Essential factors in cloud-computing adoption by SMEs

Abstract

Small and medium-sized enterprises (SMEs) invariably leverage a country’s economy. In this context, information and communication technology (ICT) improvements render cloud computing (CC) both an effective and cost-efficient platform for SMEs. Statistically, multiple factors influence SMEs in their adopting CC. In the subtext of a technology, environment and organization (TEO) framework, the purposefulness of this empirical study has been to identify the most prominent factors in the CC adoption, by SMEs in Pakistan. Data were collected through convenient and quota sampling methods from 103 SMEs. Both multiple regression and confirmatory factor analysis (CFA) were deployed to test the measurement model. The results show that CC adoption by SMEs is positively affected by six cogent factors, namely relative advantage, CC compatibility, managerial support, firm size, competitive pressures and regulatory support. Dynamic complexity seems, however, to have a negative effect on CC adoption by SMEs in Pakistan. Also discussed are the study’s implications for SME managers and researchers.

Keywords

Adoption cloud-computing SMEs technology environment and organization (TEO) framework Pakistan

Zulfiqar Hussain Pathan is a Ph.D. candidate at School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China. Before commencing his Doctoral research, he worked at Mehran University of Engineering & Technology. He holds post-graduation in Technology Innovation Management. He has attended international conferences and published research articles. His Current areas of interest includes, Technology Management in SMEs, Entrepreneurship and Web-Based business applications.

Professor Zeng Jianqiu obtained his Ph.D. degree from Cambridge University, UK. He is a professor and a doctoral advisor in the School of Economics and Management of BUPT and also the Director of Information Economy and Competitiveness Research Center.

Umair Akram is a Ph.D. candidate at School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China. Before commencing his Doctoral research, he worked as a Lecturer of marketing management at Institute of Business and Management Sciences, UAF, Pakistan. He holds post-graduation in Business Administration specialized in Marketing. He is the author of several publications including China Economic Review, International Journal of Conflict Management, Chinese Management Studies, Human Systems Management and international conference proceedings. He is working on different projects of National Natural Science Foundation of China. He is editorial board member of Business and Management studies. His Current areas of interest includes, Impulse Buying Behavior, E-Marketing and Internet Finance.

Zahid Latif is a Ph.D. candidate at Beijing University of Posts and Telecommunications, China. He has been working in telecommunication sector of Pakistan since 2003. His research interest areas are Information and Communication Technology, one Belt, One Road, China-Pakistan Economic Corridor and ICT diffusion in Pakistan.

Muhammad Kaleem Khan is a Ph.D. candidate at Beijing University of posts and Telecommunications, Beijing China. Before commencing his Doctoral research, he worked as a Lecturer of finance at COMSATS Institute of Information and Technology (CIIT). He is the author of several publications including China Economic Review, Chinese Management Studies, Human Systems Management and International conference proceedings. He holds post-graduation in Business Administration specialized in Finance. His areas of interest include Finance, Crowd funding, Corporate Governance and E-Commerce.

M. Zahid Tunio is a Ph.D. candidate at Beijing University of Posts and Telecommunications, China. He has been working as Assistant Professor at Dawood Engineering University of Karachi, Pakistan. His research interest areas are Information and Communication Technology, Software Engineering, Computer Systems.

1 Introduction

In the field of Information and Communication Technology (ICT), cloud-computing (CC) has got much attention by researchers and practitioners. Various researchers defined several versatile characteristics of CC including power computing, resource pooling and virtualization [1]. National Institute of Standards Technology (NIST) defined CC by five essential characteristics, i.e., on-demand self-service, broad interconnected access, rapid elasticity, measured service and resource pooling. CC offers IT services as well as IT infrastructure to their clients on rent via “pay-per-you-go-basis”. Furthermore CC provides on-demand self-service technology platform to their users, which can be accessed globally around the clock. Users have to pay only against acquired services for a specific time [2]. Mainly CC delivers three types of service models including Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) along with four convenient deployed styles [3].

The great promise of CC stimulates both public and private enterprises to adopt cloud services in order to expand their business performance [4]. Recently, in an organizational survey conducted on CC adoption in Asia Pacific countries, revealed that 86% CC users adopt cloud service due to its compatible technologies; about 97% participants adopted due to its usefulness, while 95% prioritized CC adoption due to its quality and security services [5]. Moreover, affordable, flexible, scalable, agile and versatile features of CC attract the behavioral intentions of owners, senior managers of SMEs towards the adoption of cloud services [6]. Relatively, large-sized enterprises tend to adopt CC quickly, however SMEs are still far behind in the adoption process of CC particularly in developing countries [7, 8]. On the other hand, global market competition emphasized pressures upon SMEs to adopt more sophisticated ICT solution according to their business nature.

Globally, SMEs are considered as a fuel for economic engine. Report of Organization for Economic Cooperation and Development (OECD, 2005), stated that in Singapore, SMEs contribute 46% of share to GDP and 63% in employment opportunities. Additionally, SMEs are supporting economies all over the world [63]. In European countries about 60% to 70% jobs are provided by SMEs whereas this proportion exceeds by 78% in case of Japan [9]. Indian SMEs contributed 40 % in export share [10]. In the case of Pakistan, SMEs contribute about 40% share in the annual GDP, 25% in manufacturing exports and 78% share in individual employment (SMEDA, 2010) [64]. Due to dearth of financial and IT assets, Pakistani SMEs found slow adopter of emerging technologies [11]. But the ICT improvements, provide numerous opportunities in order to overcome these challenges. For example CC provide ubiquitous access to IT assets including business applications, vast storage, and power computing at a reasonable cost via easy payment mode [12, 13]. Hence CC benefits in the context of SMEs can no longer be ignored.

