SMEs’ E-Commerce Adoption and Customer Responsiveness: A Test of Miles and Snow’s Typology

This study deepens understanding of the operations strategy of SMEs by examining the extent to which the strategic behavior (using Miles and Snow’s typology) of SMEs influences their e-commerce adoption and customer responsiveness. The study employed a cross-sectional survey design with data collected from 320 SMEs using questionnaires. The Partial Least Square Structural Equation Modelling (PLS-SEM) was employed in the data analysis. The study finds that SMEs are indifferent towards strategic behavioral options relative to Miles and Snow’s typology. Among the four strategic behavior options (Analysers, Prospectors, Defenders, and Reactors), the Analyser and Defender strategies were found to influence e-commerce adoption but not customer responsiveness. The prospector strategy was determined to influence both e-commerce adoption and customer responsiveness, whereas the reactor strategy was found not to impact either e-commerce adoption or customer responsiveness. The data gathered was from three selected service sub-sectors; hence, this affects the generalisability of the study for all service sector firms. This study suggests that service SMEs who intend to prioritize e-commerce and customer responsiveness must gravitate towards certain strategic behaviors more than others. The study contributes to the operations strategy literature on SMEs through the establishment of the strategic behavioral attributes of SMEs and how these behaviors influence their e-commerce adoption and customer responsiveness.

Introduction

This paper puts Miles and Snow’s (Miles & Snow, 1978) typology on the strategic behavior of firms to test SMEs’ propensity to adopt e-commerce and their capacity to be customer-responsive. The study is intended to build insights into how the strategic orientation of SMEs influences their customer responsiveness and propensity to adopt e-commerce. The objective of the paper is to contribute to the SMEs’ operations strategy literature by enhancing understanding of the SMEs’ strategic orientations and the implications on their performance.

The strategic behavior of firms involves the long-term vision of the firm translated into operational actions to organize customer worth in the firm’s target markets with the definitive aim of better competitive performance. Every organizational strategy is focus-driven, anticipated to achieve a predetermined goal or arrive at an estimated destination. In the strategic management research literature, significant effort has been made to exploring the linkages between strategic behavior archetypes and the multidimensional construct of organizational performance. In the examination of the strategy-organizational relationship literature, Anwar and Hasnu (2016) reveal that many of the empirical studies have employed strategic typologies in their studies and identify Miles and Snow (1978) framework to be the dominant, most enduring, scrutinized, and validated typology.

A firm’s strategic behavior (customer-oriented, competitor-oriented, and technology-oriented) has an influence on the firm’s performance (Slateret al., 2007). The strategic behavior of firms affects their strategic decisions, including the exploitation of opportunities. SMEs appear to be largely opportunity-driven, which may suggest that they orientate towards selected strategic behaviours due to their embedded characterisation.

SMEs are principally privately owned corporations, partnerships, or sole proprietorship businesses operated with a small number of personnel. There is no comprehensively recognized description of SMEs. Modziet al. (2016) considered the level of assets, turnover and number of employees of a firm in defining SMEs. SMEs are largely not operationally responsive (Sahiet al., 2019). Notwithstanding their significant input to economic development, not much research has been done with respect to the operations performance of SMEs. In particular, very little is known about the strategic behaviour of SMEs as well as the underlying operations performance mechanisms explaining their performance.

Generally, the operations strategy literature has mainly focused on the operations of large firms (Basuony, 2014; Eckert & Gatzert, 2019; Garavanet al., 2019; Huanget al., 2015; Sahoo, 2020; Wonget al., 2020). SMEs’ operations strategy has not been sufficiently explored in the literature. Considering the socio-economic importance of SMEs, an understanding of their strategic behaviour will enable us to appreciate the extent to which these firms are customer-responsive and may exploit innovative opportunities, including the adoption of e-commerce. Thus, the current study seeks to improve understanding and literature knowledge on SMEs operations strategy.

The emphasis on e-commerce and customer responsiveness in the current study is underpinned by their criticality to modern-day business survival. Electronic commerce (e-commerce) has become one of the key models for many businesses to promote their sales domestically and internationally, improve efficiency, and enhance productivity. E-commerce denotes a novel medium used by sellers (businesses) to transact business (exchange of goods and services) with their buyers or consumers over the internet. The use of e-commerce by SMEs can improve business relationships and collaboration, quality, and dispersion of knowledge (Iddris, 2012; Modziet al., 2016; Vaithianathan, 2010). E-commerce has proven to be a beneficial instrument for businesses, users, and society as a whole. E-commerce has been examined in the literature within the context of its contribution to competitive advantage (Chen & Zhang, 2015; Hamadet al., 2018; Khoma & Kostiuk-Pukaliak, 2017; Lestariet al., 2020; Modziet al., 2016; Xuhuaet al., 2019); the benefits it offers businesses (Iddris, 2012; Khan, 2016; Rahayu & Day, 2017; Shahriari & Mohammadreza, 2015; Vaithianathan, 2010); the influence of firms’ strategic values on its adoption (Chenet al., 2017; DeBerry-Spenceet al., 2008; Svobodová & Rajchlová, 2020; Tu & Shangguan, 2018) and the factors affecting its adoption (DeBerry-Spenceet al., 2008; Ifinedo, 2011; Oclooet al., 2018; Rahayu & Day, 2015; Taylor & Owusu, 2012). The literature is silent on the strategic behavior of firms relative to their e-commerce adoption, hence the need to fill this research gap.

Customer responsiveness, on the other hand, emphasizes a firm’s proficiency to react to customer’s queries and carry them out in an apt custom. This involves the celerity it takes to instigate the correspondence in addition to the time it takes to finalize the customer’s request. Firms that are responsive benefit from lead time reduction, inventory management, and agility (Kumar & Singh, 2017). Customer responsiveness upturns firm consciousness of the innovation opportunities that develop within technologically unsettled settings. A firm’s strategies provide models and ideas that help to recognize or discern opportunities that add value to customers. However, to remain competitive, firms need to analyze their customers’ needs and be responsive to them. Also, to be successful at achieving responsiveness, firms need to understand and execute their add-on values to customers. Rant and Cerne (2017) postulate that excellent customer intimacy is all-important for the complete effectiveness of a concealed champion approach.

Several studies have validated the Miles and Snow typology as an appropriate theory for studying firms’ strategic behaviors (Akingbade, 2020; Anwaret al., 2016; Fenișer & Sadeh, 2017; Grimmeret al., 2017; Yanes-Estévezet al., 2018). It is also interesting to note that Miles and Snow’s typology has been employed in many studies, including e-commerce (Carmichael, 2017; Kumaret al., 2018; Svobodová & Rajchlová, 2020). However, less research attention has been paid to SMEs’ strategic behavior and its relationship with e-commerce adoption and customer responsiveness. Consequently, this study is intended to find out how SMEs, on the basis of their strategic behavior (using Miles and Snow’s typology of strategies), differ in their approach to e-commerce adoption and their ability to effectively respond to customer needs. This study is important because it will help improve the knowledge and understanding of the operations strategy of SMEs in the extant literature, which is woefully limited. This knowledge could also support the improvement of SMEs’ operations performance, which forms the basis for their improved contribution to national economies.

