Journal Search Engine
Search Advanced Search Adode Reader(link)
Download PDF Export Citaion korean bibliography PMC previewer
ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.18 No.3 pp.482-494

Validating Technology-Organization-Environment (TOE) Framework in Web 2.0 Adoption in Supply Chain Management

Arun Kumar Tarofder*, Adnan Jawabri, Ahasanul Haque, Sultan Rehman Sherief
Faculty of Business Management and Professional Studies, Management and Science University, Malaysia
Al Khawarizmi International College, United Arab Emirates
Department of Business Administration, International Islamic University, Malaysia
Faculty of Business Management and Professional Studies, Management and Science University, Malaysia
Corresponding Author, E-mail:
June 3, 2019 June 16, 2019 June 19, 2019


The second stage of Internet revolution has started with Web 2.0, which allows users to generate and develop the content without code. Web 2.0 not only change the way individual use internet but also tremendously transformed business activities. The primary aims of this study are (a) to validate the TOE framework in understanding Web 2.0 adoption in an organizational context, and (b) measuring the importance of each variable from the different industry perspective. This study developed a conceptual model based on the Technology-Organization-Environment (TOE) framework. A Webbased structured questionnaire was developed to collect primary data. With three months effort, this study managed to get 205 respondents from Malaysian manufacturing and service industry. Multiple regression and Dominance analysis were applied to understand the effect of the TOE framework on Web 2.0 adoption and predicting the importance of each factor form different industries perspective respectively. Multiple regression results confirmed that all the factors are important for Web 2.0 adoption, however, the technological characteristic is the most important determinant for Web 2.0 adoption. Moreover, dominance analysis showed very interesting results that relative advantage is not important for the service industry but top management support is the utmost importance. Similarly, results also indicated that top management support plays important role in Web 2.0 adoption for the fewer experience companies pertaining to internet usage. This study is one of the very few that provides insightful information regarding the effect of the TOE on Web 2.0 adoption in the supply chain management system. This study would be the guideline for the managers of both the manufacturing and service industry in order to implement the Web 2.0 in their supply chain system.



    Web 2 technology has dramatically transformed the way we do business, share information with suppliers and customers, managing inventory and so on (Ngah et al., 2017). Therefore, this Web 2 technology becomes a pre-dominant component of the business process in the 21st century. This technology continuously assists firms to extend their business by applying the new business model, gaining new market, improving understanding of their customers, which ultimately improves firms’ performance and image (Singh et al., 2018). Many gigantic companies, Microsoft, Apple, Samsung, name of few, have reaped competitive advantages in the market by adopting Web 2 technology in their supply chain process (Castorena et al., 2014;Duru and Chibo, 2014;Purnama, 2014;Dim and Ezeabasili, 2015;Wang and Lu, 2016;Pillai and Sivathanu, 2018).

    Supply Chain Management (SCM), one of the most complicated business process, includes several processes ranging from purchasing raw material from the right suppliers to deliver to the right customers with the aim of creating value for both business and customers (Sweeney et al., 2018;Haddud et al., 2017;Beh et al. 2016). More specifically, SCM emphasizes lowering the operational cost by selecting and interacting with the right sources or suppliers and providing unparalleled benefit to the customers by improving services and facilitating flexible delivery system (Hussain et al., 2018;Iqbal et al., 2017;Salman et al., 2018;Shabbir, 2009, Shabbir and Kassim, 2018;Shabbir et al., 2018). Though it is easy to design theoretically, however enabling such platform where both end users, suppliers and customers, work together with the firms is a difficult task. Web 2 technology, truly, has the most compatibility with such a complicated task (Singh et al., 2018). This technology is able to enhance real-time information sharing between many parties, which eventually improve the overall supply chain performance. In the case of Walmart, the biggest retail chain in the World reported that web 2 technology has improved their payment system, manage inventory better than ever, fulfil the customer order with zero error, and eventually gain more profit and market share. Not only Walmart, but there are also many gigantic companies across the world who has attuned with the Walmart. According to Tarofder et al. (2017), there is no doubt about the benefits of web 2 technology in managing the supply chain in the era of e-procurement and e-sourcing (Ali and Haseeb, 2019;Haseeb et al., 2018;Haseeb et al., 2019;Suryanto et al., 2018;Iravani and ShekarchiZade, 2014).

    The Malaysian government utterly accept the importance of IT integration in the business process and propose an IT-friendly budget. According to Rudman and Bruwer (2018), Malaysian IT expenditure increased by 5.7 % in 2018 and reached 65.2 billion USD. Malaysian government vividly promote IT sector by offering many benefits. According to Maroofi et al. (2017) , all these initiatives are the foundation of achieving the status of a developing country. Though the Malaysian government have taken many steps, however, the IT adoption rate is not at all fruitful. Kim and Galliers (2004) gloomily mentioned that less than 50 per cent of Malaysian organizations adopts IT extensively. He added that most of the Malaysian organizations adopt IT to operate the basic activities for instance e-mail, recording staff information. Very few organizations use IT for the collaboration and knowledge creating process. In line with this, Mahmood et al. (2008) explicitly mentioned that due to the lack of IT integration in the business process, Malaysian organizations are far lag behind to gain economies of scale. They strongly recommended investigating the reason for this reluctance (Santhi and Gurunathan, 2014;Anyanwu et al., 2016;Adewale, 2016;Nazal, 2017;Tanoos, 2017;Khan and Ali, 2017;Osasuyi and Mwakipsile, 2017;Mosbah et al., 2017;Tarofder et al., 2017;Malarvizhi et al., 2018;Le et al., 2018;Chowdhury et al., 2018;Singh and Singha, 2016).