Recently in Pakistan, rapid penetration of 3G and 4G connections encouraged organizations to rethink their IT requirements and adopt more sophisticated ICT solutions in order to grow and support their business activities. Although CC adoption is at its nascent stage in the country and few enterprises have adopted cloud based services, however, majority of owners and managers of SMEs are eagerly willing to adopt cloud services for their smooth business operations. In this regard, a survey has been conducted for the current study in order to investigate essential factors of CC adoption. Generally, literature indicate that studies on CC adoption has been conducted by various researchers [3 , 14–16], however, no one focused in South Asian developing countries like Pakistan specifically in SMEs context.

In-depth insights of literature on CC adoption provide significant theoretical backgrounds and adopting variables. Based on these findings, current study formulates research model. Usually most of the previous studies supported their work by using theories like Technology Organization and Environmental (TOE), Diffusion of Innovation theory (DIT) and Technology Acceptance model (TAM) [17]. Table 1, illustrate description of previous peer reviewed studies conducted on CC adoption along with supporting theories, constructs and geographical location. Next section of the paper provides theoretical backgrounds regarding the adoption of CC and then justifiable explanations of proposed hypothetical statements. Subsequently, methodology and findings would be presented in the forthcoming section of the paper.

Table 1
Recent studies conducted on CC adoption

Author Year Adopted theory Sample taken Dependent variable Findings / independent variables

[17] 2017 DIT Pakistan CC adoption Relative advantage, compatibility, complexity and cost reduction

[14] 2016 TOE Ghana CC adoption Technology (relative advantage, security concern) Organization (top management support, technology readiness), environment (trading partners’ pressures and competitive pressures)

[21] 2016 TAM Oman CC adoption Self-efficacy, perceived usefulness, trust, perceived ease of use, and job opportunity

[15] 2014 TOE+DOI Portugal CC adoption Technology (relative advantage, complexity, technology readiness) Organization (top management support, firm size), environment (competitive pressures and regulatory support)

[26] 2014 TOE Taiwan CC adoption Technology (relative advantage, triability, complexity) Organization (top management support), environment (regulatory an security issues)

[25] 2014 TOE Taiwan CC adoption Relative advantage, technology readiness regulatory issues and security concerns.

[16] 2013 TOE North east England CC adoption Relative advantage, geo-restriction, uncertainty, compatibility, size, trialability, prior experience, top management support, innovativeness, market scope, industry, supplier efforts

[13] 2013 TAM Singapore CC adoption Ease of use and convenience, security, privacy, cost reduction, reliability

[58] 2013 TOE CC adoption Technology, organization and environment factors.

Author	Year	Adopted theory	Sample taken	Dependent variable	Findings / independent variables
[17]	2017	DIT	Pakistan	CC adoption	Relative advantage, compatibility, complexity and cost reduction
[14]	2016	TOE	Ghana	CC adoption	Technology (relative advantage, security concern) Organization (top management support, technology readiness), environment (trading partners’ pressures and competitive pressures)
[21]	2016	TAM	Oman	CC adoption	Self-efficacy, perceived usefulness, trust, perceived ease of use, and job opportunity
[15]	2014	TOE+DOI	Portugal	CC adoption	Technology (relative advantage, complexity, technology readiness) Organization (top management support, firm size), environment (competitive pressures and regulatory support)
[26]	2014	TOE	Taiwan	CC adoption	Technology (relative advantage, triability, complexity) Organization (top management support), environment (regulatory an security issues)
[25]	2014	TOE	Taiwan	CC adoption	Relative advantage, technology readiness regulatory issues and security concerns.
[16]	2013	TOE	North east England	CC adoption	Relative advantage, geo-restriction, uncertainty, compatibility, size, trialability, prior experience, top management support, innovativeness, market scope, industry, supplier efforts
[13]	2013	TAM	Singapore	CC adoption	Ease of use and convenience, security, privacy, cost reduction, reliability
[58]	2013	TOE		CC adoption	Technology, organization and environment factors.

2 Literature review

2.1 Cloud computing adoption and SMEs

Pertinent literature on CC revealed various studies already conducted regarding its adoption. Some of them uncover the increasing diffusion rate of cloud-computing services among SMEs. ICT improvements encourage organizations to adopt technology at ideal time, (in order to compete well in this digital era). as could compete this digital era. According to Zeleny’s framework [60], there is ‘high technology’, ‘technology’ and ‘appropriate technology’ or ‘sequestered technique’ [61, 62, Ch. 7], each kind demanding immensely different sorts of leadership and management. Technology is a combination of statistically complicated and dynamically organized set of components, that function together to make a system. It is a most efficient way to transform the input into commodities. High technology also have same features but with an instant response and in a more reliable way. Appropriate technology is based on internet and social interactions in business, and governmental sectors to run their activities efficiently. Consequently CC is also depicted in appropriate technological domain.

Table 1, presents findings of recent studies conducted on CC adoption in SMEs. Most of the earlier studies were supported by TEO framework in both developed and developing countries. Researchers believed that TEO frame work is more appropriate and ideal in the adoption of IT/ IS studies, as it helps to examine both internal and external factors of organizations. Initially TEO framework was proposed by Tornatzky in 1990, and described enterprise’s characteristics, size, readiness, assets as internal variables, which relate the technology integration. Additionally, regulatory issues, customers/supplier pressures and available IT infrastructure pertained as external factors those emphasized IT adoption [18]. Although TOE covered inclusive factors of adoption, hence this study put efforts to identify CC adopting factors by SMEs of Pakistan.

