopted because they are essential for manufacturing firms. Also, they have been used elsewhere by several scholars, including Kantur and Iseri-Say (2015), who decomposed organizational resilience into adaptability and agility.

Relationship Between Dynamic Capabilities and Adaptability

Dynamic capabilities have been studied in relationship with several organizational outcomes, such as service innovation (Žitkienė et al., 2015), corporate performance (Protogerou et al., 2011; Yoshikuni & Albertin, 2017), competitive advantage (Breznik & Lahovnik, 2016; Ofoegbu & Onuoha, 2018).

Several studies in western and European countries have shown positive relationships between dimensions of dynamic capabilities and adaptability as a measure of organizational resilience. Žitkienė et al. (2015) studied the effect of dynamic capabilities for service innovation focusing their research on service firms in Slovenia. They collected data from existing literature (secondary data) and adopted a comparative analysis research design. They concluded that ‘dynamic capabilities are important for service-oriented firms, as they allow firms to identify market opportunities and client needs, take action on those opportunities by organizing available resources, and gain a competitive advantage in the process’ (p. 276).

In a recent study, Ofoegbu and Onuoha (2018) investigated the nexus between ‘dynamic capabilities and competitive advantage of fast foods restaurants in Nigeria’. They adopted a cross-sectional survey design, while information was gathered by a method of a self-structured questionnaire that was self-administered to 120 fast-food workers. The scholars found that the restaurants’ dynamic capabilities significantly impacted on the levels of competitiveness, with sensing capability having a positive and significant correlation with a competitive advantage, learning capability and reconfiguration capability. Thus, they suggested that owners of the fast-food restaurants should encourage swift response to environmental dynamism, by strengthening workers’ capacity to recognize, screen and react to changes in the competitive environment. Thus, dynamic capabilities amplify adaptability. Therefore, the under-listed hypotheses were proposed for this study:

H₁: There is a positive relationship between sensing capability and adaptability.

H₂: There is a positive relationship between reconfiguration capability and adaptability.

Relationship Between Dynamic Capabilities and Agility

Studies have shown a positive relationship between dynamic capabilities and agility (Kanten et al., 2017; Teece, 2016). Kanten et al. (2017) investigated the effect of dynamic capability on organizational agility in the Turkish retailing industry. Data was collected from 176 employees through survey method and analysed through structural equation model. The result of the study uncovered that the dynamic ability measurements of integration and coordination decidedly and altogether impact the retailers’ deftness. Teece (2016) examined dynamic capabilities and organizational agility and found an encouraging inter-linkage among the variables.

Summarily, management scholars such as Clausen (2013) affirmed that dynamic capabilities have an enormous influence on product innovation. Giniuniene and Jurksiene (2015) opined that dynamic capabilities impact organizational learning, innovation and performance. More so, Teece (2016) stressed that ‘strong dynamic capabilities are required to provide organizational agility’. In the context of this study, it is normal that dynamic capabilities are considered as predecessors of organizational resilience. Thus, it was hypothesized that:

H_a3: There is a positive relationship between sensing capability and agility.

H_a4: There is a positive relationship between reconfiguration capability and agility.

Methodology

In this study, the causal research design was followed. The causal research design is also called explanatory research design and is a type of research carried out to identify ‘the extent and nature of cause-and-effect relationships’ (Zikmund et al., 2012). Causal studies focus on the examination of a circumstance or a specific situation to explain the nature of relationships between variables (Kimberlin & Winterstein, 2008; Levin, 2006).

The population of this study comprises all the employees in the 32 manufacturing firms that are registered with the Manufacturing Association of Nigeria, Rivers State Chapter. However, due to proximity, the study adopted 11 manufacturing firms that are clustered around the Trans-Amadi Industrial Layout as the accessible population for this study.

Data on the study constructs (dynamic capability and organizational resilience) is better obtain from personnel at the managerial cadre due to their experience on the job. Thus, the sample element comprises foremen, supervisors, factory managers, personnel managers, operations managers, general managers and all other employees at managerial cadre.