    The implementation process of Web 2 technology in the traditional supply chain system is a challenging task in spite of immense benefits (Chaputula and Mutula, 2018;Hossain et al., 2017). Singh et al. (2018) postulated that successful implementation of web 2 technology requires smooth and effective integration of organizational, functional, and economic factors. In line with this, Tarofder et al. (2017) emphasized that technological compatibility of Web 2 with the existing system would be the main concern for the successful adoption of Web 2. Though there is an immense number of researches on the Web 2 technology, however, many questions regarding the effective adoption of Web 2 technology yet to be revealed, including, what are the important factors affecting the successful adoption of Web 2 technology? Does Technology-Environmental-Organizational theory validate for the successful adoption of Web 2 technology? What would be the appropriate managerial strategy for successful implementation of Web 2 technology in the SCM? Is there a difference in the importance of the factors affecting Web 2 technology implementation in different industries?

    As a result, this study tries to unveil the unknown regarding the effecting adoption of Web 2 technology in the context of SCM. More specifically the predominant aims of this study are two-fold, (a) validating TOE theory in relation to explain the effective adoption of Web 2 technology in SCM; (b) understanding the impotence of TOE from the different industry perspective. Without a doubt, there is an immense number of researches in the area of adoption by applying the concept of TOE in a different technological context, such as IT (Arya et al., 2017); EDI (Giannakis and Louis, 2016); ERP (Sasson and Johnson, 2016), however very few empirical research available in this context (Maurice, 2013;Chielotam, 2015;Castorena et al., 2014;Purnama, 2014;Luna-Maldonado et al., 2016;Mowlaei, 2017;Albasu and Nyameh, 2017;Maroofi et al., 2017;Küçükkocaoğlu and Bozkurt, 2018;Maldonado-Guzman et al., 2018;Pu et al. 2018;Yuen and Thai, 2017;Nze et al. 2016;Kimengsi and Gwan, 2017;Cheng et al., 2018;Le et al., 2018). Ranganathan et al. (2004), in their study, explicitly mentioned that vast amount of research is required to comprehend the knowledge of web technology adoption in the organizational context especially in the developing countries such as Malaysia. Hence, this study will fill this knowledge gap by applying quantitative techniques to understand the effect of TEO framework in Web 2 adoption.

    This study is different than prior studies in several ways. For instance, primary rather than secondary adoption is the key issue in this study. There is a substantial difference between these two types of adoption. Primary adoption mainly describes the organizational adoption process, whereas the secondary focus on an individual. This study mainly emphasis on primary adoption. Secondly, this study not only identifying important factors but also compare the importance of those factors from the different industry perspective, which eventually guides the industries in IT adoption process.


    2.1 Technology-Organization-Environment Theory

    Last two decades, many theories have been developing and articulating pertaining to technology adoption and diffusion. Diffusion of Innovation (DOI) (Rogers, 1995), Theory of Planned Behavior (Ajzen, 1985, 1991); Technology Acceptance Model (Davis et al., 1986), the TOE framework (Tornatzky et al., 1990) are the name of few. Among these theories, DOI, TAM and TOE are most popular among the IS researchers. Many scholars, however, argued that DOI and TAM do not include the environmental issues in their model, which is one of the main pitfalls of these theories. In their study, X (Awa et al., 2017) utterly argued that there must be more variable in Rogers and Davis model, which may give more comprehensive understudying of technological adoption. TOE, on the other hand, consist of three important components, technological; organizational and environmental; which enables researches to understand a holistic perspective of technological adoption. In their study, Awa et al. (2015), clearly mentioned that TOE is one of the very few theory that is able to explain the technology adoption process in 360 degrees (Ali and Haseeb, 2019). Hence this study used TOE as the foundation of the conceptual model. Additionally, this study used the theory of Technology Acceptance Model to understand the technological characteristic and its effect on technological adoption. Details about the conceptual model explain below:

    2.2 Characteristics of New Technology

    Attributes of the innovation play an utmost important role promoting the rate of adoption. All most all the prior studies vividly agreed with this association (Huda, 2019;Chhonker et al. 2018;Barhoumi, 2017). In their study, Jeyaraj et al. (2006) listed 100 different determinants of the technology adoption and broadly classified these variables into two main branches, namely individual and organizational domain. They added in their suggestions that perceived usefulness and perceived ease of the use are the two most used and the strongest influential variables or determinants for the technology adoption. These two variables were suggested by Davis et al in 1986 in their theory called Technology acceptance model and explained that both have a significant positive effect on technology adoption in the organizational setting. Therefore, this study includes both of them as characteristics of the technology (Haseeb et at 2018 ).

    2.3 Perceived Usefulness

    Without a doubt, organization use to invest promptly to that innovation which offers better benefits than the existing system, and the benefits that can be observed. In the context of Web 2 technology, perceived usefulness consider as the benefits associated with operational efficiency reaped from using Web 2 technology in supply chain context. In relation to benefits, Lee and Jung (2016) uttered that Web 2 technology is able to advocate organization not only in operational efficiency but also create an opportunity for being a strategic leader in the market. In their study, Verma and Bhattacharyya (2017) identified that Web 2 technology reduced more than 50 per cent of inventory cost for Walmart by managing inventory, subsequently, offered Walmart an unmatchable competitive advantage. Similarly, many prior studies, (Pillai and Sivathanu, 2018;Kumar et al., 2018) confirmed the positive relationship between perceived usefulness and the higher rate of technology adoption in the organizational setting (Haseeb et al., 2018;Kozhabergenova et al., 2018).

    2.4 Perceived Ease of Use

    Fundamentally, individual does not like to adopt technology that is difficult to understand or use. But it has a different effect in an organizational setting. Generally, however, there is a positive relationship between complexity and resistance to change. As a result, most of the adoption theorist agreed that complexity plays an adverse role in the innovation adoption process. In their theory, Davis et al. (1986) suggested that the organization shows a strong willingness to adopt those technologies that are easy to install and use (Osmonbekov and Johnston, 2018;Du et al., 2018). In this study, perceived ease of use considers as to what extent web 2 technology ease the users to understand and apply in their daily life without significant destruction. In relation to this, many researchers agreed that Web 2 is one of the easiest innovation that can fit with existing organizational system with minimal intervention (Lai et al., 2018;Gorane and Kant, 2017). Hence, this study proposes the following hypothesis:

    • H1: There is a positive significant effect of perceived usefulness on the adoption of web technology in SCM.