CC is a platform using remote grid servers hosted on internet in order to store, manage and process data rather than on personal computers or local servers. Probably CC is not new IT trend, but associated ideas with CC is generic. It brought new applications or services to business type firms, like Enterprise Resource Planning (ERP), Customer Resources Management (CRM) and Supply Chain Management (SCM). In addition to that CC allows to shift data and basic applications of firms on to cloud. This might generate vast savings and more efficiency in SMEs. Moreover, by the adoption of CC, SMEs become enable with ubiquitous, convenient, on-demand inter-connected access to a shared pool of configurable computing resources [19]. Emergence of CC has broken resistance of SMEs in the adoption of technologies. Noteworthy, CC also offers cost efficient ICT services to their clients via pay-per-go-basis [20]. Indeed CC significantly reduces the cost burdens particularly for new entrepreneurs from computer based analytical applications that previously only afforded by large-scale firms. In cloud services, entrepreneurs could get instantaneous access to IT assets including hardware, software, and data without any upfront capital investment., which leads a faster entry into market [13]. In this competitive era, these advantages attract SMEs to reorient their IT and think prudently for adoption of technologies.

2.2 CC adoption

CC adoption has been segregated in to two domains i.e., developed and developing nations. Few studies significantly contributed in the field of CC adoption in developed countries [3 , 16]. For example, Oliveira conducted research among the SMEs of Portugal by using TEO and DOI framework. The results indicate that relative advantage, complexity, technology readiness managerial support, firm size, competitive pressures and regulatory support are pivotal factors of CC adoption [15]. Furthermore, Alshamaila examined CC adoption in North-east England and found relative advantage, geo-restriction, uncertainty, compatibility, size, trialability, prior experience, managerial support, innovativeness, market scope, industry, supplier efforts as determinants of CC adoption [16]. On the other-hand in developing countries rare studies on CC adoption has been conducted [14, 21]. CC adoption has recently investigated by Senyo in Ghana followed by TEO framework and revealed relative advantage, security concern, managerial support, technology readiness, trading partners’ pressures and competitive pressures are significant factors towards CC adoption in SMEs context [14]. According to Sharma SK [21], self-efficacy, perceived usefulness, trust, perceived ease of use, and job opportunity as fundamental variables of CC adoption in Oman.

Existing CC literature used prominent adoption theories like, Diffusion of Innovation Theory (DIT), Technology Acceptance framework (TAM) and TEO framework. Two types of groups were observed in adoption of technology. First is individual it-self and second is the organizations. TAM and DOI frameworks are usually followed in those studies which identify only innovation determinants of technologies and it is only suitable for individual and micro level adoption studies. Both theories mostly support only one dimension of TEO framework i.e., technology. However not covering the organization and environmental aspects of adoption. Since the purposefulness of this study is to identify CC adopting factors by SMEs of Pakistan, hence TEO framework is more appropriate framework. Because TEO help to find firm’s decision regarding the adoption of technology [18]. It considers three aspects of the firms that influence the adoption. Technology context refers to perceived benefits such as relative advantage, CC-compatibility and reduce the complex procedures of an organization. Organization context refers to IT readiness, size, managerial support and innovative-characteristics of firms. Environment domain consists of regulatory support, customer/supplier pressures [18, 22]. These elements are effectively used to know that how an organization perceive the need for, look for and adopt new technologies.

Yet no study has been found on CC adopting factors by SMEs in the regions of South Asian developing countries like Pakistan. Therefore, it stimulates the motivation of author to dig out the pivotal CC adopting factors by SMEs of Pakistan. Discussed above characteristic of TEO almost covers all core domains of organizations, which affect the adoption. Hence this study dig factors in the light of TEO framework. Following variables have been extracted from peer-reviewed literature; and in the light of their explanation, next section depicts research model and hypothetical statements.

2.3 Research framework and hypothesis formulation

Designed research framework is shown in Fig. 1. By following TEO framework, this study attempts statically to examine the relations between CC adopting factors in SMEs. In this sub-context, following hypothetical assumptions have been drawn.

Fig.1

Research framework.

2.3.1 Technological context

Technology attribute of TEO is based on internet and social interactions in business, and governmental organizations to run their activities efficiently [60]. Furthermore, it consisted upon both interior and exterior ICT applications those are associated with enterprise along with those which are available in market but not currently in use [23]. Most crucially, high technology enables societal self-development and self-management [61, 62, Ch. 7]. Linked to CC, such societal improvements have diverse and versatile consequences, depending on the autonomy, intentionality and purposefulness of CC adopters and users. Convenient and multipurpose use of any emerging technology stimulates enterprises to quickly adopt CC [24]. Pertinent literature of CC revealed that, relative advantage, complexity and compatibility are considered as the components of technological domain and has been examined as substantial factors by various scholars [16 , 24–26]. Relative advantage is defined as a degree to which an innovation is perceived greater benefits for the enterprise [24]. Adoption of cloud – based service in SMEs, relatively add values in the strategic and operational dimensions of firms like annual sales increase and minimize the operational cost [27]. Adoption of technologies relatively impact organizations in terms of their scalable and productive business operation [28]. Moreover, CC adoption positively impact the internal business communication and coordination among the stakeholders of firms [29]. Advantages like less power consumptions and scalable infrastructure have been found as relative advantages to motivate SMEs and enforce to adopt cloud services [13]. These elaboration formulate the first hypothesis of this study as under.