This paper proceeds as follows: Section two (2) reviews existing literature on the strategic behavior of SMEs, e-commerce adoption, customer responsiveness, and Miles and Snow typology. Furthermore, Miles and Snow’s model has been discussed to pave the way for the development of the conceptual framework for the current study. Section three (3) describes the methods employed for the study, and Section four (4) presents the data analysis. Section five (5) discusses the findings of the study, whilst Section six (6) and Section seven (7) present the limitations and implications of the study, respectively.

Theoretical Foundations

Theoretical Review

Technology-Organizational-Environment (TOE)

The Technology-Organizational-Environment (TOE) theory and Theory of Reasoned Action (TRA) are employed to provide the theoretical foundations for the study. Three major theoretical approaches (i.e., environmental, technological, and organizational/internal) influence SMEs to positively implement e-commerce as a local or international distribution channel (Sanchez-Torres & Juarez-Acosta, 2019). Abed (2020) theorizes that the three dimensions, Technology, Organization, and Environment, are noteworthy factors for SMEs’ adoption of social commerce. Technological factors (security concern and perceived usefulness), organizational factors (organizational readiness and top management support), and environmental factors (consumer and trading partner pressures) have a momentous impact on behavioral intent to adopt social commerce by SMEs. However, trading partner pressure (environmental), followed by top management support (organizational), and perceived usefulness (technological) have the greatest substantial impact on SMEs’ behavioral intent to employ social commerce. This implies that environmental factors by Abed (2020) are the most significant influencers than technology and organizational factors of social commerce adoption.

Awa and Ojiabo (2016) posits that technical know-how, availability of ICT infrastructures, perceived compatibility, security, size, and perceived values of the firm are important technological determining factors for the adoption of ERP by SMEs. Firm size, demographic composition, scope of business operations, and subjective norms are organizational features that impact SMEs’ adoption of ERP. Firm size is a dire cause in e-commerce and ERP adoption. Management’s knowledge about innovation, experience, and demographic differences will equally impact the adoption of technology. This implies that the scope of business operations is a dire adoption influence; nonetheless, it does not underwrite the adoption of ERP. Competitive pressure, trading partners’ readiness, and external support are environmental adoption factors. However, Awa and Ojiabo (2016) conclude technological factors to drive the adoption of ERP by SMEs more than by organizational and environmental factors. Therefore, this study seeks to find out if organizational factors and strategic behavior (Miles and Snow typology) drive innovation adoption more than technological and environmental.

Theory of Reasoned Action (TRA)

TRA can be expanded to hypothesize the human behavioral pattern in the decision-making strategy on the exploitation of a new innovation or technology. It has the ability to enlighten the individual behavior, such as the utilization of new innovation and whether it is influenced by behavioral intentions, the individual’s attitude concerning the behavior, the idiosyncratic norms connecting the conduct of the behavior, and the individual’s opinion of the comfort with which the behavior can be carried out. Therefore, the merit of this theory in expounding behavior depends on the extent to which people can have a large degree of authority over the behavior, and this has led to the conclusion that TRA is a very strong theory over several models that have been drawn on in technological innovation adoption studies in spite of the fact that TRA has not been comprehensively exploited in studies such as technology adoption and circulation in the field of Information Systems and ICT in general (Otienoet al., 2016; Fawzy & Salam, 2015). This theory is, therefore, appropriate for this study because literature (Oclooet al., 2018; Rahayu & Day, 2015; Taylor & Owusu, 2012; Ifinedo, 2011) has proven that owner/manager characteristics touch on the adoption of e-commerce. Therefore, there is a need to appreciate the influence of the behavioral intention of these owners/managers on the strategic behavior of firms.

Strategic Behaviour of Firms

Miles and Snow (1978) developed a typology of strategic behaviour of firms. This typology is the oldest and most accepted typology in the study of organizational strategy. The typology offers an influential tool for categorising organizations by their strategic decisions.

The typology theorizes that organizational strategy is strongly influenced by founder personality and organizational culture, and this characteristic makes the Miles and Snow typology distinct in that it is the only seminal theory that takes into consideration owner influence and organizational culture. This typology presents a suitable framework for analysing the operations of Small and Medium Sized firms (SMEs). SMEs tend to have strong founder influence and are often controlled by the founder throughout the organization’s existence (Mayfieldet al., 2007).

The major challenge faced by many SMEs in Ghana is how to stay competitive and globalize their operations. Donkoret al. (2018) argue that SMEs in urban areas possess moderate innovation skills. According to Savrulet al. (2014) small firms compared to large firms are more eager to adopt e-commerce for the many benefits it offers. E-commerce adoption in contemporary years has abetted numerous firms to commute information and to incorporate into their operations properly in order to better serve their customers (Ramanathanet al., 2012). Only a few SMEs in Ghana deploy technology-based marketing strategies in reaching their customers. The trend is the use of social media (Facebook, Twitter, and Instagram) other than corporate websites and emails because social media is interactive in nature, and a social media page is less difficult and relatively cheap to set up and manage compared to a corporate website (Dzisi & Ofosu, 2014). Some SMEs ride on existing e-commerce platforms such as Jumia and Kikuu to be able to reach out to numerous consumers because of the cost involved in owning their own website and its maintenance. The difference in technology readiness of firms to adopt e-commerce could lead to the argument that strategic behavior or orientation of a firm can have an influence or play a role in a firm’s readiness to adopt a technology. SMEs, especially those in the trading sector, prefer to carry out their trading activities through traditional mediums instead of adopting electronic commerce. Therefore, there is more to be done with regard to SMEs’ (trading sector) technological readiness to adopt IoT (Parra-Sánchezet al., 2021). SMEs may also adopt emerging technologies to improve their operations to improve customer responsiveness. Strategic response to the needs of customers involves changes to the firm’s strategic behaviour, and such responses may take many forms depending on the environment in which it operates (Carmichael, 2017).

Miles and Snow (1978) suggested that firms in broad-spectrum develop fairly stable forms of strategic behavior in order to realize a good orientation with the apparent environmental settings. They centered their typology on three sets of problems challenging every firm: entrepreneurial (the choice of products and markets), engineering (the choice of technologies for production and distribution), and administrative (the choice of areas for future innovation and rationalization of existing structure and processes). Four different strategies are defined by the authors for firms. These are Prospectors, Defenders, Analysers, and Reactors. A firm is a prospector, defender, analyzer, or reactor, depending on how it approaches the three problems that challenge the firm. The four strategies described by Miles and Snow can be applied as a framework over which to perfect and apprehend SMEs’ decisions as regards the adoption or non-adoption of e-commerce as well as their response to the changing needs of customers (Carmichael, 2017; Oltra & Flor, 2010).