There were a total of 109 employees at the managerial cadre from the 11 manufacturing firms. Copies of the questionnaire were sent to all, out of which 62 copies were correctly filled and returned, which represented a 56.8% return rate. Therefore, 62 copies of the questionnaire returned were used for the final analyses. A bootstrap method (with a bootstrap sample of 5,000) was used in the final analysis (Ali et al., 2018; Hair et al., 2003; Lalatendu et al., 2017).

Operational Measure of Variables

Dynamic capabilities were decomposed into sensing capability and reconfiguration capability. These dimensions were adopted from MacInerney-May (2012) and were measured using 15 statement items. The sensing capability was measured using eight items such as ‘we periodically review the likely effect of changes in our business environment (e.g., regulation) on customers’. Reconfiguration capability has seven statement items, such as ‘We can effectively integrate new externally sourced capabilities and combine them with existing capabilities into “novel” combinations’. All the items were adapted from MacInerney-May (2012).

Organizational resilience was studied using two measures (adaptability and agility), which were adopted from Sylva and Ojiabo (2018). A total of 12 statement items were used to describe the resilience of the firms. Specifically, four items were used to describe adaptability, such as ‘Our organization frequently adopts new marketing techniques’; and ‘Our firm frequently introduce new products/services’. On the other hand, eight items were used to describe agility. The items include: ‘we quickly switch suppliers to take advantage of lower costs, better quality or improved delivery items’; ‘our firm quickly adopts new technologies to deliver better, faster and cheaper services’. The items were adopted from Ahsan and Ngo-Ye (2005), Oosterhout et al. (2007), Tallon (2008), Chu (2012), Kantur and Iseri-Say (2015) and Sylva and Ojiabo (2018). The items were adjusted to suit the Nigerian business environment and were scaled on a five-point Likert scale with weights assigned as follows: 5 = strongly agree, 4 = agree, 3 = neutral, 2 = disagree, 1 = strongly disagree.

Presentation, Analysis and Interpretation of Data

Analysis of Demographic Characteristics of Respondents

Under this section, the demographic details of the 62 respondents, who correctly answered their questionnaire, were analysed.

Table 1 shows the demographic profile of the 62 respondents who took part in the study. Results show that for the gender distribution male respondents were 43, representing 69.4%, while female respondents were 19, representing 30.6%. For age distribution, those within 18–35 age bracket were in the majority with 30 respondents, representing 48.4%; those within 51 and above age bracket were the minority with only 5 respondents, representing 8.1%. These two findings imply that there are more male in the manufacturing firms sampled than female. An observation of partakers’ age distribution shows that the workforce is comprised of relatively youthful workers aged between 36 and 50 age bracket. This outcome is expected since the manufacturing sector requires strength and physical fitness to be able to perform and meet the expectations of employers.

As for marital status, the singles among them were 35, representing 56.5%; the married ones were 27, representing 43.5%. This reveals that there are more single workers in manufacturing firms. This may be the cause by the fact that employers in the manufacturing sector prefer single workers to married applicants. Concerning the level of education of the respondents, it was shown that 21 of them representing 33.9% have obtained ‘O’ level certificate, 14 representing 22.6% have obtained either Ordinary National Diploma (OND) or National Certificate in Education (NCE), 25 constituting 40.3% have obtained either Higher National Diploma (HND) or BSc; last, 2 of them, which represent 3.2%, have master degrees. However, none has obtained a doctorate degree.

Table 1.

Analysis of Demographic Profiles of Respondents

Variable	Item	Frequency	Percentage
Gender	Male	43	69.4
	Female	19	30.6
Marital status	Married	27	43.5
	Single	35	56.5
Age	18–35	30	48.4
	36–50	27	43.5
	51–above	5	8.1
Years of work experience	0–5	17	27.4
Years of work experience	6–10	38	61.3
	11–15	5	8.1
	16–20	2	3.2
Highest level of educational attainment	O level	21	33.9
	OND/NCE	14	22.6
	HND/BSc	25	40.3
	MBA/MSc	2	3.2
	DBA/PhD	–	–

Source: Field data, 2020.