H1 (RA⟶CCA): Relative advantage relates positively with the adoption of cloud computing in the context of SMEs.

CC-compatibility is defined as the degree to which innovation predicted as being reliable and consistent with the current values, requirements and past experience of potential adopters [24]. Furthermore, CC-compatibility refers to what extent cloud-based services fit to existing enterprise’s business operations, IT infrastructure and corporate culture [30]. Adoption of CC also increases with the availability of compatible ICT resources at both ends (receivers and sender) [29]. Hence in-compatible characteristics of cloud service resist SMEs towards its adoption. Although cloud service providers are continuously getting efforts to enhance their compatibility degree, as they can catch more market share [26]. Therefore, the existing system of an organization and compatible characteristic of clouds are necessary elements in the adoption process of SMEs. These elaborations derived another hypothesis of study as under.

H2 (CC-COMPT⟶CCA): CC-compatibility relates positively with the adoption of cloud computing in the context of SMEs.

Generally, complexity pertained as to convenient use of the adopted technology. There are two types of complexities, one is static and other is dynamic. In this study we assumed as a dynamic complexity. Usually, complex and complicated nature of technology act as inhabiting force in the adoption process and it negatively influence the diffusion of emerging technology [16, 31]. For instance, CC adoption needs training and continual efforts to minimize the complexity of cloud, then SMEs seem less likely to adopt. Oliveira examined complexity as inhibitor in the adoption of CC particularly in service sector of firms [15]. The disruptive aspect of CC is associated with the complexity of cloud, thereby critically influencing the adoption in SMEs. Moreover, complexity is related with the perception of change that may cause frustration and discomfort [32]. Therefore, convenient and easy mechanism of CC is necessary that would motivate SMEs in the integration of cloud based application. In addition to that, lack of IT professionals in SMEs also caused an increment in complexity of CC, for example, the adoption of SaaS (ERP), IaaS (Oracle’s Elastic Cloud) and PaaS (SDK) require well IT oriented professionals. These descriptions of the studies led another hypothesis as under:

H3 (COMPX⟶CCA): Dynamic complexity relates negatively with the adoption of cloud computing in the context of SMEs.

2.3.2 Environmental context

Second attribute of TEO framework is environmental, that also influence the adoption of technology in the organizations. Facilitators like role of regulatory bodies and their standards, competitive pressures are the components of environmental context, these variables might emphasis organizations towards adoption of emerging innovations. Prior studies have also found competitor, customer and supplier pressures as well as regulatory support as significant factors in the adoption of CC by SMEs [15 , 29]. Moreover, regulatory support by governmental authority also encourage organizations to assimilate the cloud technologies [36]. Complex rule and regulations might be critical in the adoption of emerging technologies. Therefore flexible policy regulations regarding the addiction of technologies in the country act as motivator in adoption. In the light of these explanations, one more hypotheses has been derived as:

H4 (RS⟶CCA): Regulatory support relates positively with the adoption of cloud computing in the context of SMEs.

Another component of environmental context is competitive pressures, provoked by stakeholders of organizations including their customers, suppliers and business partners in the diffusion of CC. Usually enterprises after getting pressure from their associated participants (customers, suppliers) and then-after sensitively decide for adoption [22, 29]. Noteworthy, by the adoption of CC, SMEs might improve their business operations, enhance market efficiencies, increase accuracy, protection of data and can attract more customers [37]. Hence it formulates the following hypothesis as:

H5 (CP⟶CCA): Competitive pressures relates positively with the adoption of cloud computing in the context of SMEs.

2.3.3 Organizational context

Third attribute of TEO framework is organization. It is consisted of existing resources and characteristics of the firm and individuals that support adoption of technology [33]. In business enterprises and institutions the organizational structure, size and a dynamic enactment of manager intentionality affect technology adoption [62, Ch. 7]. Various organizational factors facilitate the adoption of technologies, such as firm size, scope of the enterprise and decision makers’ intentions, interests and support [18, 29]. This study attempts to correlate the firm size and managerial support with the adoption of CC, as these two constructs are frequently used in the previous studies.

In the organizational context, managerial support considered as prominent factor in CC adoption studies. Commitment and vision of managerial executives regarding the resource allocation, service integration, and the re-engineering processes are very important in the adoption of CC [29]. Extant literature of CC, examined that managerial support has a direct effect in adoption of CC [14 –16]. When executive management of firm recognizes the advantages of cloud service, it seems more likely to allocate the resources for its adoption and also recommend their organizational staff to integrate. However, when top management fail to recognize advantages they oppose adoption [15]. Hence managerial support is essential in the CC adoption. Therefore, current study hypothesizes that:

H6 (MS⟶CCA): Managerial support relates positively with the adoption of cloud computing in the context of SMEs.

Firm size can be defined by the strength of manpower, market size and total capital investment [34]. Firm size also stimulates SMEs towards the adoption of CC. Comparatively, large-scale firm has an advantage over SMEs, in cloud based services, because they have more resources and could tolerate associated risks [35, 36]. Relevant studies indicate that small scale firms usually are not much associated with the adoption of emerging technologies [33]. Hence firm size is also considered as determinant of CC adoption. These explanations led to another hypotheses as under.

H7 (FS⟶CCA): Firm size relates positively with the adoption of cloud computing in the context of SMEs.