    • H2: There is a significant positive effect of perceived ease of use on the adoption of web technology in SCM.


    Unlike the individual, environmental factor becomes one of the utmost important concern for technological adoption in the organizational setting. In this study, external pressure consider as the environmental factor for the adoption of Web 2 technology in SCM. Efficiency is the focal point in supply chain and business partners often pressurize each other to be efficient. In their study, Zhu et al. (2018) mentioned that generally, large manufacturer uses to put pressure on their partners to be efficient in their business process by adopting technologies so that they can meet the world standard. As a result, rapid adoption of Web 2 technology may observe in the market (Mandal, 2017;Yuen and Thai, 2017;Osman et al., 2018). Similarly, many prior studies suggested that there is a strong association between the intensity of competition in the industry and Web 2 technology adoption rate. In other words, competitive environment demands maximum efficiency from all the actors, subsequently boost the technological adoption. Moreover, Beh et al., (2016) identified that being affiliated with the group of firms, many organizations adopt the technology. They also added that trading partners’ pressure plays important role in adopting innovation in the organizational context. Besides, being the pioneer in the industry, organization use to adopt technology to differentiate their product, service, or process. Hence this study considers competitive pressure as the most important determinant under environmental factor (Marakarkandy et al., 2017;Ramdani et al., 2013;Farzadnia et al., 2017;Carreto et al., 2018). It leads to the following hypothesis:

    • H3: There is a significant positive effect of competitive pressure on the adoption of web technology in SCM functions.


    Based on the TEO theory, organizational factors greatly influence the adoption process of technology. Organizational factors mainly related with the internal policies, employees’ skills, organizational objectives and so on. According to Hsu et al. (2017) alignment between organizational requirement and technological features determine the adoption rate of that particular technology. Almost every prior study agreed that top management support is an influential determinant for technological adoption (Gumel, 2017). Without getting full support from top management, technological adoption in the organizational setting is next to impossible. According to Jeyaraj et al. (2006), top management is the most popular determinant from the organizational context for the technological adoption. This factors can be considered as commitment, involvement, and support from the top management. Moreover, allocating adequate resources by top management also plays an important role in the successful adoption. Besides, top management provides the right direction for technological adoption (Rahi and Ghani, 2018;Hossain et al., 2017;Chatzoglou and Chatzoudes, 2016;Muhammad, 2018). Hence this study considers top management as an important determinant for web technology adoption and proposes the following hypothesis.

    • H4: There is a significant positive effect of top management support on the adoption of Web technology in SCM functions.


    5.1 Sampling and Respondents’ Attributes

    Pertaining to the sampling strategy, this study applied five steps, recommended by Sekaran and Bouge (2016). Based on the recommendation, this study identifies its population and selected Malaysian organizations who are using Web 2 technology in their supply chain. Indeed to identify these companies, this study assembles a sample frame combined the three most popular database in Malaysia, namely Federation of Malaysian Manufacturers (FMM), Malaysian Industrial Development Authority (MIDA), and Port Klang Shipping Agencies Association 2017. These three databases have comprehensive coverage the details regarding Malaysian manufacturing and service organizations, including total employees, annual turnover, email, address and so on. This study applied stratified random sampling by using a stratum that organization must use Web 2 technology for their business activities. After searching meticulously on the website, this study identified 2031 organizations as a sample unit. Initially, this study sent an invitation to all these firms and data collection was completed in June 2018. With only 10.09 per cent response rate, this study managed to get 205 responses in three months. This number deemed adequate in the organizational context. Hence, this study continues final data analysis with 205 responses.

    Demographic information regarding the organizations present in Table 1. Frequency results indicated that respondents from the manufacturing industry (59.02%) were slightly higher than the service industry. In relation to revenue, respondents had greater diversity. More specifically, 38.53 per cent respondents’ revenue were between 1 to 5 million followed by 5 to 10 million (30.73%). Additionally, 10.24 per cent respondents’ revenue was below 1 million, whereas 0.97 per cent respondents’ revenue was more than 50 million. One of the important discovery is that 78.04 per cent claimed that they have been using Web 2 technology for SCM for more than 5 years. Regarding the number of employees, results indicated that 31.70 respondents have less than 500 employees, whereas, 41.46 per cent claimed more than 1000 employees. Majority of the respondents were operation managers and procurement managers.

    5.2 Development of Instrument

    A structured web-based questionnaire was employed for this study and sent to those organizations, who were willing to participate. Several steps were adopted to develop the questionnaire suggested by Sekaran and Bouige (2016). The first step of the questionnaire development started with conceptual and operational definitions of the variables, which subsequently helps in selecting the right set of constructs. Based on the definition, this study adopted items from prior studies pertaining to technology adoption in different perspective such as EDI, ERP and so on. This adoption, eventually, ensure face, content and construct validity. This questionnaire adhered the principles of wording and measurement, which includes the length of the questions, the sequence of the questions, and so on. A cover letter was attached along with the questionnaire, which explains the aim of the project and also boosts the confidence of the respondents. Although, this questionnaire avoided recall based questions but need to include questions regarding their experience in using web technology. This was inevitable because these questions give an idea regarding the respondents’ experience. Simple English was applied in the construct. Pertaining to the collection, this study applied internet survey due to the three main reasons including (a) this method mitigate the problem of getting an appointment; (b) it reduces response error, inputting errors and interview bias. (c) It is a very costeffective method when respondents are geographically fragmented. There were 3 sections in this questionnaire. The first section included demographic information regarding the respondents’ organizations, including types of industry, annual revenue, and a number of employees and so on. The second section focused mainly on the conceptual model, including dependent and independent variables. Multiple items were used to measure the variables and all the items were adapted from prior studies. A pilot test was conducted to ensure the reliability of the scale.

