Process of big data analysis adoption: Defining big data as a new IS innovation and examining factors affecting the process

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1 th Hawaii International Conference on System Sciences Process of big data analysis adoption: Defining big data as a new IS innovation and examining factors affecting the process Dal-woo, Nam, Dong-woo, Kang, SoungHie, Kim College of Business, Korea Advanced Institute of Science and Technology ddalwoo@business.kaist.ac.kr, sparklingade@gmail.com, seekim@business.kaist.ac.kr Abstract This paper defines big data analysis a type3 innovation and extends our previous studies on the adoption/assimilation of innovation technologies. The paper develops a three-stage adoption integrative model based on the past diffusion context literatures. The model utilizes TOE (Technology-Organization- Environment) framework as antecedents of this adoption process. Based on the perception model, we hypothesize how perceived direct/indirect benefit, financial readiness, IS competence, industrial pressure affects big data analysis adoption at the organizational level. These five factors are tested using SEM(Structural Equation Modeling) and our analysis leads to following key findings. (1)Financial readiness, IS competence, and industrial pressure are found to affect adoption stages significantly but we could not find such relationship between perceived direct/indirect benefit and the following stages. (2)IS competition had expansive influence on the overall adoption process. (3)Adoption stage is influenced by external factors, which is industrial pressure for our case. (4) Pre and post stages of adoption are affected by internal resources of organization rather than environments. 1. Introduction Big data is at the heart of modern business world. If we put our clock back to the 90 s, this phenomenon roots from the term business intelligence (BI) and analytics. Since then BI and related field of big data analytics have become increasingly important both in science and business world. Necessity of grappling with big data, and the desire to unlock valuable insights, is now a key theme. In order for us to properly understand the term, we need to focus on the unique characteristic of big data. The first characteristic we propose is that big data describes overall business centric practice. Swanson [1] have categorized IS innovation into three types. Among them type3 innovation has most broad impact on the firm. If we have to classify big data in Swanson s definition on types of innovation, Big data analysis could be classified as type3 innovation in that it has very expansive impact on the firm system. The term describes a series of activities to utilize the value of data, including data procurement to extraction. Second feature of big data analysis is that it has multi-purposes. Unlike typical Information systems big data analysis could be utilized across every sector in the value chain, and firms could have different goals when deploying it. So keeping these features in mind the research proposes unique model for big data analysis adoption. Today, enterprises are eager to explore big data to discover facts they didn t know before. So in order to keep up with the competition around everywhere, it is important to understand which factors affect firms to adopt big data analysis and also implementing it successfully. Drawing upon past literatures on innovation diffusion, we defined three stages of adoption. By categorizing adoption stage in three initiation, adoption, assimilation we set up model to understand the key factors that influence big data analysis assimilation. The model is set to examine how these factors may have differential effects at different adoption stages using TOE (Technology- Organization-Environment framework). We seek to study following questions: 1) What factors affect big data adoption process? 2) What theoretical perspectives can be used to study big data assimilation? 3) How would each factor vary across different stages of adoption? 2. Literature review 2.1. Definition of Big Data Analysis Recently, the term big data and big data analysis refers to data sets that are so large and complex that they require advanced storage, management, visualization and analysis technologies. The term sets its root on business intelligence which was popular in the business and IT communities only in the 1990s. Since then business intelligence and related field of /15 $ IEEE DOI /HICSS