3 Research methodology

Purposefulness of this study is to investigate the essential adopting factors of CC in SMEs of Pakistan under the context of TEO framework. White-collar officials including CEO, Managers and IT experts from SMEs exhibiting profound knowledge about adoption of technologies were called for participation in data collection process. Data were collected through online survey and paper questionnaire from February 1st to 28th 2017 by using convenience and quota sampling method. Total one hundred and thirty two questionnaires were distributed among CEO’s, managers and IT experts of SMEs in Pakistan.

Research questionnaire of the current study was consisted of two parts. The first section of the questionnaire was intended to check the demographic information of the firm including size, nature and the role of respondent in the firm. The second part was designed to examine the essential factors of CC adoption i.e., relative advantage, CC-compatibility, complexity, managerial support, firm size, regulatory support and competitive pressures) in SME’s of Pakistan.

Out of 132 questionnaires, 121 were received back and 103 found useable for data analysis with 78.03 per cent response rate. We discard 18 questionnaires due to unclear and incomplete responses. In order to motivate the respondents, small gifts (note books along with pen) were provided which costs around 90 Rupees ($0.86). It is noteworthy that as SMEs in Pakistan are in developing phase, CC adoption was found at nascent stage, thereby number of organizations that adopted CC remained small in our sample. Table 2, illustrate the demographical facts about the enterprises responded, their business nature, key position of respondent and Table 3 illustrate the number of firms using internet and CC services.

Table 2
Demographic facts of respondents

Company size Service Sector Role in the company

(Staff) 1–20 32.82% Financial services 28.21% Owner 32.54%

21–50 26.46% IT / ICT services 44.19% IT manager 37.26%

higher than 51 40.72% Hotel 10.35 % Employee 30.2%

Total 100% Travelling services 14.65% Total 100%

Others 2.6%

Company size	Service Sector	Role in the company
(Staff) 1–20	32.82%	Financial services	28.21%	Owner	32.54%
21–50	26.46%	IT / ICT services	44.19%	IT manager	37.26%
higher than 51	40.72%	Hotel	10.35 %	Employee	30.2%
Total	100%	Travelling services	14.65%	Total	100%
		Others	2.6%

Table 3

No. of SMEs using intent and CC

Internet Connectivity		CC Service Model
Yes	94.7%	SaaS	41%
No	5.3%	IaaS	31%
		PaaS	28%

3.1 Measurements

All measurement items were adopted from previous peer reviewed studies. Four item scales were used to measure relative advantages and adopted from [15 , 38–40]. Similarly, CC-compatibility is measured by four item scale and sourced from [35 , 42]. Dynamic complexity also measured at four scale items and adopted from [35 , 42]. Three item scale is used for managerial support and adopted from [43 –45]; firm size adopted from [41 , 46]. Moreover, regulatory support and competitive pressures measured by three item scale and adopted from [44, 47] and [38, 48] respectively. Lastly CC adoption was measured by 3 items [3, 35]. Each construct items were measured by five-point Likert scale (1 = strongly disagree, 5 = strongly agree). All scale items have been given in Appendix-A.

4 Results

The current study utilized important data analysis techniques in order to test the hypotheses and to ensure the quality of research. We analyzed data into different phases: first; demographic analysis, second; to assess the reliability and validity of measurement scales, third; CFA analysis. Above mentioned steps were used to verify essential factors of CC adoption i.e., Relative advantage, CC- compatibility, complexity, regulatory support, competitive pressures, managerial support and firm size. Finally, multiple regression analysis was employed to test the hypotheses. Model fitness was assessed by different statistical techniques: Chi-square (χ2), comparative fit index (CFI) goodness of fit index (GFI), adjusted goodness of fit (AGFI), and root mean square error of approximation (RMSEA).

4.1 Assessment of measurement model

Confirmatory factor analysis CFA was employed in order to validate the causal relations among all variables in the research model. All values of CFA analysis demonstrated a satisfactory fit and meet the threshold standard (χ2 = 241.93, CFI = 0.95, GFI = 0.86, AGFI = 0.84, RMSEA = 0.07). Table 4 reports all reflective variables have satisfactory levels of Cronbach’s alpha and composite reliability CR values i.e., internal consistency reliability. All values of CR and Cronbach’s alpha are higher than standard value 0.8 [49]. Convergent validity was measured by three principles (a) construct item loading higher than 0.7 (b) average variance extracted (AVE) higher than threshold value 0.5 and last and (c) criteria CR higher than 0.80 [50]. Discriminant validity was assessed by comparing the values between square root of AVE should be larger than its correlations coefficients with all other constructs [50] in line with [51].

Table 4
Validity and reliability of scales

Constructs CR α AVEs 1 2 3 4 5 6 7 8

RA 0.78 0.76 0.717 0.846

COMPT 0.69 0.81 0.671 0.319 0.819

COMPX 0.89 0.91 0.856 0.601 0.168 0.925

RS 0.88 0.92 0.876 0.487 0.411 0.123 0.935

CP 0.81 0.89 0.701 0.345 0.131 0.321 0.511 0.837

MS 0.88 0.78 0.781 0.289 0.222 0.432 0.543 0.432 0.883

FS 0.67 0.86 0.849 0.543 0.587 0.549 0.357 0.121 0.232 0.921

CCA 0.87 0.78 0.719 0.511 0.277 0.432 0.548 0.433 0.543 0.232 0.847

Constructs	CR	α	AVEs	1	2	3	4	5	6	7	8
RA	0.78	0.76	0.717	0.846
COMPT	0.69	0.81	0.671	0.319	0.819
COMPX	0.89	0.91	0.856	0.601	0.168	0.925
RS	0.88	0.92	0.876	0.487	0.411	0.123	0.935
CP	0.81	0.89	0.701	0.345	0.131	0.321	0.511	0.837
MS	0.88	0.78	0.781	0.289	0.222	0.432	0.543	0.432	0.883
FS	0.67	0.86	0.849	0.543	0.587	0.549	0.357	0.121	0.232	0.921
CCA	0.87	0.78	0.719	0.511	0.277	0.432	0.548	0.433	0.543	0.232	0.847