    Exploratory Factor Analysis (EFA) was employed to confirm the discriminant validity and resents the results in Table 2. Mainly two types of scales, nominal and interval, were used for this questionnaire. Four items were used to measure all the fours independent variables and adopted from prior studies (Mosbeh and Soliman, 2008;Nagi et al., 2007;Alam et al., 2007). EFA results confirmed five variable with eigenvalue more than 1. All these four independent variables explained 79.693 variances. Moreover, factor loading values were satisfactory, ranging from .790 to .857. Cronbach alpha value for the five variables presented in Table 3 and it shows satisfactory for all the five variables. Based on the results, this study can conclude that the data it effective for the hypothesis testing.

    5.3 Testing of Hypothesis

    Multiple Regression analysis was used to identify the effect of independent variables on the dependent variables. Table 5 presents the results of multiple regression. Results confirmed that 62.2 per cent variance of Web 2 technology adoption explained by these four factors. Results indicated the, 42.1 per cent variance of Web 2 adoption explained by perceived ease of use, followed by perceived usefulness (31.6%), top management support (28.2%), and competitive pressure (24.1%). Therefore, results accepted all the four hypothesis. Table 4

    In their study, Ngah et al. (2017) utterly mentioned that perceived ease of use is the most important factors influencing Web 2 technology adoption. This study further proved the same. It means the Malaysian organizations emphasis on the perceived ease of use before implementing Web 2 technology in their supply chain management system. Besides, perceived usefulness also plays an important role in influencing the Web 2 adoption in Malaysian organizations. In this regard, Karahoca et al. (2018) suggested that none of the organization will embrace new technology unless it offers better service and facilities than the existing one. Similar results found in this study too. In relation with top management support, this became the third most influential in this study and similar results can be found in many previous studies (Corinna et al., 2017;Dey et al., 2016;Ahmadi et al., 2018;Sharif and Butt, 2017). In their study, Molinillo and Japutra (2017) utterly mentioned that top management support is one of the top most influential factors for the Web 2 adoption in the organizational setting. Last but not least, results also confirmed the significant positive effect of competitive pressure on the Web 2 technology adoption in Malaysian organization. In one study, Fuchs et al. (2018) mentioned that in order to compete with competitors’ organizations willing to adopt cutting- edge technology. This study’s results show similar findings and conclude that competitive pressure has a significant positive effect on the Web 2 technology adoption. Results of this study refinement and reconfirmed the findings of the prior studies.

    5.4 Prediction of Importance

    The second objective of this study is to identify the differences in the importance of these four factors in different industries (manufacturing and service), and different level of experience (more than and less than 5 years) of using Web 2 technology in their supply chain activities. In order to achieve this objective, this study applied the most popular technique name dominance analysis. This analysis originally developed by Budescu (1993), and have become popular in recent years among social science researches. The predominant features of this techniques are that this test is able to measure the relative importance of the predictor variables (Behson, 2002;Tu, 2018). In addition to this, this techniques is able to depict the contribution of each predictor variables on the dependent variables (Budescu and Azen, 2004). Table 6, presents the effect of each predictor variable on Web 2 technology adoption for both manufacturing and service industry. An interesting outcome can be identified from this dominance analysis.

    From Table 6, it is clear that all the four independent variables are important for the manufacturing industry, on contrary, perceived usefulness is not significant in the service industry. Moreover, it is clear that both perceived ease of use and top management support are the most important determinants for Web 2 technology adoption in the manufacturing and service industry respectively. Unlike hospital, saloon, most of the service industry transform and deliver their service through online. Hence perceived usefulness became insignificant in this industry, however top management support becomes important determinants (Asare et al., 2016). On the other side of the coin, the manufacturing industry still focuses on the easiness of the new technology in terms of using, understanding and implementing in day-to-day life. Unlike the service industry, it is quite difficult to replace the entire existing system, hence manufacturing industry mainly focus on the perceived ease of use.

    Similarly, Table 7 presents the effect of each predictor’s effect on Web 2 technology adoption for two group of organization based on the experience of using Web 2 technology (more than 5 and less than 5 years). Though results showed that all four predictors are important for both groups, however, perceived ease of use is the most important predictors for those organizations who have more than 5 years of experience. On the other hand, top management support became the most important for those organizations who have less than 5 years of experience.

    5.5 Discussions, Contributions and Managerial Implications

    Based on the results, ‘perceived ease of use’ is the most important factor for the Web 2 technology adoption in the organizational setting for both industries. These results provide an in-depth understanding of this factor. In general, organizations always focus on those technologies which are easy to implement, understand and use with minimal disruption to the existing system. It is because a completely new system requires a huge investment in implementing and training. Hence, the organization likely to find a technology that can improvise an existing system with minimal disruption. Therefore, this result reconfirmed, theoretically and practically, that ‘perceived ease of use’ is important for Web 2 technology adoption in Malaysian organizational SCM system.

    Secondly, this study confirmed that ‘perceived usefulness’ is the second most important factors influencing Web 2 adoption in Malaysian organizations. Doubtlessly, most of the prior studies and theory confirmed that organ-izations do not adopt any technology if it does offer allure benefits. However, the importance of this factor difference between manufacturing and service industry. Despite being highly significant, results indicated that perceived usefulness is not influential determinant to boost Web 2 technology adoption in the Malaysian service industry. Top management support plays the most important role in the service industry instead of perceived usefulness. Practically, service industry relatively more dynamic than manufacturing, hence response to the industry and environmental changes are key for any service organization rather emphasizing the benefits of the technology.

    In relation to competitive pressure, results confirmed that it is more important than top management support in Malaysian organization. There is no doubt that every organization wants to secure a strong position in the market, as a results organization immediately respond to their competitors’ technological initiatives. For instance, though there is very minimal students’ engagement in the local universities web portal, however, every university is offering their service through their web portal. It is clear that organization want to, therefore, competitive pressure accelerate the adoption process of Web 2 technology in Malaysian organizations. However, technology developer must not ignore the part that top management also plays a significant role in accelerating the adoption process. In fact, they are the initiator, hence, convincing them with attractive benefits would the right strategy to diffuse the Web 2 technology in the SCM.