2 big data analytics has become an important agenda for over two decades[2]. From scientists, researchers and to business people, they are eager to access to the massive quantities of information from various sources. The term big data has been used in the sciences to refer to data sets large enough to require supercomputers. The term is vague enough, since nowadays what once required such machines can now be analyzed on desktop computers. So we could see that volume is not the defining characteristic of this new paradigm. Rather it is about a capacity to search, aggregate, and cross-reference large data sets [3]. In this research big data analysis is classified as type 3 innovation. It is said that IS innovation technologies undergo three distinct pathways of organizational adoption and use. Among them, type3 innovation has most broad impact on the firm. Type1 is close to functional core. It is innovation that focuses on technical task or administrative system supporting information system work. Type2 innovation refer to the use of IS products and services for enhancing the administrative work process of firms, which is beyond the IS function. Type3 innovation is information technologies that have strategic relevance for firms because their integration into the core business processes or strategies could directly impact financial performance. The definition could vary among the accepting parties recognition. Part of the challenge lies in the unique nature of different information technologies, their heterogeneous applications, and their various impacts. In some literatures Business Intelligence is seen as IT-intensive systems [4]. In this research, big data will be considered as one of IT-intensive system for data analysis and reporting that provide managers at various levels of the organization with timely, relevant, and easy to use information, which enable them to make better decisions [5]. As in the definition stated above, to judge whether certain firm has adopted or not is rather difficult. It is better to see how much they are integrated into their routine business process Information technology adoption and assimilation TOE framework for IS innovation adoption. Since we need to identify factors that affect the big data analysis adoption, we use TOE (Technology- Organization-Environment) framework. This describes the process, when a firm adopts and implements technological innovation, is influenced by the technological context, the organizational context, and the environmental context [6]. These three elements present both constraints and opportunities for technological innovation [6]. The technological context includes both the internal and external technology that is relevant to the firm. Organizational context refers to characteristics and resources of the firm. It includes descriptive measures such as firm s size, scope, resources, managerial structure, etc. Environmental context includes the size and structure of the industry, the firm s competitors and dealings with government [6]. The TOE framework has been used in earlier studies. Most of the framework is targeted at identifying factors that influence the adoption of certain system. Many past literatures [7-9] have drawn on the TOE framework to identify facilitators and inhibitors for e- business adoption decision. Lee and Shim [10] had examined how TOE factors affect RFID adoption in the healthcare industry. TOE framework has been especially examined by adoption of EDI. Tornatzky, Fleischer and Chakrabarti [6], developed a model formulating technological, organizational, and environmental factors as the main drivers for EDI adoption. Mostly dichotomous decision variable was used in these studies, which indicates whether certain firm has adopted the innovation system or not. But also some other literatures involved post adoption stages. Thong [12] examined the role of TOE factors to influence likelihood of IS adoption and extent of IS adoption. Focused on the retail industry, Zhu and Kraemer [7] studied how TOE factors may influence post adoption variations in usage and value of e- business. After reviewing such theoretical roots and empirical evidence, TOE framework has proved to be consistent. Still, specific measures within the three contexts could vary across different settings Organizational Level Process Models of Assimilation. Classically in the previous literatures, assimilation is defined as the extent to which the use of a technology diffuses across organizational work process and becomes routinized in the activities associated with those processes [13-16]. Rogers [17] advocated developing this kind of models to capture the complex, over-time nature of innovation process in organizations which permits greater insight in tracing the nature of the innovation process. Assimilation is one of process and stage model and it is useful in identifying the context in which events occur and show the causal linkages and temporal relationships among context, behavioral processes and outcomes. It is not technology use or user adoption per se that matters as the outcome of interest, but rather how extensively the innovation is used and how deeply the firm's use of the technology alters 4793

3 processes, structures, and organizational culture. Researchers have generally referred to this notion as the innovation's degree of assimilation into the organization, or assimilation stage [15, 18]. Rogers [17] set a five-stage model of innovation adoption and implementation in organizations. It is roughly consists of initiation and implementation. While Rogers model was the first process model of organization adoption and implementation, there have been many other researches. Thompson [19] has seen innovation as generation, acceptance, and implementation of new ideas, processes, products or services. Innovation therefore implies the capacity to change or adapt. Meyer and Goes [18] examined the assimilation of medical innovations into organizations. He has conceptualized as nine-step process but seen it as a process unfolding a series