The Analyser Strategy

Analyzers, according to (Linet al., 2014), avoid the risk that occurs in Defender and Prospector and take advantage of Defender and Prospector by connecting their essential competencies. Similar to Prospectors, they devote a great deal of responsiveness to innovation and concurrently run several stable businesses, just as defenders pay attention to launching products. Production, Marketing, and Research & Development (R&D) capabilities are prerequisites to adopting an analyzer strategy. That is, its capability allocation principle is one in which the production, marketing, and R&D capabilities should be almost equal to each other.

Klingeret al. (2019) also opine that Analysers are purposefully exploratory in nature. The Analyser makes an effort to discover new products and venture into market opportunities while concurrently preserving the firm’s principal traditional products and customers. Analysers grow into new marketplaces and are risk takers only in a way that builds on their prevailing capabilities. Analysers are evident in businesses that work in two types of product-market areas: relatively stable and permanent change. Analysers endeavour to control risk while exploiting opportunities Miles and Snow (1978).

It is interesting to understand that, analysers jump unto a new innovation only after success has been demonstrated for that particular innovation. They also embark on environmental skimming in an effort to study how to accomplish and defend stability amongst contradictory demands for litheness and solidity. Consequently, an analyser is expected to adopt e-commerce after skimming the environment of adopters of e-commerce to protect the firm from features of indecision in the market. Analysers are the second driving force in the market, and so their efforts to exploit on market prospects are restricted (Carmichael, 2017). Hence, it is hypothesized that:

H1a: The pursuit of the analyser strategy influences SMEs’ adoption of e-commerce.

Pehrsson (2011) makes known that customer responsiveness encompasses relationships with customers, solutions to customers’ problems, and after-sales services. He adds that in an uncertain market characterized by changing competitive patterns, customer responsiveness must be high. Analysers are slow to reacting to changes in dynamic or competitive markets (Carmichael, 2017). Soutaret al. (2007) identified service firms that pursue analyser strategy to have significant relationship with customer orientation and not customer responsiveness. This means that Analysers will be slow in responding to the changing needs of customers; hence, the hypothesis:

H1b: The pursuit of the analyser strategy negatively influences SMEs’ customer responsiveness.

The Defender Strategy

Defenders are organizations that pay attention to growth of new products by a conventional means. They typically compete on quality and price rather than new markets or products and concentrate on revamping the competence of their present processes. Defenders espouse a unified arrangement to preserve control over effectual facilities that pay attention to the core of the business or service goals Miles and Snow (1978). Hawrysz (2020) states that Defenders take on a lot of proper scheduling, gather and scrutinise huge volumes of records on service necessities, appraise alternatives to meet those necessities, and custom progressive procedures to balance the costs and benefits of each choice. Defenders cautiously assess any suggested modifications in technology and actions before taking action. Defenders generally aim their products or services for an openly defined market and accentuate a steady set of products and customers. They repetitively try to apprise their current technology to preserve efficiency. Innovative change, growth, and diversification are accomplished gradually through market infiltration (Hawrysz, 2020).

Haj Youssef and Christodoulou (2017) also describe defenders to be firms that strive in stable domains and unchanging environments, and all their actions are in line with the constancy of the external environment. Defender firms have forethought actions and function in a way that emphasizes proficiency in operations and secure internal orientation. However, (Lumbantoruan & Pujangkoro, 2020) identified defenders to be firms that wrestle to secure a certain market share and afterward toil to conserve it. They are, therefore, likely to adopt e-commerce because it is an approach that helps reduce operating costs while maintaining existing customers. Due to the exceptional services that they provide to their customers, they are also likely to be attentive to the needs of their customers. Hence, we hypothesize that:

H2a: The pursuit of the defender strategy influences SMEs’ adoption of e-commerce.

A firm with strong customer orientation culture endeavors to improve its capabilities in responding to customers’ changing needs and having positive reviews that will improve its rating distribution (Hendaret al., 2018). Defenders provide exceptional services to customers. Due to the exceptional services that they provide to their customers, they are also likely to pay attention to the needs of their customers (Lumbantoruan & Pujangkoro, 2020) hence, the hypothesis:

H2b: The pursuit of the defender strategy influences SMEs customer responsiveness.

The Prospector Strategy

Prospectors are firms with flexible, non-formal organizational structures that always look out for new market opportunities: innovation processes and new product development. They are creators of change and risk-takers. They are innovative, technology inclined, and are more concerned with human resources (Yanes-Estévezet al., 2018).

Cassolet al. (2019, p. 109) describe prospectors as “organizations that continuously seek for new product and market opportunities; sources of change and creation. … they aim to innovate through extensive and non-intensive planning, the main focus being on innovation and not on efficiency; decentralized control, permitting the monitoring of the environment.”

Chaimankong and Prasertsakul (2012) profess prospectors to be organizations that are characterized by flexibility and step into wholly new markets, taking momentous risks. Their organizational structure has control which is more decentralized. They work to feat innovative product and market prospects and maintain the reputation as innovators in product and market development. They also are not restricted to their modern product line and are habitually the inventors of transformation in their industry. A disadvantage of this strategy is that, it may have trouble in being operational as insecurity surges. Younger managers may comprehend the prerequisite for greater technological amalgamation, which will surge the possibility that they will be more courageous and innovative in the adoption of technological solutions and strategies. The positive and antagonistic market stand taken by prospectors supports thriving in markets characterized by a high level of volatility, hence, the likelihood to adopt e-commerce (Carmichael, 2017). It is therefore hypothesized that:

H3a: The pursuit of the prospector strategy influences SMEs’ adoption of e-commerce.

According to Harrafet al. (2015) as cited in (Jermsittiparsertet al., 2019), customer responsiveness is strongly related to information and it is essential to put customer information to appropriate use. When customers’ requirements change, they expect greater responsiveness to these changes. Prospectors do focus on product and process technology innovation and will, therefore will go to all extent in responding to customers’ changing demands and may not even consider cost factors in fixing customers’ changing needs Chaimankong and Prasertsakul (2012). On this account, we hypothesize that:

H3b: The pursuit of the prospector strategy influences SMEs customer responsiveness.

The Reactor Strategy

Reactors are organizations without a clear strategy or operational approach. This strategy usually operates dominantly in stable markets. Reactors do not have a clearly defined strategy; their organizational structures are inconsistent with the selected strategy, and such organizations often ignore changes in the environment (Aleksic & Jelavic, 2017).

Though they do not have a clearly defined strategy, their response to market development is through altering their market positions in limited ways. They are characterized by apathy and lethargy of actions as they recognize opportunities in their operating environment. They prove to be unable to put actions into practice to exploit opportunities unless pressured by the competition. Reactors exhibit a pattern of adjustment that is both inconsistent and unstable. The organization maintains a current strategy-structure relationship despite overwhelming changes in environmental conditions (Klingeret al., 2019). Reactors will not support market vitality or competitive force in a considerable style. Reactors are greatly exposed to adopting e-commerce, but their adoption is grounded on their acknowledgement of modifications in their industry toward grander execution of technology. They are likely to adopt e-commerce in as much as it would profit their operations. They are not likely to incorporate e-commerce into their operations because of technological innovation. Even those who have employed e-commerce apply technology either to develop their operations or because of the varying requests of customers, which indicates a reaction to an imminent change rather than a practical stance on technology (Carmichael, 2017). It is therefore hypothesized that:

H4a: The pursuit of the reactor strategy negatively influences SMEs’ adoption of e-commerce.