    Empirically, this study validates the TOE framework for the Web 2 technology and confirmed that all three factors, technology, organization and environment, are important for the Web 2 adoption in the organizational setting. Additionally, results ensure that technological factors are the most important followed by environment and organization for technological adoption. Pertaining to the experience, dominance test showed that perceived ease of use is the most important for the more experienced organization followed by competitive pressure, top management support and perceived usefulness. On the contrary, top management support is the most important for less experience organization followed by competitive pressure. Therefore, it is wise to develop new technology which can easily be compatible with the existing system and convince top management to accelerate the adoption process.


    Despite having significant contributions to this study, some inevitable limitations can’t be avoided. Firstly, this study followed cross-sectional techniques which may help you understand the apparent perception of the respondents, however, it is wise to test the effect of all these variables by applying longitudinal research setting. Besides, this study included only four variables though it is the most important, this study significantly ignores the moderating effect of organizational size, number of employees name of few. It is because the primary aim of this study was to validate the TOE model to understanding Web 2 adoption in SCM context. Hence, it is worth testing this moderating effect on the proposed model of this study.

    Despite having these limitations, this study indeed is one of the very few studies that validated TOE framework for the Web 2 adoption, especially in supply chain context. This study provides significant contributions in both knowledge and practice. This study, additionally, illustrated the difference in the importance of the TOE framework from both the manufacturing and service industry. With this knowledge, managers would have indepth understating pertaining to web 2 technology adoption and will be the guideline for their further references.



    Summary of sample characteristics

    Tests for unidimensionality or discriminant validity

    Reliability test

    Test of collinearity

    Hypothesis testing

    Impact of explanatory variables on adoption of web2 technology: Manufacturing sector vs service sector

    Impact of explanatory variables on adoption of web 2 technology: firms with usage of W2T < 5 Yrs Vs firms with usage of W2T > 5 Yrs