of decisions to evaluate, adopt, and implement new technologies. Fichman and Kemerer [20] had seen many IT innovations involve a two-part adoption decision process. First step is a formal decision to make the innovation available to organization. Then first step is followed by local decisions about whether to actually use the innovation, and how [21]. Examples of other two-step decision process include software development tools [20], work group support technologies [22], and new communication technologies [23]. For this kind of model we should pay attention to the term adoption. When should we consider an organization to have really adopted an innovation system? Depending on which definition is used, a vastly different conclusion may be drawn [20]. Likewise what does it mean by we use these innovation technology? So rather than including vague definition, 3 stage assimilation model seems to be most generalized version of assimilation study and it would be deployed for the research Pre & Post Adoption Stages. The process of adoption starts from a firm s initial awareness and evaluation of the innovation. According to Rogers [17], initiation includes agenda-setting (problem identification) and matching (fitting an innovation to a predefined problem). Kwon and Zmud [24] proposed a stage model of IT implementation activities. According to them, Initiation of a process is active and/or passive scanning of organizational problems/opportunities and IT solutions are undertaken. The past literature on IT suggests that the potential of IT to enhance a firm s performance in value chain activities is a significant motivation for the firm to adopt IT [8]. Applying those views on pre-stage of IT adoption, we define first stage as initiation. It is the first stage of big data analysis adoption, stage in which firms evaluate the potential benefits of big data analysis to enhance business processes in value chain activities. This definition referred to the Zhu, Kraemer and Xu [8] s definition on e-business initiation. The following stage is adoption. Zhu, Kraemer and Xu [8] defines adoption as making the decision to use the internet for value chain activities. Similarly we define second stage as adoption. It is defined as stage where firms decide to adopt big data analysis related tools and accept those tools for decision making in value chain activities. Yet adoption does not always result in widespread usage of the technology by a firm [8]. Assimilation theories suggest that most information technologies exhibit an assimilation gap, i.e., their wide-spread usage tends to lag behind their adoption [20]. According to Rogers [17], last stage of innovation adoption is called implementation stage, and it includes making changes to both the organization and the innovation to exploit the innovation: redefining / restructuring, clarifying, and routinizing. A firm might decide to adopt new technology but usually firm and its members are lack of knowledge in using them. This often leads to misalignments occur between the new technology and the use environment [20]. According to Fichman and Kemerer [15], 42% of firms adopted computer-aided software engineering, but only 7% of them had achieved widespread deployment. Therefore, lessons learned about the assimilation of prior information technologies adoption, this could be extended toward understanding how firms promote the assimilation of big data analytics. Assimilation is third stage defined as, stage where big data analysis is integrated into the decision making process of the firm. Big data analysis should be used widely in the value chain activities. Assimilation is treated as important dimension of information systems success in the past literature [25]. In this research initiation, adoption, assimilation are set as three stage model for big data analysis adoption, and these have set its root on the past literatures. 3. Theoretical development 3.1. The Conceptual Model Integration of TOE framework and assimilation model provides good theoretical foundations for examining the antecedents and consequences of big data analysis. The former helps the categorization of resources, whereas the latter provides the theoretical rationale for linking them to adoption and implementation stages. Out of many assimilation 4794

4 models, we propose 3 stage models for following reasons (figure1): <Figure 1> Conceptual Model 3.2. Secondary attributes of innovation Characteristics of an IT have been frequently used in the adoption-related literatures. Rogers [17] viewed adoption of IT is related to the attributes of the innovations as perceived by potential adopters. Relative advantage, compatibility, complexity, and observability were suggested as five attributes that influence organizational adoption of IT. Based on these basic characteristics of Rogers and considering the special context of big data analysis adoption, 6 independent variables are selected: Perceived direct benefit, Perceived indirect benefit, Perceived financial readiness, Perceived IS Competence, Perceived Industrial Pressure and Perceived governmental regulations. The most differentiated part about this research is that it uses perceived variables which are second attribute of innovation Hypotheses Technological Context. Two factors