Reckeret al. (2017) posit that all-embracing retorts to varying customer necessities encourage lowered retort efficiency, signifying that widespread responses entail extra response determinations, which decreases retort efficiency. Reactors do not consider local market conditions as a priority. Reactors will not orient with either market vitality or competitive force in a significant way (Carmichael, 2017). This implies that Reactors may not be responsive to the changing needs of their customers. Hence, the following hypothesis will be tested:

H4b: The pursuit of the reactor strategy negatively influences SMEs’ customer responsiveness (Fig. 1).

Fig. 1. Conceptual framework.

Methods

The purpose of this study was to determine the nature of relationships that exist between the strategic behavior of firms, customer responsiveness, and e-commerce adoption. To achieve this purpose, the study employed the quantitative approach using a cross-sectional study design. The study population included SMEs involved in retailing. Participating firms included firms involved in the sale of laptops, phones, and other electrical gadgets (Information and Communication), firms involved in the selling of home, office, and school supplies (Administrative and Support Services), and firms involved in fashion retailing including firms who are into the sale of clothing, shoes and fashion accessories (other services activities). These sectors are areas where e-commerce and customer responsiveness are typically critical for success. The target respondents from these firms were owners/managers, proprietors, company administrators, and customer service personnel. The choice of this category of respondents was influenced by the knowledge that the operational strategy of these firms is largely influenced by the decisions taken by these personnel. These individuals are deemed to be the sources of operations strategy for small firms; hence, they represent an appropriate source of data for the study (Stockdale & Standing, 2006). 384 SMEs were sampled from the fashion, computing, electronics, phones and tablets, home and office supplies, and fashion industries. The firms were identified through convenience sampling based on their readiness and willingness to partake in the study. The purposive sampling technique was employed to select the survey respondents.

Data for the study was collected using questionnaires, which were administered largely through face-to-face industry visits. The questionnaire had four sections. The first section solicited information on respondents’ demographics such as gender, age, educational qualification, position, and other general information about the respondent. The second section contained a 5-point Likert Scale measuring the four strategic behaviors of firms. The third section collected data to help ascertain the tendency to which these firms were prone to e-commerce adoption, whilst the fourth section evaluated the extent of customer responsiveness to the firms.

Analysis of Data

Analysis of Demographic Data

A total of 410 questionnaires were distributed, out of which 320 respondents returned their responses, leaving out 28 firms who failed to return their responses, generating a response rate of 92%, which is deemed extremely good (Baruch & Holtom, 2008; Lindemann, 2021) The 320 questionnaires were subjected to data cleaning process following which 300 questionnaires were considered usable for inclusion in the analysis. The demographic data analyzed is summarised and presented in Table I.

Table I. Demographic Characteristics of Respondents

Demographic characteristics	Frequency	Percent (%)	Cumulative percent (%)
Gender
Male	189	63	63
Female	111	37	100.0
Total	300	100
Age
18–24	33	11.0	11.0
25–34	191	63.7	74.7
35–44	60	20.0	94.7
45–54	7	2.3	97.0
55–64	6	2.0	99.0
65–above	3	1.0	100.0
Total	300	100
Education
Primary education	8	2.7	2.7
Secondary education	50	16.7	19.3
Tertiary	205	68.3	87.7
Professional	37	12.3	100.0
Total	300	100
Position
Owner	106	35.3	35.3
Manager	56	18.7	54.0
Customer service personnel	103	34.3	88.3
General manager	8	2.7	91.0
IT manager	27	9.0	100.0
Total	300	100
Years in Business
0–5 years	161	53.7	53.7
6–10 years	96	32.0	85.7
11–15 years	29	9.7	95.3
16–20 years	9	3.0	98.3
above 20 years	5	1.7	100.0
Total	300	100
Number of Employees
1–5	100	33.3	33.3
6–30	195	65.0	98.0
31–100	5	1.7	100.0
Total	300	100

Strategic Behaviour of SMEs

Data was analyzed using Partial Least Square Structural Equation Model (PLS-SEM) using the Smart PLS software. PLS-SEM was adopted for this study because the sample size is small and the data are non-normally distributed (Hairet al., 2017). To understand the strategic orientation of the SMEs, we employed the Average Mean Score. The Average Mean Scores obtained for the dimensions of the strategic behavior constructs are summarised in Table II.

Table II. Construct and Measurement Item’s Means and Standard Deviation

Items	Min	Max	Mean	SD
Strategic behaviour			3.23	0.91
Prospector
Pros 1	1	5	3.12	1.34
Pros 2	1	5	3.60	1.24
Pros 3	1	5	3.28	1.11
Pros 4	1	5	3.42	1.25
Pros 5	1	5	3.52	1.17
Pros 6	1	5	3.47	1.26
Pros 7	1	5	3.53	1.13
Average			3.42	1.2
Analyser
Anal 1	1	5	3.45	1.07
Anal 2	1	5	3.54	1.10
Anal 3	1	5	3.36	1.24
Anal 4	1	5	3.38	1.10
Anal 5	1	5	3.26	1.17
Anal 6	1	5	3.41	1.11
Anal 7	1	5	3.25	1.17
Average			3.38	1.14
Defender
Defe 1	1	5	3.04	1.39
Defe 2	1	5	3.38	1.37
Defe 3	1	5	2.96	1.16
Defe 4	1	5	2.73	1.28
Defe 5	1	5	2.65	1.33
Defe 6	1	5	2.84	1.33
Defe 7	1	5	3.09	1.29
Average			2.96	1.31
Reactor
Reac 1	1	5	3.35	1.26
Reac 2	1	5	3.31	1.27
Reac 3	1	5	3.35	1.22
Reac 4	1	5	3.13	1.39
Reac 5	1	5	3.01	1.36
Reac 6	1	5	2.99	1.24
Reac 7	1	5	3.09	1.30
Average			3.18	1.29

On the basis of the Likert Scale employed for the data collection, a mean score below 2.5 suggests that respondents disagreed with being oriented towards a strategic behaviour relative to Miles and Snow taxonomy. Mean scores between 2.5 and 3.4 suggest respondents are indifferent towards a strategic behavior, and mean scores from 3.5 and above suggest respondents agreed to be oriented towards that strategic behavior. It is observable from Table II that the average mean scores for the four strategies, Prospector, Analyser, Defender, and Reactor, were 3.42, 3.38, 2.96, and 3.18, respectively. This is an interesting finding suggesting that SMEs do not demonstrate any particular strategic behavior, as all the mean scores fell between 2.5 and 3.4. SMEs are, therefore, indifferent towards all four strategies and may suggest that their pursuit of a particular strategy could be influenced by external environmental factors.