Note: Bold diagonal values are square root of average variance extraction non-diagonal values are Interco relations of all constructs. Acronyms: Relative Advantage (RA); CC-Compatibility (COMPT); Complexity (COMPX); Regulatory Support (RS); Competitive Pressures (CP); Managerial Support (MS); Firm Size (FS) and Cloud Computing Adoption (CCA).

Table 4, demonstrates the results of convergent and discriminant validity, reliability, average variance extracted and root mean square value (RMSV). All construct values meet the threshold values of convergent validity and reliability. These results showed that the construct model in current study has good convergent validity and reliability. In addition, Table 4 provides the outcomes of discriminant validity, the square root of AVE is above its construct correlations coefficients. This results indicated that discriminant validity has acceptable level [50, 51].

4.2 Results of structure model

Table 5 presents the results of direct effect of hypotheses with β values and t-statistics. H1 (relative advantage⟶+CC adoption) that proposes the positive relation between relative advantages and CC adoption was supported with path coefficient of 0.81, standard error of 0.03 and t-statistics of 27.0. This infers that relative advantages strongly and positively influence SMEs towards adoption. H2 indicating that CC-compatibility (compatibility⟶+CC adoption) with β path coefficient of 0.78, SE of 0.03 and t-statistics of 26.0 elaborate that compatibility of cloud influences CC adoption. H3 indicating that complexity has negative relation with CC adoption is supported by path coefficient – 0.16, standard error of 0.09 and the value of t-statistics of 1.77. H3 shows that CC negatively influenced by complexity. As shown in Table 5, H4 demonstrating that regulatory support has mapped as (regulatory support⟶+CC adoption) with β path coefficient of 0.34, SE of 0.03 and t-statistics of 11.33. This implies that regulatory support positively influences CC adoption. Similarly H5 tested competitive pressures and found positive relation with CC having β path coefficient of 0.65, SE of 0.13 and t-statistics of 5.0. H6 (managerial support ⟶+CC adoption) having β path coefficient of 0.21, SE of 0.05 and t-statistics of 4.2. H7 indicating that firm size given a path as (managerial support ⟶+CC adoption) with β path coefficient of 0.40, SE of 0.12 and t-statistics of 3.33. This show a positive relation evaluated between firm size and adoption of CC.

Table 5
Results of hypotheses testing and structure relations

Hypotheses Path Path Coefficient Standard Error t-statistics Decision

H1 RA⟶CCA 0.81 0.03 27 Supported

H2 COMPT⟶CCA 0.78 0.03 26.00 Supported

H3 COMPX⟶CCA –0.16 0.09 1.77* Supported

H4 RS⟶CCA 0.34 0.03 11.33* Supported

H5 CP⟶CCA 0.65 0.13 5 Supported

H6 MS⟶CCA 0.21 0.05 4.2 Supported

H7 FS⟶CCA 0.40 0.12 3.33* Supported

R² 54.3

Adjusted R² 34.3

Hypotheses	Path	Path Coefficient	Standard Error	t-statistics	Decision
H1	RA⟶CCA	0.81	0.03	27**	Supported
H2	COMPT⟶CCA	0.78	0.03	26.00**	Supported
H3	COMPX⟶CCA	–0.16	0.09	1.77***	Supported
H4	RS⟶CCA	0.34	0.03	11.33***	Supported
H5	CP⟶CCA	0.65	0.13	5**	Supported
H6	MS⟶CCA	0.21	0.05	4.2**	Supported
H7	FS⟶CCA	0.40	0.12	3.33*	Supported
R²		54.3
Adjusted R²		34.3

Note: t-values for two-tailed test: *1.65(significance level 10%); **1.96(significance level 5%); and ***t-value 2.58 (significance level 1%) [50]. Acronyms: Relative Advantage (RA); CC-Compatibility (COMPT); Complexity (COMPX); Regulatory Support (RS); Competitive Pressures (CP); Managerial Support (MS); Firm Size (FS) and Cloud Computing Adoption (CCA).

Additionally, Table 5 showed the value of R² for this model relations between independent and dependent variables. The R² value for this model relation indicate that 54.3% change in cloud computing adoption occurs due to cogent factors i.e., RA, CC-COMPT, COMPX, RS,CP, MS and FS. Therefore, all hypotheses were supported by results.