are specified within the technological context perceived direct benefit and perceived indirect benefit. Perceived benefit refer to the level of recognition of the relative advantage that a technology can provide to the organization [17]. The variable used in this study, closely correspond to the term relative advantage. Among five characteristics of innovation, relative advantage of a new IT has been found to be one of the best predictors of the adoption rate of an innovation [26]. The perceptions of an innovation affect firm s evaluation of and propensity to adopt a new product [17]. Anderson, Narus and Narayandas [27] sees perceived benefits of adopting a new technology should exceed that of alternatives, if organizations are to consider adopting. In some literatures, perceived benefits are divided into direct benefits and indirect benefits. In the context of EDI and RFID, numerous direct and indirect benefits have been found [10, 26, 28]. Direct benefits refer to operational benefits, i.e. improvements made to the internal functioning of the organization apparent in everyday activities [26]. This definition is derived from research on EDI but it still makes sense in big data context so we use it. They include improving business process efficiencies, reducing process time and costs, etc. Indirect benefits refer to strategic benefits, i.e. development of corporate strategies through the building of external relationship with customers, partners and competitors. Examples include improving competitive advantage, images, marketing, sales, customer relationships, partner relationships, etc. This kind of distinction still makes sense in the context of big data analysis. As it is defined above, big data analysis has multi-purposes that could differ according to settings of each acceptance. So arguments on the above lead to the following hypotheses Hypothesis 1. Perceived Direct benefit is positively related to big data analysis initiation, adoption, and assimilation. Hypothesis 2. Perceived Indirect benefit is positively related to big data analysis initiation, adoption, and assimilation Organizational Context. Regardless of how great the benefits are, adoption stage is meaningless to the firm if they cannot be achieved due to lack of readiness. Readiness of an organization involves financial and technological capabilities. Kwon and Zmud [24] claimed that successful IS implementation occurs when sufficient organizational resources are directed toward motivation, then toward sustaining the implementation effort. We suggest perceived financial readiness and perceived IS Competence as organizational context variables. Financial readiness was used as important variable in many literatures and here we adopt the definition that was done by Iacovou, Benbasat and Dexter [11]. It refers to the financial resources available to pay for new technology innovation costs, for implementation of any subsequent enhancements, and ongoing expenses during usage. IS competence is defined as level of sophistication of IT usage and IT management in an organization [11]. These variables have been widely used for adoption context literatures as various similar concepts such as, financial resources, technology readiness [29], 4795

5 technology competence [7, 9], technology knowledge [10], perceived barriers [30]. Costs and technical experience have been identified as two of the most important factors that hinder IT growth especially in small organizations [31]. Still this applies same to the context of large firms and adoption of big data analysis. As argued in the previous section we use secondary characteristics since primary characteristics could lead to the inconsistency of findings. What appear to be costly to an adopter could be inexpensive to another. That is, one firm may perceive itself with high financial readiness, but another firm might perceive itself with low readiness [26]. Arguments on the above lead to the following hypotheses. Hypothesis3. Low perceived financial readiness is negatively related to big data analysis adoption and assimilation. Hypothesis4. Perceived IS Competence is positively related to big data analysis initiation, adoption, and assimilation Environmental Context. In many cases, a company may adopt a technology affected by business partners or its competitors. Decision might nothing to do with the technology and organization perspective [26]. A firm may feel pressure from both business partners and/or competitors and altogether forms industrial pressure. Industrial pressure is defined as the degree that the firm is affected by competitors and partners in the market. There could be two cases, which a firm might feel to adopt innovation by industrial pressure. First is if its business partners request or recommend it to do so. Second is when a firm sees more and more companies in the industry adoption the technology and therefore feels the need to adopt [26]. One more environmental context that affects big data analysis is government policies. In the past literatures, EDI systems had governmental pressures. The government has required that all import and export documents to be submitted through the EDI system by a specific deadline. In the case of big data, there are some privacy issues regarding collecting and managing personal data. Even though there is no apparent policies inhibiting such activities, still there are concerns going through governmental institutes so, we added the variable. Following the arguments above, two more hypotheses are proposed. Hypothesis5. Perceived Industrial pressure is positively related to big data analysis initiation, adoption, and assimilation. Hypothesis6. Perceived Government pressure is negatively related to big data analysis initiation, adoption, and assimilation. 