Measurement Model Assessment

The measurement model assessment was purposefully carried out to analyze the assumptions relative to the reliability and validity of the structural measurement model (Ahmedet al., 2017). In assessing the measurement model for this study, indicator reliability and internal consistency reliability were carried out to help examine the reliability of the constructs of the model. Convergent validity, as well as discriminant validity, were carried out to ascertain the validity of the model.

Indicator Reliability

An indicator with a standardized outer loading estimate of 0.700 and above is considered reliable (Sarstedtet al., 2014). However, outer loadings between 0.40 and 0.70 are retained if only their exclusion will reduce the Cronbach alpha and the Average Variance Extracted (AVE) of the matching construct. Consequently, indicators with outer loadings less than 0.700 are expected to be included in the analysis (Aibinu & Al-Lawati, 2010; Avkiran, 2018; Hairet al., 2021; Sarstedtet al., 2014). Subsequently, submitting items with their loadings to this test, Reac 1 and Reac 3 were removed from the analysis. The outer indicator loadings of the constructs are exhibited in Table III and Fig. 2.

Table III. Indicator Loadings of the Constructs

Indicator	Analyser	Customer	Defender	Ecommerce	Prospector	Reactor
Anal 1	0.7609
Anal 2	0.8418
Anal 3	0.8128
Anal 4	0.8413
Anal 5	*0.6811
Anal 6	0.8577
Anal 7	0.8204
CusR 1		0.8817
CusR 2		0.9210
CusR 3		0.8637
CusR 4		0.8341
CusR 5		0.9062
CusR 6		0.9003
CusR 7		0.7412
Defe 1			0.7339
Defe 2			*0.6781
Defe 3			0.8474
Defe 4			0.7492
Defe 5			0.8662
Defe 6			0.8438
Defe 7			0.8150
Ecom1				0.8282
Ecom 2				0.7624
Ecom 3				0.8080
Ecom 4				0.8437
Ecom 5				0.8425
Ecom 6				0.8695
Ecom 7				0.8071
Pros 1					*0.6170
Pros 2					0.8825
Pros 3					0.8483
Pros 4					0.8824
Pros 5					0.7616
Pros 6					0.8267
Pros 7					0.8495
Reac 2						*0.6623
Reac 4						0.8064
Reac 5						0.9252
Reac 6						0.8651
Reac 7						0.8242

Fig. 2. Indicator outer loadings.

Internal Consistency Reliability

The Cronbach alpha measure was used to estimate the internal consistency of the constructs, and the results are presented in Table IV. The Cronbach alpha values, as submitted in Table IV, ranging between 0.897 and 0.944, confirm that all the variables indicated satisfactory internal consistency. Cronbach alpha greater than or equivalent to 0.70 indicates higher levels of reliability (Bajpai & Bajpai, 2014); hence, our variables exhibited very high levels of reliability.

Table IV. Construct’s Cronbach’s Alpha, rho_A, Composite Reliability (CR) and Average Variance Extracted (AVE)

Constructs	Cronbach’s alpha	rho_A	Composite reliability	Average Variance Extracted (AVE)
Analyser	0.9095	0.9230	0.9273	0.6470
Customer responsiveness	0.9440	0.9557	0.9543	0.7498
Defender	0.9023	0.9220	0.9219	0.6292
E-commerce adoption	0.9209	0.9271	0.9365	0.6784
Prospector	0.9129	0.9257	0.9317	0.6633
Reactor	0.8970	1.1040	0.9111	0.6745

Convergent Validity

The AVE of the constructs was used to measure the convergent validity. The threshold of the AVE is 0.50 or more because 50% differences or more in the indicators should be explained by the construct Hairet al. (2014). It is evident in Table IV that all the constructs satisfied the minimum threshold requirement with AVE values extending from 0.629 to 0.750 to indicate the presence of sufficient convergent reliability.

Discriminant Validity

In analysing the discriminant validity of the constructs, the HTMT was used. The rule of thumb is that HTMT values should be below the threshold of 0.9 or 0.85. The summary results presented in Table V confirm that all constructs exhibited good discriminant validity based on the HTMT method.

Table V. Assessment of Discriminant Validity (HTMT Values)

Constructs	Analyser	Customer responsiveness	Defender	E-commerce adoption	Prospector	Reactor
Analyser
Customer responsiveness	0.0634
Defender	0.1034	0.1000
E-commerce adoption	0.1889	0.3975	0.2751
Prospector	0.0663	0.1845	0.0906	0.2567
Reactor	0.0613	0.0354	0.0826	0.0553	0.0886

Structural Model Assessment

The evaluation of the structural model comprises an assessment of the predictive power and the associations among the constructs of the model. The specific analyses were multicollinearity assessment, path significance assessment, coefficient of determination, effect size, predictive relevance, and model fitness. The assessment of the structural model in the current study adhered to the technique recommended by Hairet al. (2014).

Multicollinearity Assessment

The multicollinearity test examines and averts path coefficient partialities, which may ensue from comprising the estimates and predictors that reveal collinearity. The variance inflation factor (VIF) was employed to test for multicollinearity in the structural equation model. Hairet al. (2014) and Sarstedtet al. (2014) advocate for a satisfactory VIF of a measurement item to be below or equal to five (x ≤ 5) as a VIF value exceeding 5 points to a possible collinearity issue. The results in Table VI show that the VIF for all the predictors are below 3.0. This means that the problem of multicollinearity does not exist among the predictor constructs.

Table VI. Multicollinearity Assessment (VIF)

Constructs	E-commerce adoption	Customer responsiveness
Analyser	1.0091	1.0091
Defender	1.0111	1.0111
Prospector	1.0093	1.0093
Reactor	1.0113	1.0113

Assessment of Path Significance

The p-value was employed for the assessment of the significance levels of the hypothesised relationships. The p-value is the chance of mistakenly rejecting a true null hypothesis. Extant literature estimates relationships at 5% significance level. Sarstedtet al. (2017), however, maintain that a significance level of 0.10 (10%) is acceptable for exploratory research. The authors add that the selection of a significance level and type of test (one or two tails) hangs on the field of study and the objectives of the study. 5% significance level using a two-tailed test was employed in the current study. Thus, a hypothesis with p-value below or equal to 0.05 or an equivalent t-statistics value of 1.96 or higher is considered significant. The results of the valuation of the path significance of the hypothesized relationships are presented in Table VII and Fig. 3.