Note: DBA = Doctor of Business Administration.

Assessment of Non-response Bias

To curtail the effect of respondents’ bias, the non-response bias was tested to confirm the conclusion drawn from the findings. The research instrument was administered twice, and the responses were compared to examine whether the first responses significantly differ from the second responses. The two batches were named ‘Set A’ and ‘Set B’, respectively.

To check for non-response bias, the researcher carried out the approach endorsed by Armstrong and Overton (1977). The guideline is if no substantial differences exist on the variables, it may be argued that respondents and non-respondents shared some level of commonality, consequently signalling the non-incidence of non-response bias.

The determination of non-respondents bias using the differences between early responses and late responses was deemed appropriate because late respondents, or those requiring more prodding to respond, are theorized to have similar characteristics with non-respondents (Armstrong & Overton, 1977; Newman, 1962). Furthermore, the continuum of resistance model by Boniface et al. (2017) and Lahau et al. (2003) posits that late respondents can be adopted as a proxy for non-respondents in estimating non-response bias. Moreover, this method has been adopted by several authors, including Paulraj et al. (2017).

The results on all variables of dynamic capabilities and organizational resilience from the perceptions of the Set A and Set B are compared in Table 2.

It can be observed in Table 2 that the two batches do not have significant differences. The Levene criterion for homogeneity of variances was not significant (p > .05), indicating that all data are from same population. Therefore, data collated from both first respondents and second respondents were merged for subsequent manipulation.

Multivariate Data Analysis

The multivariate data analysis in this study was done with SmartPLS 3.2.7 (Ringle et al., 2015). The analysis was carried out in a two-step approach involving the evaluation of the study models via the PLS-SEM, that is, the assessment of both the measurement and the structural model. Since this study involves the examination of the relationship between the criterion and predictor variables, the PLS-SEM is used for analyses. According to Hult et al. (2017), PLS-SEM serves as a substitute for non-parametric statistic approaches, and it is a better option since it works with fewer restrictions, especially pertaining to sample size and normality of data. However, to ascertain the psychometric quality of the data, it was tested for reliability and validity.

Table 2.

Test for Non-response Bias on the Variables (Dynamic Capabilities and Resilience)

	Responding Batch	N	Mean	SD	Std. Error Mean	Sig. (Two tailed)
Sensing capability	Set A	65	16.16	4.271	.505	Eq. var. assumed	.125
	Set B	62	14.95	2.991	.388	Eq. var. not assumed	.120
Recon. capability	Set A	65	8.95	3.460	.412	Eq. var. assumed	.133
	Set B	62	6.82	1.581	.196	Eq. var. not assumed	.130
Adaptability	Set A	65	24.30	7.162	.838	Eq. var. assumed	.559
	Set B	62	25.09	6.902	.865	Eq. var. not assumed	.557
Agility	Set A	65	13.49	3.488	.415	Eq. var. assumed	.845
	Set B	62	13.72	3.231	.417	Eq. var. not assumed	.842

Source: SPSS computation from data, 2020.

Model Specification and Assessment Using PLS-SEM

The PLS-SEM approach involves two main components: assessment of the measurement model and an assessment of the structural model (Hulland, 1999). In this section, the first component—assessment of the measurement model—is carried out.

The measurement model in Figure 1 reflects the conceptual framework of this study. The interior model describes the links among the constructs, while the outer model indicated the relationship between the dimensions and measures of the constructs and the loadings of their indicators. The regressor variable is dynamic capability, which has sensing capability and reconfiguration capability as its dimensions. The regressand variable is organizational resilience, and it is disintegrated into adaptability and agility.

The outer model is the measurement model, and it pertains to the assessment of reliability and validity of the constructs using factor loadings of the manifest variables, indicator reliability and the average variance extracted. Statistically, factor loadings more than 0.70 indicate that the construct elucidates more than 50% of the indicator’s variance (Hulland, 1999). This shows that the indicator manifested an adequate level of reliability.