4. Research Methods 4.1. Data Both case-based study and survey-based study approaches could be used in this research. Case study method can provide richer description of the issue but surveys can provide a basis for generalizing, allow for replicability, and permit some degree of statistical power [32]. The survey method was chosen for this research secure generalization and easy replication. Since organizations but not individuals adopt big data analysis, the unit of analysis for the study was at the organizational level. Subjects for this study were required to be top management level both IS manager and non-is managers. One might suspect that IS and non-is managers might tend to have different perceptions about IS usage [7], but Zhu, Kraemer and Xu [8] used Kolmogorov-Smirnov test to compare sample distributions of two groups and found that two independent groups do not differ statistically. Subjects were drawn from a list of top management team or IS managers. The list contained 450 organizations which have operations in Korea. An of purpose about this study, together with online questionnaire link, was sent to the senior executives of these organizations. Total 73 responses were received within 2 weeks, with a response rate of 16.2%. Among them, 15 responses were excluded because of incomplete responses or because the response time took too long (over a day) while survey took 15minutes in average for others. Among the remaining 58 respondents, 24(41.4%) were adopters and 34(58.6%) were non-adopters. Survey was conducted to the firms in four major industries (information, financial services, retail/wholesale/manufacturing). The sampling was a stratified sample by industry and firm size selected randomly within each category to minimize bias. The final data set contains 58 respondents. We examined common method bias and the result suggested no significant common method bias in our data set. [Table 1] Data Description Category Number Percentage adopter group Adopter non-adopter number of employees in the firm < , < , < , < , <

6 Instrument development and operationalization of factor To operationalize the constructs in the model, direct use of instruments in previous studies is not possible since there are few studies conducted in the context of big data analysis adoption. So items used were specifically developed of modified for this study based on the literature in a variety of sources Dependent Variables. Items used in this study to measure dependent variable were developed based on the Fichman and Kemerer [15] s research. They used 6-stage model for measuring assimilation of SPI (Software Process Innovations). Those were: 1)awareness, 2)interest, 3)evaluation/trial, 4) commitment, 5)limited deployment, 6)general deployment. In our research, there are three dependent variables. Initiation is defined as stage in which firms evaluate the potential benefits of big data analysis to enhance business processes in value chain activities. 1)Awareness and 2)Interest stage in the Fichman s model could be categorized as initiation stage the first stage before adoption. Next, adoption is defined as stage where firms decide to adopt big data analysis related tools and accept those tools for decision making in value chain activities. 3)evaluation/trial and 4)commitment could be categorized as second stage for our research. Lastly assimilation stage is where big data analysis is integrated into the decision making process of the firm. 5)limited deployment and 6)general deployment are categorized to be last adoption stage(assimilation) for our model. After modifying items to appropriately reflect the big data context, these items were applied to represent initiation, adoption, assimilation stages Independent Variables. Perceived direct and indirect benefits were derived from Iacovou, Benbasat and Dexter [11] and Kuan and Chau [26]. Respondents were asked to give their level of agreement or disagreement on potential direct/indirect benefits of adopting big data analysis in their firms. 7-point Likert-type scale was used, from 1 (strongly disagree) to 7 (strongly agree). Three items were used to measure direct benefits and six items were used to measure indirect benefits. Perceived financial readiness was measured by three items based on Kuan and Chau [26] s measure on financial costs. Items cover set-up, training, and operating costs of big data analysis. As perceived direct/indirect benefits, 7-point Likert-type scale was used. Perceived IS competence was operationalized with three items [26] that measures organization s IT performance including expertise and experience in providing big data supports and other general IT. 7- point Likert-type scale was used as above. Perceived industry pressure was measured by seven items [26] that were used to evaluate degree of influence exerted by business partners and competitors on the adoption decision. 