5 Discussion

H1, H2, H3, H4, H5, H6 and H7 all were substantiated. Considering the importance and advantages of CC, SMEs recognized the adoption of CC technologies. As it offers various advantages like transformation of business process, content management, collaborations, mobility and rapid application growth. Purposefulness of this study is to identify the essential factors of CC adoption with TEO perspectives in the service sector of Pakistan’s SMEs. Results show that CC adoption in SMEs is influenced by seven factors: relative advantage, CC- compatibility, dynamic complexity, regulatory support, competitive pressures, managerial support and firm size (see Table 5). All constructs are analyzed in the light of TEO framework. The first aspect of TEO framework is technology:

5.1 Technological context

Present study has formulated H1, H2, and H3 with the domain of technological context and conceptualized the relation between CC adoption with relative advantage, CC-compatibility and dynamic complexity respectively. Based on analyzed results, relative advantage, CC-compatibility and complexity has significant influence SMEs towards the adoption of CC. Since relative advantage is mapped as (H1: RA⟶ CCA) and found that there is strong and positive relation. Virtualization and fast computing power offer various relative advantages to organizations. This hypothetical result is consistent to the prior studies conducted by [14–16 , 52]. Additionally, this study confirms that Pakistani SMEs, those adopted CC solutions has recognized the importance of relative advantages of cloud. Integrations of CC in the service sector of SMEs has brought improvement in business operations, executes specific tasks faster, enhances the productivity and avails more new business opportunities comparatively those organizations which are non-adopter of CC. Such kinds of advantages recently attract more SMEs in the adoption process of cloud technologies. Moreover, the timely adoption of CC provides SMEs better business communications with their stakeholders including customers, partners, and suppliers. As well as global access of market that is considered as relative advantages obtained by CC adoption.

Second hypothesis derived about the CC-compatibility and mapped as (H2: CC-COMPT⟶ CCA). Generally, SMEs assume, cloud services should be easy to use and compatible with their existing IT infrastructure. Results indicate that CC-compatibility of cloud influence positively the adoption of CC. Rationality of compatibility variable is also supported by various scholars and found from the pertinent literature studies of [15 , 45]. In this study, the selected sample of SMEs were well IT oriented and usually operating their business over internet hence those SMEs exhibiting compatibility as a facilitator. Business nature and scope of selected sample quite matched with the cloud services, therefore results of this study were found positive and has significant relation between CC-compatibility and CC adoption. Although few studies examined compatibility as inhibitor and non-significant in CC adoption like [29, 53]. But present study negate their findings and conclude that CC-compatibility is mixed nature variable hence need more research.

Third hypothesis of technology domain is regarding the complexity of cloud and mapped as (H3: COMPX⟶ -CCA). Based on the results, dynamic complexity is found as negative relation with CC adoption and statistically is supported in this study. The complex nature of cloud usually acts as inhibitor in the context of SMEs. This finding has been consistent to the prior studies in literature with [14–16 , 53]. Generally complexity associated with the perceptional changes in the organization that causes discomfort and frustration [32]. Current study identified that complexity has negative influence the adoption of CC and acts as inhibitor among the service sector SMEs. Due to the dearth of qualified IT experts in the SMEs, these organizations do not feel much comfortable with the complicated dimension of CC. Moreover, adoption of CC affected by the psychological factors of CEO/Manager like frustration, stress and mental efforts, affect the business operations and growth adversely. Therefore, complexity examined as inhibitor and analysis support the given hypothesis as negative relation between complexity and CC adoption.

5.2 Environmental context

Second attribute of TEO framework is environmental domain, which has been measured by two hypotheses in this study. Since CC is emerging new ICT trend in information system discipline, hence limited studies addressed the environmental context. Based on pertinent literature environmental factors were measured by regulatory support and competitive pressures. The fourth hypothesis of this study has given a path as (H4: RS⟶ CCA). According to results, regulatory support has also been found to influence positively Pakistani SMEs, in the process of CC adoption. Recently cyber-crime bill has been approved in the country, which also encourages organizations in order to adopt more appropriate technologies. As this legislation protect users against any data security and mobility. Moreover, this finding is also seen consistent to [15 , 25]. Hence the regulatory support act as facilitator to SMEs, in the adoption process of technology.

Consistently the fifth hypothesis of this study has been mapped as (H5: CP⟶ CCA). Results indicate that competitive pressures also influence positively SMEs towards the adoption of CC. Competitive pressures usually aroused from the customer, suppliers, and partners of the organizations. By the increase number of customers, SMEs are intentionally willing to add more appropriate technologies in order to develop and empower business. However, CC platform provide various amazing cloud services, by which firm can manage and coordinate with their customers, suppliers and partners conveniently [3]. This result is also consistent to other prior studies conducted by [14 , 20]. These pressures enforce SMEs to adopt CC.

5.3 Organizational context

Third attribute of TEO framework is organizational context which has been conceptualized by two hypothesis H6 and H7 in this study. H6 presents managerial support and H7 pertains to the firm size. Both constructs result indicate that these are positively related to CC adoption in SMEs. Managerial support is very important factor in the adoption of any technologies. It has been given a path as (H6: MS⟶ CCA). Generally, MS depends upon the vision, commitment and interest of top level officials of organizations. Empirically evidence of current study noticed that MS significantly influence the adoption of cloud service. Pakistani SMEs owners or related managerial staff are much interested, willing and committed to adopt more sophisticated and economic ICT solution. Since cloud offers very economical solution like SaaS, PaaS and IaaS, moreover allows user to pay various easy method to pay. By the adoption of cloud, SMEs do not need to invest in ICT, while could get service by “pay-per-go” basis [3]. This finding has been supported by various scholars [15 , 55]. Last hypothesis of this study formulated as (H7: FS⟶ CCA). Adoption of CC is also influenced by the firm size, usually firm size is measured by the work force and annual volume. Based on the tested results, this study also found that CC adoption in Pakistani SMEs is influenced by the firm size. Increasing number of customer and over-burdened transactions pressurize SMEs to add more appropriate technologies. Due to versatile characteristics of cloud service, SMEs were found more intended to adopt CC. This results has been consisted in the related studies like [14 , 57]. Moreover, for new start-up firms, CC is more suitable platform, as it helps to avoid heavy upfront capital investment.