In regard to the facets of dynamic capabilities, all items for sensing capability met the 0.70 benchmark (Hulland, 1999) except SC₆ (l_k = 0.524) and SC₈ (l_k = 0.357). All indicators for reconfiguration capability met the benchmark point of 0.70 except RC₁ (l_k = 0.249) and RC₅ (l_k = 0.318).

Pertaining to organizational resilience, all items for adaptability satisfied the 0.70 threshold. Also, most of the indicators for agility met the threshold of 0.70 except AG₄ (l_k = 0.323) and AG₈ (0.150).

Likewise, when individual item factor loadings were squared (indicator reliability), all the items that failed to meet the 0.70 factor loading threshold (Hulland, 1999), also failed to meet the 0.50 threshold for indicator reliability. Thus, they were not used for the final analysis, except item LC_2, (0.472), which was very close to the benchmark and was retained.

Figure 1.

Source: The authors.

Validity and Reliability of the Measurement Instrument

The validity of the research instrument was confirmed by examining the standardized factor loadings and convergent validity (indicator reliability, average variance extracted). The convergent validity was in line with the Fornell and Larcker (1981) and Bagozzi and Yi (1988) criteria. That is, for the average variance extracted (AVE) values to be accepted is must be up to 0.5. The results in Table 2 indicate that the measurement model attained a large convergent validity.

The reliability of the instrument was confirmed through composite reliability and Cronbach alpha techniques. In the case of Cronbach alpha, to be acceptable, it must meet the 0.70 threshold suggested by Nunnally and Bernstein (1994). The initial outcome is shown in Table 3.

Table 3 reveals that although majority of the latent variables show satisfactory levels of internal consistency, only adaptability and agility fulfilled the threshold criterion for convergent validity (AVE should be > 0.5) as prescribed by Fornell and Larcker (1981).

Hence, indicators under their corresponding manifest variables, which failed to meet the recommended thresholds (factor loadings < 0.7), were treated as redundant items or ‘free parameter estimates’. Thereafter, the measurement model was re-run in order to compute new values of internal consistency and convergent validity. The final PLS-SEM assessment results of measurement model is shown in Figure 2 and Table 4.

Composite reliability: The composite reliability of the items in the scale was confirmed in Table 4. According to Bagozzi and Yi (1988), composite reliability has a lesser level of bias in estimating reliability in comparison to Cronbach alpha. Bagozzi and Yi (1988) suggested that an item should have a composite reliability value of .70 for it to be assumed reliable. By this estimation, each of the items in the latent variables showed sufficient internal consistency.

Cronbach alpha: Cronbach alpha reliability values lie between 0 and 1. The closer the value is to 1, the stronger the internal consistency of the indicators in the scale. George and Mallery (2003, p. 231) proffer the following rules of thumb ‘α > 0.9 = excellent, α > 0.8 = good, α > 0.7 = acceptable, α > 0.6 = questionable, α > 0.5 = poor and α < 0.5 = unacceptable’. Based on this rule, the items in the scale are reliable because their Cronbach alpha values are well above the minimum threshold of 0.70 as suggested by Nunnally and Bernstein (1994).

Convergent validity: To assess the convergent validity, the AVE was observed. As suggested by Fornell and Larcker (1981), to achieve convergent validity, the construct should reach at least an acceptable level of 0.5 AVE. Founded on the condition prescribed by the above scholars, all the latent constructs in this study achieved an acceptable level of convergent validity of 0.5 and above. This is contained in Table 4.

Discriminant validity: Discriminant validity shows the ambit, a particular latent construct differs from other constructs. AVE was used to appraise the discriminant validity of the study instrument (Fornell & Larcker, 1981). The following criterion was followed: ‘Discriminant validity is assessed by comparing the shared variance (squared correlation) between each pair of constructs against the minimum of the AVEs for these two constructs. If within each possible pairs of constructs, the shared variance observed is lower than the minimum of their AVEs, then discriminant validity is evidenced’ (Fornell & Larcker, 1981, p. 45). Table 5 shows the output for discriminant validity.