7-point Likerttype scale is used. Perceived government regulation was measured by one item to evaluate the governmental pressure, including the security issue related to big data. 7- point Likert-type scale is used. 5. Data Analysis and Results 5.1. Results of the Measurement Model Structural Equation Modeling (SEM) could be analyzed by various tools. PLS is applicable to small samples in estimation as well as testing and appears to converge quickly even for large models with many variables and constructs [33]. It can be said that PLS trades parameter efficiency for prediction accuracy, simplicity, and fewer assumptions. Since this research contains survey participants under 200, we used PLS as an analyzing tool. According to Gefen, Straub and Boudreau [34], at least 10 times the number of items in the most complex construct is required as minimal sample size. The most complex construct is perceived industrial pressure for our case which contains seven items, and net sample size is 58 so it could be considered just as appropriate sample size In this study, in order to confirm whether the measurement model is appropriately used to its purpose, convergent validity and discriminant validity analysis has been performed. Also internal consistency reliability and construct reliability analysis were performed to secure reliability. Convergent validity assesses the consistency across multiple constructs. A successful evaluation of convergent validity shows that test of a concept is highly correlated with other tests designed to measure theoretically similar concepts. In this study, convergent validity is assessed by factor loading, composite reliability and average variance extracted (AVE). Common rule of thumb suggests that the factor loading should exceed.7. Composite reliability should be equal to or greater than.7 and AVE should be greater than

7 Internal consistency is examined using the composite scale reliability index and cronbach s alpha. Fornell and Larcker [35] recommended using a criterion cut-off of.7 or higher for both composite reliability and cronbach s alpha. Discriminant validity indicates the difference between one construct and another in the same model. To assess discriminate validity, AVE is compared to the squared correlation between two constructs. According to Fornell and Larcker [35], the level of square root of AVE should be greater than the correlations involving the constructs. That is, the average variance shared between a construct and its measures should be greater than the variance shared between the construct and other constructs in the model. [Table 2] Composite Reliability, Cronbach s alpha and AVE Variable Composite Cronbach's Reliability alpha AVE (1) Perceived Direct Benefit (2) Perceived Indirect Benefit (3) Perceived Financial Readiness (4) Perceived IS Competence (5) Perceived Industrial Pressure (6) Initialization (7) Adoption (8) Assimilation Every composite reliability and cronbach s alpha value is over.7 so every measurement item is reliable. Also every latent variables AVE is over.5 and every factor loading value is over 0.6 so convergent validity is satisfied. Discriminant validity was assured by examining factor and cross factor loadings Results of the Structural Model In order to evaluate the significance of path coefficient, PLS bootstrapping was performed. The result of causality relationship between latent variables is illustrated in the following figure. From the result analysis, it could be said that Perceived Financial Readiness significantly affects Assimilation stage (t=3.897). Perceived IS Competence has significant influence on Initiation (t=3.027) and Assimilation stage (t=2.100). Also Perceived Industrial Pressure does influence Adoption stage significantly (t=2.2.09). Initiation stage was explained by 29.6%, Adoption stage by 55.7%, Assimilation stage by 55.3% from variables set in the model. In summary, perceived financial readiness has negative effect on assimilation stage and perceived IS competence had positive effect on Initiation and Assimilation. Lastly, perceived industrial pressure had positive influence on Adoption stage. <figure2> Results of the Structural model 6. Discussion Within TOE framework, perceived IS competence emerges as the most expansive factor for big data analysis adopting process, while perceived financial readiness and perceived industrial pressure also significantly contribute to the process. Three factors within the TOE framework are significant variables affecting the adoption process. Among these, IS competence appears to be the most expansive factor, as indicated by figure, it influence on initiation and assimilation process. Also, among the significant factors, IS Competence s influence on initiation was the strongest ( , t=3.027). This suggests that firms with stronger IS competence. 6.1 Technological Context. Perceived direct and indirect benefits both were not found to be significant. Rogers argued that adoption of innovations is related to the attributes of the innovations as perceived by potential adopters. Especially, many innovation and IT adoption literatures have been pointed out perceived benefit as a critical factor. So we need plausible explanation for this result. First plausible explanation for the insignificance of perceived direct/indirect benefit is that the since advantage of big data analysis is widely known through various media, there might have been no significance difference between adopter and non-adopter. Nonadopters could already have vague perception on advantage of big data analysis. Second explanation is that expectations have fallen short for the adopters. Similar cases have been found 4798