    1. Adewale, A. A. (2016), Change, customer satisfaction and competition: Issues from the strategic management context, International Journal of Economics, Business and Management Studies, 3(2), 55-66.
    2. Ahmadi, F. , Rahimi, M. , and Rezaei, A. (2018), Study of relation between business model and sensemaking decisions, Journal of Humanities Insights, 02(02), 99-108.
    3. Ajzen, I. (1985), From intentions to actions: a theory of planner behavior. In: Kuhl, J. and Beckmann, J. (Eds.), Action Control: From Cognition to Behavior, Springer-Verlag, New York, NY, 11-39.
    4. Ajzen, I. (1991), The theory of planned behavior, Organizational Behavior and Human Decision Processes, 50(2), 179-211.
    5. Alam, S. S. and Ahsan, N. (2007), ICT adoption in Malaysian SMEs from service sector: Preliminary findings, Journal of Internet Banking and Commerce, 12(3), 1-11.
    6. Albasu, J. and Nyameh, J. (2017), Relevance of stakeholders theory, organizational identity theory and social exchange theory to corporate social responsibility and employees performance in the commercial banks in Nigeria, International Journal of Business, Economics and Management, 4(5), 95-105.
    7. Ali, A. and Haseeb, M. (2019), Radio frequency identification (RFID) technology as a strategic tool towards higher performance of supply chain operations in textile and apparel industry of Malaysia, Uncertain Supply Chain Management, 7(2), 215-226.
    8. Anyanwu, J. O. , Okoroji, L. I. , Ezewoko, O. F. , and Nwaobilor, C. A. (2016), The impact of training and development on workers performance in imo state, Global Journal of Social Sciences Studies, 2(2), 51-71.
    9. Arya, V. , Sharma, P. , Singh, A. , and De Silva, P. (2017), An exploratory study on supply chain analytics applied to spare parts supply chain, Benchmarking: An International Journal, 24(6), 1571-1580.
    10. Asare, A. K. , Brashear-Alejandro, T. G. , and Kang, J. (2016), B2B technology adoption in customer driven supply chains, Journal of Business & Industrial Marketing, 31(1), 1-12.
    11. Awa, H. , Ojiabo, O. , and Emecheta, B. (2015), Integrating TAM, TPB and TOE frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs, Journal of Science & Technology Policy Management, 6(1), 76-94.
    12. Awa, H. , Ojiabo, O. , and Orokor, L. (2017), Integrated technology-organization-environment (T-O-E) taxonomies for technology adoption, Journal of Enterprise Information Management, 30(6), 893-921.
    13. Barhoumi, C. (2017), Analysis of technological, individual and community factors influencing the use of popular Web 2.0 tools in LIS education, The Electronic Library, 35(5), 977-993.
    14. Beh, L. , Ghobadian, A. , He, Q. , Gallear, D. , and O’Regan, N. (2016), Second-life retailing: A reverse supply chain perspective, Supply Chain Management: An International Journal, 21(2), 259-272.
    15. Behson, S. J. (2002), Coping with family-to-work conflict: The role of informal work accommodations to family, Journal of Occupational Health Psychology, 7(4), 324-341.
    16. Budescu, D. V. (1993), Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression, Psychological Bulletin, 114(3), 542-551.
    17. Budescu, D. V. and Azen, R. (2004), Beyond global measures of relative importance: Some insights from dominance analysis, Organizational Research Methods, 7(3), 341-350.
    18. Carreto, C. , Gêgo, D. , and Figueiredo, L. (2018), An eyegaze tracking system for teleoperation of a mobile robot, Journal of Information Systems Engineering & Management, 3(2), 16.
    19. Castorena, O. H. , Enríquez, L. A. , and Adame, M. G. (2014), The influence of information technology and communication supply chain management performance for greater SME manufacturing in aguascalientes, International Journal of Business, Economics and Management, 1(12), 382-396.
    20. Chaputula, A. H. and Mutula, S. (2018), eReadiness of public university libraries in Malawi to use mobile phones in the provision of library and information services, Library Hi Tech, 36(2), 270-288.
    21. Chatzoglou, P. and Chatzoudes, D. (2016), Factors affecting e-business adoption in SMEs: An empirical research, Journal of Enterprise Information Management, 29(3), 327-358.
    22. Cheng, C. P. , Phung, M. T. , Hsiao, C. L. , Shen, D. B. , and Chen, B. S. (2018), Impact of operational risk toward the efficiency of banking-evidence from Taiwans banking industry, Asian Economic and Financial Review, 8(6), 815-831.
    23. Chhonker, M. , Verma, D. , Kar, A. , and Grover, P. (2018), M-commerce technology adoption: Thematic and citation analysis of scholarly research during (2008- 2017), The Bottom Line, 31(3/4), 208-233
    24. Chielotam, A. N. (2015), Oguamalam masquerade performance beyond aesthetics, Humanities and Social Sciences Letters, 3(2), 63-71.
    25. Chowdhury, T. S. , Habibullah, M. , and Nahar, N. (2018), Risk and return analysis of closed-end mutual fund in Bangladesh, Journal of Accounting, Business and Finance Research, 3(2), 83-92.
    26. Corinna, A. C. , Marco, A. D. , and Rafele, C. (2017), Egrocery supply chain management enabled by mobile tools, Business Process Management Journal, 23(1), 47-70.
    27. Davis, F. D. , Bagozzi, R. P. , and Warshaw, P. R. (1986), User acceptance of computer technology: A comparison of two theoretical models, Management Science, 35(8), 982-1003.
    28. Dey, A. , Vijayaraman, B. S. , and Choi, J. H. (2016), RFID in US hospitals: An exploratory investigation of technology adoption, Management Research Review, 39(4), 399-424.
    29. Dim, N. U. and Ezeabasili, A. C. C. (2015), Strategic supply chain framework as an effective approach to procurement of public construction projects in Nigeria, International Journal of Management and Sustainability, 4(7), 163-172.
    30. Du, W. , Qian, Z. , Parsons, P. , and Chen, Y. (2018), Personal web library: Organizing and visualizing web browsing history, International Journal of Web Information Systems, 14(2), 212-232.
    31. Duru, P. N. and Chibo, C. N. (2014), Flooding in imo state Nigeria: The socio-economic implication for sustainable development, Humanities and Social Sciences Letters, 2(3), 129-140.
    32. Farzadnia, E. , Hosseini, Z. , and Riahi, A. (2017), Study of hospital quality management and improvement rates in the hospitals, Journal of Humanities Insights, 01(01), 7-11.
    33. Fuchs, C. , Beck, D. , Lienland, B. , and Kellner, F. (2018), The role of IT in automotive supplier supply chains, Journal of Enterprise Information Management, 31(1), 64-88.
    34. Giannakis, M. and Louis, M. (2016), A multi-agent based system with big data processing for enhanced supply chain agility, Journal of Enterprise Information Management, 29(5), 706-727.
    35. Gorane, S. and Kant, R. (2017), Supply chain practices and organizational performance: An empirical investigation of Indian manufacturing organizations, The International Journal of Logistics Management, 28(1), 75-101.