Table 3.

Initial Loadings, Reliabilities and AVEs for All the Items Listed in the Model

Latent Variables	Indicators	Convergent Validity			Internal Consistency/Reliability
Latent Variables	Indicators	Loadings	Indicator Reliability	AVE	Composite Reliability (Pc)	Cronbach Alpha (CA)
		> 0.70	> 0.50	> 0.50	> 0.70	0.70–0.90
SC	SC₁	0.713	0.508	0.415	0.884	0.758
	SC₂	0.775	0.601
	SC₃	0.840	0.706
	SC₄	0.850	0.723
	SC₅	0.712	0.507
	SC₆	0.524	0.275
	SC₇	0.759	0.576
	SC₈	0.337	0.114
RC	RC₁	0.249	0.062	0.484	0.853	0.739
	RC₂	0.851	0.724
	RC₃	0.872	0.760
	RC₄	0.728	0.530
	RC₅	0.318	0.101
	RC⁶	0.734	0.539
	RC₇	0.821	0.386
AD	AD₁	0.712	0.507	0.595	0.854	0.741
	AD₂	0.844	0.712
	AD₃	0.701	0.491
	AD₄	0.818	0.669
AG	AG₁	0.878	0.771	0.552	0.894	0.763
	AG₂	0.828	0.686
	AG₃	0.840	0.706
	AG₄	0.323	0.104
	AG₅	0.911	0.830
	AG₆	0.893	0.797
	AG₇	0.709	0.503
	AG₈	0.130	0.017

Source: SmartPLS 3.2.7 output on research data, 2020.

Note: SC = sensing capability, RC = reconfiguration capability, AD = adaptability, AG = agility. Bold and italicized items are treated as free parameters or redundant indicators, thus not included in the final model.

Figure 2.

Source: SmartPLS 3.2.7 Output, 2020.

Table 4.

Final Factor Loadings, Reliabilities and AVEs for All the Items Listed in the New Model

Latent Variables	Indicators	Convergent Validity			Internal Consistency/Reliability
Latent Variables	Indicators	Loadings	Indicator Reliability	AVE	Composite Reliability (Pc)	Cronbach Alpha (CA)
		> 0.70	> 0.50	> 0.50	> 0.70	0.70–0.90
SC	SC1	0.713	0.508	0.603	0.901	0.778
	SC2	0.775	0.601
	SC3	0.840	0.706
	SC4	0.850	0.723
	SC5	0.712	0.507
	SC7	0.759	0.576
RC	RC2	0.851	0.724	0.642	0.899	0.759
	RC3	0.872	0.760
	RC4	0.728	0.530
	RC6	0.734	0.539
	RC7	0.821	0.386
AD	AD1	0.712	0.507	0.595	0.854	0.741
	AD2	0.844	0.712
	AD3	0.701	0.491
	AD4	0.818	0.669
AG	AG1	0.878	0.771	0.718	0.937	0.863
	AG2	0.828	0.686
	AG3	0.840	0.706
	AG5	0.911	0.830
	AG6	0.893	0.797
	AG7	0.709	0.503

Source: SmartPLS 3.2.7 output on research data, 2020.

Note: SC = sensing capability, RC = reconfiguration capability, AD = adaptability, AG = agility.

Matched with the suggestion by Fornell and Larcker (1981) were Hair et al. (2011) among other scholars, the discriminant validity of the latent variables was resolved by comparing the correlations among the latent construct with square roots of AVE, shown in Table 4. The square root of the AVE were higher to correlations among latent constructs, indicating sufficient discriminant validity of the research instrument of this study.

Tests of Hypotheses and Evaluation of Structural Path Significance

Having fulfilled the requirements of the measurement model, the structural model was examined. The structural model is where the actual of the hypotheses is carried out. Thus, in this section the correlation between dynamic capabilities and organizational resilience is undertaken. Dynamic capabilities were measured with sensing capability and reconfiguration capability. Next, organizational resilience is assessed with adaptability and agility as its proxies.