8 in other studies. According to Fearon and Philip [36] s research on adoption of EDI, there was in general a shortfall or negative gap experienced between expectations and perceptions of realized benefits of EDI throughout companies as a whole. If this is the case, benefits should meet the expectations of users or even exceed it [26]. Same explanation could be applied for this study. It was expected as big data analysis adopters should have more positive perceptions toward benefits they could harness by adopting the technology. Adopters might have had positive perceptions than non-adopters but since their expectation is high, perceptions could have fallen short. 6.2 Organizational Context. Past literatures suggest that perceived financial readiness were significantly distinguishing adopter firms from nonadopter firms. Adopter firms perceived financial cost as less as an obstacle than non-adopter firms did [26]. In this study it is found that, the variable affected last stage strongly, which is assimilation rather than adoption stage. The result is challenging conventional studies. Following the classification of innovation from Swanson, big data analysis is type3 innovation as mentioned above. Type3 innovation has its emphasis on broad assimilation compared to other type IS innovations. So when adopting big data analysis, it costs more in the assimilation stage than adoption stage. It is hard to say a firm has adopted big data analysis when their only behavior is purchase of some tools. Companies were also asked to evaluate the performance of their internal technical competence in firms of general IT support and big data analysis. We defined this variable as IS competence and it had most expansive effect on the overall process of adoption. It affected both initiation and assimilation stages. This implies two things. First, compared with adopter firms, non-adopter firms perceived themselves not to have the necessary technical resources to support big data analysis. External support might help big data analysis to diffuse among firms. Secondly, firms with IS competence tend to get more opportunity and information regarding big data and after they adopt the system, firms with bigger IS competence has motivation to integrate big data in business decision making. From the result, it could be viewed that initiation and assimilation stage of adoption is associated more with internal organizational resources (e.g., perceived IS competence, perceived financial readiness). In order for firms to integrate the innovation in their routine decision making, Firm needs enough financial resources and personnel related to big data or broadly information system. It could be viewed that, when firms adopt some new innovation (big data analysis for our study) external benefit-cost is important factor while taking interest and assimilating it in the organization internal resources play critical role. 6.3 Environmental Context. Previous literatures have shown that competitive pressure is a critical factor driving firms to adopt a new technology in order to avoid competitive pressure [11]. It is said that especially competitive pressure increases a firm s incentives to seek new technology innovations so as to maintain and enhance a competitive edge [37]. Our study shows that industrial pressure including competition, firms tend to adopt big data analysis more aggressively consistent with the past literatures. Yet, its effect on the pre & post-adoption stage is different. Rather pre and post stages were affected by other variables and perceived industrial pressure turns out to be an insignificant factor. The extent to which big data analysis is integrated into business process tends to be less tied to industrial pressure. This seems to suggest that big data analysis s integration into the general process of business originates more from internal organizational resources financial readiness, IS competence than from external pressure. In some past literatures it is said that too much competition is not necessarily good for technology assimilation and actually negatively affects the post stage. So it is partly challenging the conventional studies about competition and innovation diffusion [37]. In summary this finding demonstrates the different role of competitive pressure in three stages of big data analysis adoption. 7. Conclusion 7.1. Implications and Contributions This study provides some managerial implications for firm s managers and policy makers. Firstly, top managers should put a high priority on IS competence to ensure smooth implementation and assimilation of big data analysis. It is found that IS competition is the most expansive factor in overall adoption stages. Secondly, this study suggests that managers need to adjust management practices at different assimilation stages. For instance, at the initiation stage, IS competence is most important factor for firm to get aware of new innovation and technologies. When at adoption stage, industrial pressure plays an important role. When it comes to assimilation stage, IS competence and financial 4799