Table VII. Test of Direct Relationships

Hypothesis	Path	Path coefficient	P value	T-statistics	Decision
H1a:	Anal -> Ecom	0.159	0.001	3.202	Supported
H1b:	Anal -> CusR	0.02	0.771	0.292	Not supported
H2a:	Defe -> Ecom	0.257	0	5.269	Supported
H2b:	Defe -> CusR	0.102	0.084	1.734	Not supported
H3a:	Pros -> Ecom	0.233	0	4.54	Supported
H3b:	Pros -> CusR	0.181	0.002	3.055	Supported
H4a:	Reac -> Ecom	0.032	0.658	0.442	Not supported
H4b:	Reac -> CusR	−0.009	0.91	0.113	Not supported

Fig. 3. Test of direct relationships.

Table VII shows the path relationships hypothesized for the four strategic behaviors and e-commerce adoption, as well as customer responsiveness. Beginning with hypothesis H1a, which states that the analyzer strategy influences e-commerce adoption, a statistically significant relationship between the analyzer strategy and e-commerce adoption was established, given a p-value = 0.001. Hypothesis H1b states that the analyzer strategy inhibits customer responsiveness. Given the p-value = 0.771, a non-significant relationship between the analyzer strategy and customer responsiveness was established, suggesting that the analyzer strategy does not negatively influence a firm’s customer responsiveness. Hypothesis H2a: defender strategy influences e-commerce adoption yielded a p-value = 0.000, which indicates a statistically significant relationship between the defender strategy and e-commerce adoption. This finding implies that SMEs pursuing the defender strategy are more likely to adopt the e-commerce business model. On hypothesis H2b: the defender strategy influences customer responsiveness, the analysis resulted in a p-value = 0.084, indicating a statistically non-significant relationship between the defender strategy and customer responsiveness. In other words, the defender strategy does not influence a firm’s customer responsiveness.

The path relationship hypothesized for H3a: prospector strategy influences e-commerce adoption had a p-value = 0.000, indicating a statistically significant relationship between prospector strategy and e-commerce adoption. Thus, firms pursuing the prospector strategy have a higher propensity to adopt e-commerce as a business model. Again, the path relationship hypothesized for H3b, prospector strategy influences customer responsiveness, had a p-value = 0.002, indicating a significant relationship between the prospector behavior and customer responsiveness. The test for hypothesis H4a: the reactor strategy inhibits e-commerce adoption generated p-value = 0.658, indicating a non-significant relationship between the reactor strategy and e-commerce adoption. Finally, the path relationship hypothesized for hypothesis H4b, the reactor strategy inhibits e-commerce adoption, has a p-value of 0.910, indicating a non-significant relationship between the reactor strategy and customer responsiveness.

Coefficient of Determination (R2)

The coefficient of determination (R²) was used to evaluate the structural model’s predictiveness. It was employed to quantify the model’s predictive power by totaling the effect of all the independent variables (exogenous latent variables) on the dependent variables (endogenous latent variables), as recommended by Hairet al. (2017). Table VIII presents a summary of the results.

Table VIII. R² and Adjusted R² of Constructs

Constructs	R2	Adjusted R2
E-commerce adoption	0.156	0.145
Customer responsiveness	0.043	0.030

The results in Table VIII reveal that e-commerce adoption has an R² value of 0.156, suggesting that the combined effect of the strategic behavior (Analyser, Defender, Prospector, and Reactor) on e-commerce adoption is approximately 16%. From this finding, a deduction can be made that the strategic behavior of SMEs contributes to SMEs’ adoption of e-commerce by 15%. Customer Responsiveness, on the other hand, had an R² value of 0.043, which means that the combined effect of the strategic behaviors on Customer Responsiveness is approximately 4%. As a guideline (Hairet al., 2011; Ringle & Sinkovics, 2009), R² values of 0.75, 0.50, and 0.25 represent substantial, moderate, and weak, respectively. By implication, the strategic behavior constructs have a weak combined effect on both E-commerce adoption and Customer Responsiveness since both R² values were below 0.25.

Effect Size Assessment (F2)

Effect size indicates the disparity in the R², which ensues when an indicated independent variable is misplaced in the model. It is used to determine whether the misplaced independent variable has a strong effect on the dependent variable in terms of the R². This is done in addition to evaluating the R². It is estimated by computing the (Cohen, 1988) f², and the procedures for evaluating f² are that values of 0.02 signify a small effect, values of 0.15 signify a medium effect, and values of 0.35 signify a large effect of the independent variable. f² values below 0.02 point out that there will be no effect with its exclusion from the model (Hairet al., 2021; Leguina, 2015).

Table IX presents the f² values. The omission of the reactor strategy on e-commerce adoption will not have any effect on the model. Likewise, the removal of analyzer, defender, and reactor strategies on customer responsiveness will have no effect on the model. However, the f² values of analyzer, defender, and prospector on e-commerce adoption indicate that their omission will have a small effect on the model. Similarly, the omission of a prospector strategy on customer responsiveness will have a small effect on the model.

Table IX. F-Squares of the Study Constructs

Constructs	f 2	Customer responsiveness
	E-commerce adoption
Analyser	0.030	0.000
Defender	0.078	0.011
Prospector	0.064	0.034
Reactor	0.001	0.000

Predictive Relevance (Q2)

Stone-Geisser’s Q² value (Geisser, 1974; Stone, 1974) is employed in assessing the predictive relevance of the constructs. A PLS path model that exhibits predictive relevance accurately predicts data not used in the model estimation. Q² value measures how the PLS-SEM model envisages the data points of indicators in a reflectively measured model. As a rule of thumb, (Hairet al., 2021) posit that Q² values larger than zero indicate that the independent variables have predictive relevance for the dependent variables under consideration. Table X shows a Q² values of 0.100 (E-commerce adoption) and 0.028 (Customer Responsiveness) for the model to direct that the predictive relevance of the constructs is satisfactory.

Table X. Q² of Constructs

Constructs	Q2(=1−SSE/SSO)
E-commerce adoption	0.100
Customer responsiveness	0.028

Model Fitness

The model fitness was examined using the Root Mean Square Residual Covariance (RMS_theta). This is an alternative to Standardized Root Mean Square Residual (SRMR) but relies on covariance. (Hairet al., 2021) argue that the threshold of SRMR is likely to be low for a PLS-SEM model because the inconsistency amongst the experimental correlations and model-implied correlations does not play the same role in CB-SEM and PLS-SEM. CB-SEM’s target is to minimize the discrepancy whereas that is not the case in PLS-SEM. Consequently, this study adopted the (RMS_theta) which has a threshold of 0.12 and below to indicate a well-fitting model. The result presented in Table XI confirms that the model has a good fit.

Table XI. Model Fit Result

RMS theta	0.122

Discussion of Findings

The Strategic Behaviour of SMEs Relative to Miles and Snow’s Taxonomy

The study examined the strategic behavior of firms relative to Miles and Snow’s taxonomy. The summary of results in Table II suggests SMEs are indifferent towards strategic behavioral options. The mean scores of the constructs reveal that these firms are not oriented towards any particular strategic behavior. The evidence may be interpreted to mean that SMEs appear not to pursue any clearly defined operations strategy which would direct their long-term vision and improve their competitiveness. Perhaps the informal nature in which many of these firms operate may be a contributory factor to why they are not strategy-sensitive. It may seem that the perceived formal strategic behavior definition by firms is non-existent among SMEs and may be the preserve of large firms. This evidence makes it imperative, the need for a theory to be developed regarding SME formalization as the lack of strategic orientation is routed in the informal nature of SME operations (Yanes-Estévezet al., 2018; Yee & Platts, 2006; Zubaedahet al., 2013). The lack of strategic behavioral orientation of SMEs, as established in this study, lends support to the findings by Yanes-Estévezet al. (2018) that there is no pure strategic behavior associated with SMEs.