Table 5.

Test of Discriminant Validity: Fornell and Larcker (1981) Criterion

	AVE	SC	RC	AD	AG
SC	0.603	0.777
RC	0.642	0.258	0.801
AD	0.595	–0.097	0.053	0.771
AG	0.718	0.084	0.089	0.135	0.847

Source: SmartPLS 3.2.7 output on research data, 2020.

Note: AVE = average variance extracted, SC = sensing capability, RC = reconfiguration capability, AD = adaptability, AG = agility. The off-diagonal values are the correlations between latent variables, while the diagonal values (in bold) denote the square root of AVEs. Bold is to differentiate square root of AVEs from correlation values between latent variables.

At this stage, hypotheses are tested to agree or repudiate the underlying reasoning. Hypotheses were tested by examining the significance of the path coefficients (β) and the coefficients of determination (R² or predictive accuracy) were identified. Then, the predictive relevance of structural model (Q²) was assessed as an alternative to goodness-of-fit, using a nonparametric approach called Stone-Geisser test (Geisser, 1975; Stone, 1974). This test uses a blindfolding procedure (e.g., Hair et al., 2011) to create estimates of residual variances. Positive Q² values confirm the model’s predictive relevance in respect of a chosen construct (Fornell & Cha, 1994; Hair et al., 2011).

The last part of structural analysis (for main effect) is the evaluation of the effect size of each path in the model by means of Cohen’s f ² (Cohen, 1988). The effect size measures if an independent latent variable has ample impact on a dependent latent variable. It is the increase in R² of the Latent Variable (LV) to which the path is connected, relative to the LV’s proportion of unexplained variance (Chin, 1998). Values for f² between 0.020 and 0.150, between 0.150 and 0.350 and exceeding 0.350 indicate that an exogenous LV has a small, medium, or large effect, respectively, on an endogenous LV (Cohen, 1988).

The conditions to either accept or reject the stated hypotheses, for path coefficients (β values), values from 0.10 to 0.29, 0.30 to 0.49 and 0.50 to 1.0 are considered as weak, moderate and strong correlations, respectively (Cohen, 1988). Then, for a two-tailed test, t values greater than 1.96 are significant, while t values less than 1.96 are non-significant (Hair et al., 2011).

First-second hypotheses were clustered and tested in Figure 4 and Table 5. The results of the analyses are reflected in path relationships, path coefficients, standard errors and t-statistics.

H₁: There is a positive and significant relationship between sensing capability and adaptability.

H₂: There is a positive and significant relationship between reconfiguration capability and adaptability.

Figure 4 and Table 6 show the direct path model regarding the relationship between dynamic capabilities (underscored by sensing capability and reconfiguration capability) and adaptability. The R² is 0.462, which suggests that the model variable explains 46% of the variance of the dependent variable which is substantial (Cohen, 1988). The first hypothesis states that sensing capability is significantly related to the criterion variable adaptability result in Table 6 and Figure 3. It reveals that the hypothesis is supported with β = 0.387, t = 8.215 and p < .001. This result shows the significance of sensing capability in enhancing the adaptability of the manufacturing firms. Equally the association between reconfiguration capability and adaptability shows a similar outcome (β = 0.378, t = 7.845, p < .001); thus, hypothesis two is also supported.

Figure 3.

Source: SmartPLS 3.2.7 Output, 2020.

Table 6.

Results of Hypotheses Testing (H₁ – H₂)

Hypotheses	Path Coefficient	Standard Error	T-value	P-value	Decision
SC → AD	0.387	0.047	8.215	.001	Supported
RC → AD	0.378	0.048	7.845	.001	Supported

Source: SmartPLS 3.2.7 output on research data, 2020.

Note: SC = sensing capability, RC = reconfiguration capability, AD = adaptability, t-statistics greater than 1.92 at 0.05 level of significance.

Figure 4.

Source: SmartPLS 3.2.7 Output on research data, 2020.