    36. Gumel, F. (2017), The effects of European Negotiatory state of turkey on local management, Journal of Humanities Insights, 1(2), 57-62.
    37. Haddud, A. , DeSouza, A. , Khare, A. , and Lee, H. (2017), Examining potential benefits and challenges associated with the internet of things integration in supply chains, Journal of Manufacturing Technology Management, 28(8), 1055-1085.
    38. Haseeb, M. , Abidin, I. S. Z. , Hye, Q. M. A. , and Hartani, N. H. (2018), The impact of renewable energy on economic well-being of Malaysia: Fresh evidence from auto regressive distributed lag bound testing approach, International Journal of Energy Economics and Policy, 9(1), 269-275.
    39. Haseeb, M. , Zandi, G. , Hartani, N. H. , Pahi, M. H. , and Nadeem, S. (2019), Environmental analysis of the effect of population growth rate on supply chain performance and economic growth of Indonesia, Ekoloji, 28(107), 417-426.
    40. Hossain, M. , Standing, C. , and Chan, C. (2017), The development and validation of a two-staged adoption model of RFID technology in livestock businesses, Information Technology & People, 30(4), 785-808.
    41. Hsu, C. , Lin, Y. , Chen, M. , Chang, K. , and Hsieh, A. (2017), Investigating the determinants of e-book adoption, Program, 51(1), 2-16.
    42. Huda, M. (2019), Empowering application strategy in the technology adoption: Insights from professional and ethical engagement, Journal of Science and Technology Policy Management, 10(1), 172-192.
    43. Hussain, S. , Fangwei, Z. , Siddiqi, A. F. , Ali, Z. , and Shabbir, M. S. (2018), Structural equation model for evaluating factors affecting quality of social infrastructure projects, Sustainability, 10(5), 1-25.
    44. Iqbal, J. , Shabbir, M. S. , Zameer, H. , Tufail, M. S. , Sandhu, M. A. , and Ali, W. (2017), TQM practices and firm performance of Pakistani service sector firms, Paradigms: A Research Journal of Commerce, Economics, and Social Sciences, 11(1), 87-96.
    45. Iravani, M. R. and ShekarchiZade, A. (2014), A social work study of effective cultural, social economic factors on work stress: A review, UCT Journal of Management and Accounting Studies, 1(4), 5-7.
    46. Jeyaraj, A. , Rottman, J. W. , and Lacity, M. C. (2006), A review of the predictors, linkages, and biases in IT innovation adoption research, Journal of Information Technology, 21(1), 1-23.
    47. Karahoca, A. , Karahoca, D. , and Aksöz, M. (2018), Examining intention to adopt to internet of things in healthcare technology products, Kybernetes, 47(4), 742-770.
    48. Khan, S. N. and Ali, E. I. E. (2017), The moderating role of intellectual capital between enterprise risk management and firm performance: A conceptual review, American Journal of Social Sciences and Humanities, 2(1), 9-15.
    49. Kim, C. and Galliers, D. R. (2004), Toward a diffusion model for internet systems, Internet Research, 14(2), 155-166.
    50. Kimengsi, J. N. and Gwan, S. A. (2017), Reflections on decentralization, community empowerment and sustainable development in Cameroon, International Journal of Emerging Trends in Social Sciences, 1(2), 53-60.
    51. Kozhabergenova, G. E. , Taubaeva, S. , Bulatbayeva, A. A. , Kabakova, M. P. , and Asanov, N. A. (2018), The stewardship of school counselor education in higher educational establishments, Opción, 34(85-2), 386- 414.
    52. Küçükkocaoğlu, G. and Bozkurt, M. A. (2018), Identifying the effects of mergers and acquisitions on turkish banks performances, Asian Journal of Economic Modelling, 6(3), 235-244.
    53. Kumar, R. , Sachan, A. , and Mukherjee, A. (2018), Direct vs indirect e-government adoption: An exploratory study, Digital Policy, Regulation and Governance, 20(2), 149-162.
    54. Lai, Y. , Sun, H. , and Ren, J. (2018), Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation, The International Journal of Logistics Management, 29(2), 676-703.
    55. Le, H. L. , Vu, K. T. , Du, N. K. , and Tran, M. D. (2018), Impact of working capital management on financial performance: The case of Vietnam, International Journal of Applied Economics, Finance and Accounting, 3(1), 15-20.
    56. Lee, S. and Jung, K. (2016), A meta-analysis of determinants of RFID adoption around the world: Organization, technology, and public policy, Asia Pacific Journal of Innovation and Entrepreneurship, 10(1), 67-90.
    57. Luna-Maldonado, U. , Flores-Breceda, H. , Vidales-Contreras, J. A. , Rodríguez-Fuentes, H. , and Luna-Maldonado, A. I. (2016), Technological skills in the academic performance of students, International Journal of Education and Practice, 4(9), 234-242.
    58. Mahmood M. A. , Gemoets L. , Hall L. L. , López F. J. , and Mariadas R. , (2008), Measuring e-commerce technology enabled business value: An exploratory research, International Journal of E-Business Research, 4(2), 48-68.
    59. Malarvizhi, C. A. , Nahar, R. , and Manzoor, S. R. (2018), The strategic performance of Bangladeshi private commercial banks on post implementation relationship marketing, International Journal of Emerging Trends in Social Sciences, 2(1), 28-33.
    60. Maldonado-Guzman, G. , Marin-Aguilar, J. , and Garcia- Vidales, M. (2018), Innovation and performance in latin-american small family firms, Asian Economic and Financial Review, 8(7), 986-998.
    61. Mandal, S. (2017), The influence of dynamic capabilities on hospital-supplier collaboration and hospital supply chain performance, International Journal of Operations & Production Management, 37(5), 664-684.
    62. Marakarkandy, B. , Yajnik, N. , and Dasgupta, C. (2017), Enabling internet banking adoption: An empirical examination with an augmented technology acceptance model (TAM), Journal of Enterprise Information Management, 30(2), 263-294.
    63. Maroofi, F. , Ardalan, A. G. , and Tabarzadi, J. (2017), The effect of sales strategies in the financial performance of insurance companies, International Journal of Asian Social Science, 7(2), 150-160.
    64. Maurice, I. U. (2013), Impact of product development and innovation on organisational performance, International Journal of Management and Sustainability, 2(12), 220-230.
    65. Molinillo, S. and Japutra, A. (2017), Organizational adoption of digital information and technology: A theoretical review, The Bottom Line, 30(01), 33-46.
    66. Mosbah, A. , Serief, S. R. , and Wahab, K. A. (2017), Performance of family business in Malaysia, International Journal of Social Sciences Perspectives, 1(1), 20-26.
    67. Mosbeh, R. and Soliman, S. K. (2008), An exploratory of factors affecting users adoption of corporate intranet, Management Research News, 31(5), 375-385.
    68. Mowlaei, M. (2017), The impact of AFT on export performance of selected Asian developing countries, Asian Development Policy Review, 5(4), 253-261.
    69. Muhammad, K. (2018), The effects of electronic human resource management on financial institutes, Journal of Humanities Insights, 02(01), 1-5.
    70. Nagi, E. W. T. , Poon, J. K. L. , and Chan, Y. H. C. (2007), Empirical examination of the adoption of WebCT using TAM, Computers & Education, 48(2), 250-267.