The Relationships between SMEs’ Strategic Behavior, E-commerce Adoption, and Customer Responsiveness

Analyser Strategy, E-commerce Adoption and Customer Responsiveness

Contending on existing literature, this study hypothesized that the pursuit of the analyzer strategy influences SMEs’ adoption of e-commerce (H1a). A test of this hypothesis revealed that the analyzer strategy has a positive and statistically significant relationship with E-commerce adoption (p-value = 0.001; coefficient = 0.159). Thus, SMEs pursuing the analyzer strategy would be more inclined towards the adoption of e-commerce in their business operations. This evidence confirms the findings from Feyissa and Sharma (2017), who established that analyzers engage in innovative activities such as product development and market diversification, and e-commerce adoption by these organizations is in consonance with such innovative business processes. This finding also confirms the theory that analyzers are responsive to innovation and are efficient at their operations (Linet al., 2014). It is therefore not surprising to find out that the analyser’s behavior positively influences the adoption of innovations such as e-commerce as an operations strategy aimed at improving the firm’s performance.

Moreover, the risk-taking and purposeful exploratory nature of the analyzer, as posited by Klingeret al. (2019), has been consolidated with the findings in this study. For firms to employ a particular technology efficiently, such exploitation and study need to be done to understand the pros and cons of the technology before it is introduced into the operations of the firm. Thus, analyzers demonstrating this characteristic could form the basis for their adoption of e-commerce.

Hypothesis H1b states that the pursuit of the analyzer strategy contributes negatively to SMEs’ customer responsiveness. The result shows a rather positive but statistically non-significant relationship (p-value = 0.771, path coefficient = 0.020) between the analyzer strategy and customer responsiveness. Impliedly, the analyzer strategy does not influence customer responsiveness. Hence, H1b is rejected. Although Carmichael (2017) established that analyzers react slowly to the changing needs of their customers in competitive markets, this paper failed to establish the basis for supporting such a claim. The analyzer is noted to focus on maintaining its existing customers using efficient operations whilst swiftly responding to competitors’ novel product success and innovations. This duality compels the analyzer to acquire new knowledge about its market whilst maintaining its core functions. This two-fold description of the analyzer (conserving a natural defender position and a prospector strategy) gives such firms a certain focus, which makes it difficult to be responsive to customer demands. Griffithet al. (2012) argue that the dual nature of the analyser encourages the firm to abate the lively pursuit of new knowledge in an undefined market, and to preserve the more definite and directed knowledge transformation of the defender. It is, therefore, surprising that the hypothesized relationship was not established.

Saraç (2019) posit that analyzers encounter the ineffectiveness of defenders and inefficiencies of prospectors if they fail to administratively differentiate structures and processes that support both stable operations for the core business and innovation in rapidly changing. By this position, SMEs that are able to maintain a good balance may still be able to achieve customer responsiveness; hence, the finding may not be totally out of place.

Defender Strategy, E-commerce Adoption and Customer Responsiveness

The study hypothesized that the pursuit of the defender strategy influences e-commerce adoption (H2a). The result demonstrates that the defender strategy has a positive and statistically significant relationship with e-commerce adoption (p-value = 0.000, path coefficient = 0.257). This means that SMEs pursuing the defender strategy are potential adopters of e-commerce. Defenders habitually administer their products or services to an openly distinct market (Hawrysz, 2020). E-commerce provides an openly defined market for the promotion of products and services and helps to draw attention to stable sets of products for firms. Besides, with the aim of preserving and promoting the efficiency of operations and reducing operating costs, defenders may have a good justification for their propensity to adopt e-commerce (Haj Youssef & Christodoulou, 2017; Lumbantoruan & Pujangkoro, 2020).

The test of hypothesis H2b failed to establish any statistically significant relationship between the defender strategy and customer responsiveness (p-value = 0.084, path coefficient = 0.102). This means that the proposed relationship between defender strategy and customer responsiveness was supported. This finding contrasts with Haj Youssef and Christodoulou (2017), Hawrysz (2020), and Lumbantoruan and Pujangkoro (2020), who all agree that defenders put prominence on their customers. A possible explanation for the current finding could be the slow decision-making process by defenders and their resolve to undertake a transformation after broad research and analysis, which may take a longer time to complete. For defenders, efficiency is a key factor in their operations; hence, they may not be effective at providing exceptional customer service as embedded in customer responsiveness.

Prospector Strategy, E-commerce Adoption and Customer Responsiveness

The hypothesis H3a states that, the pursuit of the prospector strategy influences e-commerce adoption. The results revealed a positive and statistically significant relationship between the prospector strategy and e-commerce adoption (p-value = 0.000, path coefficient = 0.233). The quest to constantly seek for product and market opportunities drives prospectors to become innovative. Again, the flexibility character of prospectors and their ability to venture into completely new markets and take noteworthy risks describes the technology inclination of a prospector firms. E-commerce represents highly competitive and dynamic markets which launches new products regularly and offers flexible means of operations. The characteristics of the prospector strategy align well with the attributes of e-commerce; hence, the not-surprising result is that the prospectors are likely to adopt e-commerce. This result supports claims by Cassolet al. (2019); Chaimankong and Prasertsakul (2012); Yanes-Estévezet al. (2018) that prospectors are technology driven and innovators and influencers of e-commerce adoption.

With regards to customer responsiveness, H3b (The pursuit of the prospector strategy influences SMEs customer responsiveness) was supported at p-value = 0.002, path coefficient = 0.181. Thus, the pursuit of a prospector strategy has significant influence on a firm’s customer responsiveness. This finding was to be expected given that (Yanes-Estévezet al., 2018) describe prospector strategy as a strategic behavior that is concerned with human resources. Customers are the lifeblood of a firm and the greatest human resource aside from employees of a firm. Therefore, a firm will pay greater attention to the customer that brings in the money in exchange for its products and services. This finding also confirms the proposition Chaimankong and Prasertsakul (2012) that prospectors are proactive in addressing customers’ changing needs without considering the associated cost, thus making the prospector strategy a customer-focused strategy.