As a supplement to the R² assessment of all endogenous constructs, the variation in the R² value, when a specific predictor is omitted from the model, is also evaluated. Effect size is the observed variation on the dependent variable due to the omission of an exogenous variable (Chin, 1998). Mathematically,

E f f e c t s i z e (f^{2}) = \frac{R_{var i a b l e}^{2}_{p r e s e n t}^{} - R_{var i a b l e}^{2}_{a b s e n t}^{}}{1 - R_{var i a b l e}^{2}_{p r e s e n t}^{}}

As a guideline, effect size (ƒ²) of 0.02 = small; 0.15 = medium, while 0.35 = large effect of an exogenous latent variable. Effect sizes below 0.02 are counted as zero effects (Cohen, 1988). Table 7 shows the respective effect sizes on the endogenous sub-constructs of the model. Sensing capability emerged as having the strongest effect on adaptability with an f² value of 0.353. In addition, reconfiguration capability has small effect on adaptability with f² values of 0.018 and 0.043.

Hypotheses three and four are clustered and tested in Figure 5 and Table 8.

H_a3: There is a positive and significant relationship between sensing capability and agility.

H_a4: There is a positive and significant relationship between reconfiguration capability and agility.

The path relationship, as presented in Figure 5 and Table 9, shows that there are positive and significant paths between sensing capability and agility (β = 0.367, t = 7.225, p < .001) and reconfiguration capability and agility (β = 0.381, t = 7.765, p < .001). Therefore, alternative hypotheses three and four were supported.

Table 7.

Effect Size of the Latent Variables (H₁ – H₂)

Paths	Correlation Value	Predictive Accuracy (R²)	Adjusted R²	Effect Size (F²)	Remarks on Effect Size
SC → AD	0.387	0.462	0.458	0.353	Moderate
RC → AD	0.378	0.462	0.460	0.043	Small

Source: SmartPLS 3.2.7 output on research data, 2020.

Notes: SC = sensing capability, RC = reconfiguration capability, AD = adaptability, AG = agility, R², 0.19 = weak, R², 0.33 = moderate, R², 0.67 = substantial, Cohen (1988), effect size (ƒ²) of 0.02 = small; 0.15 = medium, while 0.35 = large effect. T-statistics greater than 1.92 at 0.05 level of significance.

Figure 5.

Source: SmartPLS 3.2.7 Output on research data, 2020.

As in the case of adaptability, the analysis above shows that sensing capability has the strongest effect on agility of the firms with an f² value of 0.368, whereas, reconfiguration capability showed a moderate effect on agility of the manufacturing firms.

Table 8.

Results of Hypotheses Testing (H_a3 – H_a4)

Hypotheses	Path Coefficient	Standard Error	T-value	P-value	Decision
SC → AG	0.367	0.047	7.225	.001	Supported
RC → AG	0.381	0.048	7.765	.001	Supported

Source: SmartPLS 3.2.7 output on research data, 2020.

Note: SC = sensing capability, RC = reconfiguration capability, AD = adaptability, t-statistics greater than 1.92 at 0.05 level of significance.

Table 9.

Effect Size of the Latent Variables (H_a3 – H_a4)

Paths	Correlation Value	Predictive Accuracy (R²)	Adjusted R²	Effect Size (F²)	Remarks on Effect Size
SC → AG	0.367	0.347	0.345	0.368	Large
RC → AG	0.381	0.347	0.343	0.213	Moderate

Source: SmartPLS 3.2.7 output on research data, 2020.

Discussion of Findings

The finding that dynamic capabilities significantly and positively correlate with organizational resilience does not come as a surprise, because it corroborates earlier empirical studies (e.g., Kaur & Mehta, 2017; Žitkienė et al., 2015). Specifically, empirical studies on the relationship between dynamic capabilities and firm adaptability show that firm dynamic capabilities amplify their adaptability to proliferating dynamism from the operating environment (e.g., Žitkienė et al., 2015).