    71. Nazal, A. I. (2017), Financial tables reports gaps in Jordanian Islamic banks, The Economics and Finance Letters, 4(2), 9-15.
    72. Ngah, A. H. , Zainuddin, Y. , and Thurasamy, R. (2017), Applying the TOE framework in the Halal warehouse adoption study, Journal of Islamic Accounting and Business Research, 8(2), 161-181.
    73. Nze, I. C. , Ogwude, I. C. , Nnadi, K. U. , and Ibe, C. C. (2016), Modelling the relationship between demand for river port services and vessel supply costs: Empirical evidence from Nigeria, Global Journal of Social Sciences Studies, 2(3), 144-149.
    74. Osasuyi, J. O. and Mwakipsile, G. (2017), Working capital management and managerial performance in some selected manufacturing firms in Edo State Nigeria, Journal of Accounting, Business and Finance Research, 1(1), 46-55.
    75. Osman, S. , Yang, C. C. N. A. , Abu, M. S. , Ismail, N. , Jambari, H. , and Kumar, J. A. (2018), Enhancing students’ mathematical problem-solving skills through bar model visualisation technique, International Electronic Journal of Mathematics Education, 13(3), 273-279.
    76. Osmonbekov, T. and Johnston, W. J. (2018), Adoption of the internet of things technologies in business procurement: Impact on organizational buying behavior, Journal of Business & Industrial Marketing, 33(6), 781-791.
    77. Pillai, R. and Sivathanu, B. (2018), An empirical study on the adoption of M-learning apps among IT/IteS employees, Interactive Technology and Smart Education, 15(3), 182-204.
    78. Pu, X. , Chan, F. , Tsiga, Z. , and Niu, B. (2018), Adoption of internet-enabled supply chain management systems: Differences between buyer and supplier perspectives, Industrial Management & Data Systems, 118(8), 1695-1710.
    79. Purnama, C. (2014), Improved performance through empowerment of small industry, Journal of Social Economics Research, 1(4), 72-86.
    80. Rahi, S. and Ghani, M. A. (2018), The role of UTAUT, DOI, perceived technology security and game elements in internet banking adoption, World Journal of Science, Technology and Sustainable Development, 15(4), 338-356.
    81. Ramdani, B. , Chevers, D. , and A. Williams, (2013), (2013), SMEs’ adoption of enterprise applications: A technology-organisation-environment model, Journal of Small Business and Enterprise Development, 20(4), 735-753.
    82. Ranganathan, C. , Dhaliwal, J. S. , and Teo, T. S. H. (2004), Assimilation and diffusion of web technologies in supply chain management: An examination of key drivers and performance impacts, International Journal of Electronic Commerce, 9(1), 127-161.
    83. Rogers, E. M. (1995), Diffusion of Innovations (4th ed.), The Free Press, New York.
    84. Rudman, R. and Bruwer, R. (2016), Defining Web 3.0: Opportunities and challenges, The Electronic Library, 34(1), 132-154.
    85. Salman, R. , Arshad, D. , Bakar, L. J. A. , Shabbir, M. S. , and Shabbir, M. F. (2018), The effect of innovative cultural processes on performance of small and medium size enterprises, Management Science Letters, 8(10), 1039-1048.
    86. Santhi, N. S. and Gurunathan, K. B. (2014), Fama-French three factors model in Indian mutual fund market, Asian Journal of Economics and Empirical Research, 1(1), 1-5.
    87. Sasson, A. and Johnson, J. C. (2016), The 3D printing order: Variability, supercenters and supply chain reconfigurations, International Journal of Physical Distribution & Logistics Management, 46(1), 82-94.
    88. Sekaran, U. and Bougie, R. (2016), Research Methods for Business: A Skill Building Approach (5th ed.), Wiley, New York, NY.
    89. Shabbir, M. S. (2009), Supportive learning environment: A basic ingredient of learning organization, Proceedings of the 2nd CBRC, Lahore, Pakistan.
    90. Shabbir, M. S. and Kassim, N. M. (2018), Supply chain management drivers and sustainability of green initiatives in manufacturing enterprises: A case In Pakistan, International Journal of Entrepreneurship, 22(15), 1-19.
    91. Shabbir, M. S. , Shariff, M. N. M. , Asad, M. , Salman, R. , and Ahmad, I. (2018), Time-frequency relationship between innovation and energy demand in Pakistan : Evidence from wavelet coherence analysis, International Journal of Energy Economics and Policy, 8(5), 251-258.
    92. Sharif, A. and Butt, H. (2017), Online businesses and influence of e-marketing on customer satisfaction, Journal of Humanities Insights, 01(02), 89-93.
    93. Singh, A. K. and Singha, N. C. (2016), Environmental impact of nuclear power: Law and policy measures in India, Humanities & Social Sciences Reviews, 4(2), 88-95.
    94. Singh, J. , Chandwani, R. , and Kumar, M. (2018), Factors affecting Web 2.0 adoption: Exploring the knowledge sharing and knowledge seeking aspects in health care professionals, Journal of Knowledge Management, 22(1), 21-43.
    95. Suryanto, T. , Haseeb, M. , and Hartani, N. H. (2018), The correlates of developing green supply chain management practices: Firms level analysis in Malaysia, International Journal of Supply Chain Management, 7(5), 316-324.
    96. Sweeney, E. , Grant, D. , and Mangan, D. (2018), Strategic adoption of logistics and supply chain management, International Journal of Operations & Production Management, 38(3), 852-873.
    97. Tanoos, J. J. (2017), East Asian trade cooperation versus us and eu protectionist trends and their association to chinese steel exports, Asian Journal of Economics and Empirical Research, 4(1), 1-7.
    98. Tarofder, A. , Azam, S. , and Jalal, A. (2017), Operational or strategic benefits: Empirical investigation of internet adoption in supply chain management, Management Research Review, 40(1), 28-52.
    99. Tornatzky, L. G. , Fleischer, M. , and Chakrabarti, A. K. (1990), The Process of Technology Innovation, Lexington Books, Lexington, MA.
    100. Tu, M. (2018), An exploratory study of Internet of Things (IoT) adoption intention in logistics and supply chain management: A mixed research approach, The International Journal of Logistics Management, 29(1), 131-151.
    101. Verma, S. and Bhattacharyya, S. (2017), Perceived strategic value-based adoption of big data analytics in emerging economy: A qualitative approach for Indian firms, Journal of Enterprise Information Management, 30(3), 354-382.
    102. Wang, Y. B. and Lu, J. R. (2016), A supply-lock competitive market for investable products, Asian Development Policy Review, 4(4), 127-133.
    103. Yuen, K. and Thai, V. (2017), The influence of supply chain integration on operational performance: A comparison between product and service supply chains, The International Journal of Logistics Management, 28(2), 444-463
    104. Zhu, S. , Song, J. , Hazen, B. , Lee, K. , and Cegielski, C. (2018), How supply chain analytics enables operational supply chain transparency: An organizational information processing theory perspective, International Journal of Physical Distribution & Logistics Management, 48(1), 47-68.