Reactor Strategy, E-commerce Adoption and Customer Responsiveness

H4a proposed that the pursuit of the reactor strategy inhibits SMEs’ adoption of e-commerce. The results did not establish any statistically significant relationship (p-value = 0.658, path coefficient = 0.032). Klingeret al. (2019) established that a reactor firm upholds a current strategy-structure relationship in spite of the vast changes in its operational environmental conditions. Reactors are known to be firms that pay little to no attention to their environment and the changes that occur in them. As technology such as e-commerce advances within the environment, reactors will rarely recognize the value until compelled by their operating market environment, particularly by the efforts of competitors. Aleksic and Jelavic (2017) state that firms pursuing the reactor strategy do not have a clear operational approach; hence, they do not have a laid-out plan with regard to how they will incorporate e-commerce into their operations. Reactors insensitivity to technological advancement as well as their inability to align well with changes in their operational environment makes it more difficult for them to adopt e-commerce. There is, therefore, a strong position in the literature on the inverse relationship between reactors and e-commerce adoption. This paper, however, did not establish the evidence to support the existing view in the literature.

H4b states that the reactor strategy inhibits customer responsiveness. H4b was not supported. Even though the path coefficient was negative as predicted, the relationship between the reactor strategy and customer responsiveness was not statistically significant (p-value = 0.91, path coefficient = −0.009). This means that the reactor strategy has been established not to have an impact on customer responsiveness. It takes a firm that constantly acquires knowledge about its environment to notice the changing needs of customers and put in efforts to meet these changes. However, the reactor strategy is noted for temperately acquiring knowledge about their markets. This mild method of acquiring knowledge is done without a determined exertion. Reactors, even after acquiring knowledge from their markets, hardly understand the knowledge acquired and oftentimes ignore such information. Griffithet al. (2012) for instance, emphasize that knowledge acquisition by reactors is modest and, at best, without rigorous efforts, and their ability to make use of prevailing knowledge is either disregarded or taken the wrong way, in addition to being unable to efficiently apply any knowledge attained to reduce cost. As a result, they are unable to apply any knowledge acquired to enhance their operational effectiveness. Firms demonstrating this strategic behavior are, therefore, least expected to be responsive to the changing demands of their customers. It is obvious from this finding that the reactor strategy does not influence customer responsiveness as reactors do not pay attention to changes that occur in their operating environment (Pudenz, 2019; Carmichael, 2017; Miles & Snow, 1978). However, the hypothesis that reactor strategy has a negative impact on customer responsiveness was not confirmed.

The Extent to which SMEs Differ in Terms of their Propensity to Adopt E-commerce based on Miles and Snow’s Taxonomy

The study further sought to determine the extent to which the sampled firms differ with regards to e-commerce adoption relative to Miles and Snow’s taxonomy. In reference to the loadings of the taxonomies to e-commerce adoption in Fig. 3, the surveyed firms differ with respect to e-commerce adoption. Prospector loaded 0.233 towards e-commerce adoption. Analyser loaded 0.159 towards e-commerce adoption. Defender loaded 0.257 towards e-commerce adoption, and finally, Reactor loaded 0.032 towards e-commerce adoption. By implication, Defenders are 26% likely to adopt e-commerce, whereas Prospectors are 23% likely. This finding is surprising as Prospectors are perceived in the literature as innovative and technology-inclined and, therefore, were anticipated to show a greater probability of e-commerce adoption. Analyzers and Reactors were 16% and 3% likely to adopt e-commerce, respectively. This finding contrasts with Feyissa and Sharma (2017), who postulate that Prospectors and Analysers adopt technological innovation more than Defenders. The evidence is further in contrast to Saraç (2019), who posits that prospectors are more innovative than defenders. These findings may, however, support the claim (Rahayu & Day, 2015) that many SMEs do not see the need to incorporate e-commerce in their operations because they do not have applications for it in their business.

Rahayu and Day (2015) established that owners’ innovativeness, IT experience, and IT knowledge, as well as the firms’ strategy, technological readiness, and perceived strategic benefits of e-commerce, influence their adoption. Our findings on SMEs’ probability of e-commerce adoption can be linked to the discovery by Rahayu and Day (2015), as in many developing countries, the IT knowledge and experience of owner-managers are low. Thus, this study confirms that SMEs attitude towards innovativeness influences their adoption of e-commerce (Lai, 2017; Lestari, 2019; Linet al., 2020; Otienoet al., 2016).

Conclusions and Implication of the Study

The study concludes that service SMEs are indifferent toward the strategic behavioral options described by Miles and Snow (Analyser, Defender, Prospector, and Reactor). We also draw the conclusion that service SMEs, based on the four behavior options described by Miles and Snow (1978), differ in terms of their propensity to adopt e-commerce. The study further concludes that some strategic behaviors (Analysers, Defenders, and Prospectors) do influence e-commerce adoption and/or customer responsiveness to some extent. The reactor strategy is observed to have an influence on e-commerce adoption and customer responsiveness.

The findings of this study contribute to research/theory, practice, and policy. Firstly, this study contributes to research in that it sets out the association between the strategic behavior of firms, e-commerce adoption, and customer responsiveness. This study has advanced knowledge on how the strategic behavior of service SMEs can affect their e-commerce adoption and customer responsiveness. The study adds to the operations strategy literature of SMEs, which, admittedly, is woefully inadequate.

The findings of the study provide insights to SME owner-managers to appreciate the extent to which their strategic orientation and behavior may influence their e-commerce adoption and customer responsiveness. For SMEs with a particular focus on innovations such as e-commerce as well as being responsive to changing customer needs, the current study establishes the basis for key considerations for taking that strategic decision relative to Miles and Snow’s typology. Entrepreneurs need to know how to respond strategically to their changing business environment and to develop business models that will help them to be competitive globally. The findings from this study provide the foundation for developing sound criteria for strategic behavioral choices, particularly among service SMEs.

The results of the study could be useful to policymakers. It is important for policy-makers to appreciate the operations strategy of SMEs in that the continued existence of these firms will rest on the quality of their strategic orientation being in alignment with the changing business environment (Miles & Snow, 1978; Yanes-Estévezet al., 2018). Therefore, the institution of industry-specific policies and action plans targeted at helping SMEs gain an understanding of strategic behaviors and their implications on their performance is paramount.

Limitations and Directions for Future Studies

Data for the study was gathered from three selected service sub-sectors; hence, this affects the generalisability of the study for all service sector firms. Future studies should broaden the scope to cover other service sectors such as real estate, education, social work activities, and other service activities, as operations strategy is relevant to these sectors. Additionally, the study adopted a cross-sectional design approach. Future studies may explore the use of longitudinal studies to determine if SMEs do migrate in their strategic behaviors over time.

The quantitative research method was employed in this study and would therefore not involve the collection of specialized knowledge specifying the respondents’ reasons for giving out precise answers about their strategic behavior, e-commerce adoption, and customer responsiveness. Future studies may consider employing a qualitative design, such as case studies, to discover why SMEs are largely indifferent toward strategic behaviors relative to Miles and Snow’s taxonomy. This study may be the first study to have explored the impact of the strategic behavior of firms on e-commerce adoption and customer responsiveness. Future studies using the same Miles and Snow typology may verify what impact the strategic orientation of a firm has on the performance of the firm using a mediator (age or size).