Summing up all the above discussion above, this study investigated that CC adoption (dependent variable) in service sector SMEs of Pakistan is influenced by seven attributes as independent variable (relative advantage, CC-compatibility, complexity, regulatory support, competitive pressures, managerial support and firm size) under the dimension of TEO framework. Results suggest that CC offer relative advantage to SMEs when there is compatible IT infrastructure in the existing business operations and complexity looks as inhibitor towards its adoption. However, firms are more interested in the CC services due to its cost effective and economic model. Therefore, relative advantage, compatibility, regulatory support, competitive pressures, firm size and managerial support also collectively influence the SMEs in CC adoption. CC is very dynamic and offering business orienting and cost effective cloud service like SaaS, ERP, SCM, PaaS, IaaS.

6 Managerial implication

For managers and practitioners, imperative implications have been drawn from results of current study. First, global business competition pressurizes SMEs to adopt appropriate technologies, otherwise, SMEs usually suffer and may result in a shutdown of their business. Consequently, it is suggested to the senior level management of SMEs, to recognize the basic technological needs of the firms, comprehend the scope and requirement of the firms carefully. For the fulfilment of those necessities, cloud computing is very much suitable and appropriate technology. It offers cost effective IT-services, infrastructure and platform like (SaaS, IaaS, PaaS) along with convenient deployed model (public, private, hybrid and community) to the organization with minimum start-up cost, access to a shared pool of configurable computing assets, via easy mode of payment like pay-per-you-go.

Secondly, this study also contributed to extent literature by providing up-to-date empirical evidence for practitioners about CC adoption in SMEs with three main domains of the firms i.e., technology, environmental and organization. Additionally, results suggest that CC is relative advantageous to SMEs when there is compatible IT infrastructure in the existing business operations, along with the support of management and regulations. Finally results provide valuable insights for managers of SMEs to make decision of CC adoption.

7 Limitation and future research direction

Although, this study has made a slight contribution for academicians and practitioners, however, it has few limitations. This study highlighted few confinements, hence suggested for future research work. First this study only focused on Pakistani SMEs specifically service sector not on manufacturing and trade sector. Therefore, similar type of study may conduct on manufacturing and trade sector SMEs. Moreover, this study only sheds light on adoption of CC, so that future studies may be conducted upon the impact and challenge of CC in SMEs context.

8 Conclusion

Present study examined the essential factors of CC adoption among Pakistani SMEs particularly of service sector in the light of TEO framework. In this regard, seven constructs were formulated and their reliability and convergent validity were tested, while relations between variables was evaluated by regression method. Results of multiple regression revealed that six hypothesis positively influence the CC adoption i.e., RA, CC-COMPT, RS, CP MS and FS, however COMPX is found negatively. All these constructs were formulated within the domain of TEO framework. This framework is very much appropriate to measurer holistically the adoption factors of information system (IS) in the organizations, as it covers almost each domain of organization including internal and external aspects of firms. Moreover, findings indicate that MS, CP, COMPT and RA are strongly related in CC adoption respectively. However the remaining attributes also influence the CC adoption. This study would also fill the gap in CC adoption literature. Additionally, this study suggest a path to SMEs that how to assess and implement sophisticated technologies like CC. Based on this study, it can be concluded that CC adoption would grow exponentially and provide several benefits to SMEs in the future. Therefore, senior level management of SMEs need to pay more attention in the adoption of CC, as it offers cost effective, compatible, agile and scalable solutions as well as application for their business growth. Findings suggest valuable insights to owners / managers for their decision regarding the CC adoption.

Footnotes

Appendix A: Questionnaire

Relative advantage adapted from [15 , 42]

RA1 – CC provide you efficient business operations in order to manage.

RA2 – CC effect the quality of operations in your business.

RA3 – Using CC, your enterprise executes specific tasks more rapidly.

RA4 – CC offers new business opportunities.

CC-Compatibility adapted from [35 , 42]

COMPT1 – Adopted CC service fits with the existing work style of firm.

COMPT2 – Integrated CC service is compatible with your current business operations

COMPT3 – Using CC is compatible with your company’s corporate culture and value system.

COMPT4 – Use of CC will be compatible with existing hardware and software in the company.

Complexity adapted from [35 , 42]

COMPX1 – CC take a lot of mental effort.

COMPX2 – Usage of CC create frustration.

COMPX3 – CC service are complex in nature for business operations.

COMPX4 – CC is complex and ambiguous for employees of the firm.

Regulatory support adapted from [44, 47]

RS1 – There is legal protection in the use of CC.

RS2 – Rules and regulations supporting the use of CC.

RS3 – Cybercrime regulations encourage to adopt CC.

Competitive pressures adapted from [38 , 59]

CP1 – Does market pressurize the adoption of CC.

CP2 – Does your firm is under pressure from customers, suppliers and partners to adopt CC.

CP3 – Few competitors have already using CC.

Managerial support adapted from [43, 45]

TMS1 – The firm’s managerial supports the integration of CC.

TMS2 – The firm’s executive management and leadership committed to adopt IT.

TMS3 – The SMEs management is ready to take risks (financial and organizational) involved in the adoption of CC.

Firm Size adapted from [41 , 46]

SoF1 – Total number of workers.

SoF2 – Annual sale

SoF3 – Business annual turnover

CCA adapted from [3, 35]

CCA1- Its ease to use

CCA2- Its reliability

CCA3 - It is Security and private

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