In a related study, Kaur and Mehta (2017) studied dynamic capability and how it affects the level of agility of international IT firms in India and came to the conclusion that dynamic capabilities help firms ‘to compete and cooperate with foreign companies as well as to exhibit adaptability’ (p. 8) in a changing business environment.

Likewise, Žitkienė et al. (2015) studied the effect of dynamic capabilities for service innovation, focusing their research on service firms in Slovenia and affirmed that ‘dynamic capabilities are important for service-oriented firms, as they allow firms to identify market opportunities and client needs, take action on those opportunities by organizing available resources, and gain a competitive advantage in the process’ (p. 276).

Correspondingly, Kanten et al. (2017) examined the effect of dynamic capability on organizational agility in the Turkish retailing industry, having collected data from 176 employees through survey method and analysed through structural equation model. The outcome of the analysis revealed that the dynamic capability dimensions positively and significantly influence the retailers’ agility. Furthermore, Kanten et al. (2017) noted that in rapidly changing business environment with a high level of uncertainty, dynamic capabilities of sensing, seizing and transforming must be strategically married with firm’s strategy to promote the firm’s agility.

Contrarily, Teece (2016) noted that agility may sometimes be a ‘fool’s errand’. Enterprise death may, in fact, be the best solution if transformation would leave stakeholders worse off. Because dynamic capabilities require a strategy to be coupled to agility, only when everything is working well together can value be created and captured and durable competitive advantage realized. Also, Eisenhardt and Martin (2000) submitted that dynamic capabilities do not necessarily result in better resilient, since organizational resilience is not directly related to dynamic capabilities but the configuration of resources affected by dynamic capabilities. These findings have important implications for firms. First, the positive relationships among the variables show that dynamic capabilities (sensing and reconfiguration capabilities) point to the possibility of their being useful in boosting the level of resilience among manufacturing firms in Nigeria. Hence, the firms are better off devoting their resources on the enhancement of their dynamic capabilities since it boosts the firms’ resilience.

Conclusion and Recommendations

Conclusion

This study examines the relationship between the dimensions of dynamic capabilities and measures of organizational resilience among manufacturing firms in Rivers State, Nigeria. Dynamic capabilities were decomposed into sensing and reconfiguration capabilities. On the other hand, the measures of organizational resilience were adaptability and agility.

This study empirically proved that dynamic capabilities in terms of sensing capability and reconfiguration capability amplify the manufacturing firm’s resilience in terms of adaptability and agility in a turbulent environment. This indicates that a high level of sensing and reconfiguration capabilities significantly and positively influences the adaptability and agility of the manufacturing firms in Nigeria.

Recommendations

The following recommendations were generated from the study:

Manufacturing firms in Nigeria should take actions to improve their capability to foresee possible changes in the environment, so as to adequately prepare and adjust their internal processes to adapt fully to the changes.

Managers of the manufacturing firms in Nigeria and other developing countries should strategically position their firms to be among the first to identify and acquire external knowledge about their market, technology and industry. This will help the firms to adapt quickly to volatilities from the environment and stay ahead of rivals.

Nigerian manufacturing firms should devise means to effectively integrate new externally sourced capabilities with existing internal capabilities into novel combinations. This will lead to the production of better and improve products and ensure resilient of the firm.

Managers of Nigerian manufacturing firms should monitor possible changes in customers’ needs and preferences so as to react swiftly by modifying processes and/or products to satisfy the needs of the customers.

Limitations and Suggestions for Further Studies

The first limitation of this study is that it concentrated only on the manufacturing sector of the country’s economy, leaving other sectors such as telecommunications, banking, oil and gas among other sectors. However, it is advised that further studies be carried out in these sectors to confirm the generalization of findings.

Second, due to the inaccessibility of the CEO/top managers of the firms, respondents were drawn from managers at the middle and low levels comprising departmental heads and supervisors. Also, out of 109 copies of a questionnaire distributed, only 62 copies were duly filled and returned and used for analyses. However, to minimize the effect of the relatively small size, a bootstrap method was followed (with a bootstrap sample of 5,000). Thus, it was suggested that future studies should consider increasing the sample size.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Appendix

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