9 readiness plays critical role. So, managers could now anticipate how well the new technology, especially big data analysis, could be implemented on their organization based on the each variables stated above. Finally, for policy makers, in order to diffuse big data analysis among firms external support is important. The technology s performance nearly always lags the promotion of new technology, so it is more important that vendors or governmental assist user companies with change management. When firms perceive difficulties in transforming value chain activities through big data analysis, government and vendors should provide more technical support, training, and information. Many firms increasingly seek to improve their performance in value chain activities by using big data analysis. So it is important to understand factors that affect the adoption and implementation. This study represents an early attempt to examine a big data analysis adoption model. Drawing upon theoretical perspectives on the contexts of innovation diffusion, we developed an integrative model to examine factors affecting three stages of big data analysis adoption. The model was grounded on TOE. Our studies have some theoretical contribution to the literatures on adoption and assimilation. First, while many TOE frameworks focused on only adoption as a unique dependent variable, we integrated this with three stage assimilation model. We extend our previous work on both literatures by emphasizing the process of adoption and by developing an integrative conceptual model. External validity of the integrative model tested in this work is enhanced by other related studies. Also they are based on different theories and topics which is cutting edge technology-big data analysis. Most of the existing studies in the literature examined in innovation diffusion are adoption versus nonadoption. The process oriented approach used in this research allows us to examine differential effects of TOE factors along the three stages of the diffusion process. This conceptual model can be used as a theoretical framework for studying other type3 innovations. Secondly, the independent variable used in the research was secondary characteristics. We believe the focus on secondary characteristics would facilitate a more general understanding in big data analysis adoption in agreement with Tornatzky, Fleischer and Chakrabarti [6]. Thirdly, the study confirms the usefulness of the TOE framework for studying adoption of technological innovations. The framework has shown that same factors may play different roles at different adoption stages. As the adoption of a variety of technological innovations can be viewed from this process-oriented perspective, we believe that our results represent a theoretical advancement to the literature Limitations and Future Research First, since our data set is cross-sectional, we could only explain associations, not causality. Longitudinal analysis could show more dynamic context such as evolution of technology adoption and assimilation. The phenomenon we are studying is changing while in the very process of studying it [8]. Since this research is only cornerstone of behavioral research regarding big data analysis, future research should compare data collected at different periods and gain more insights about adopting & assimilation process in a various environment. Second, single responses from each business units were collected, thus leading to the possibility of response bias. In small businesses the decision making is done by usually single person (owner/manager), but larger firm s decision making is done by group of senior management. So in order to avoid bias future research should contain more than multiple responses per each firm. It is said that IS and non-is managers are found to have no significant differences in their responses to both technology and business questions [8]. In the same context, in order to further achieve validity future research should collect data from both an IS manager and business manager. Thirdly, although construct reliability and validity were empirically tested in our research, the measurement instruments should be further tested to determine the external validity of the results. The items used in the survey was derived from other IS contexts so developing solid instruments for studying big data should be on-going process of development, testing, and refinement. Fourthly, some variables are defined abstractly, so that it could be explained in multiple ways by respondents. For example respondents criteria on IS competence could be different according to their experience and environments. More concrete definition is required in future study. Finally, future research should contain many other independent variables according to the contexts. As an example, research on small firms should contain more variables such as CEO s characteristics. Also according to countries and industry the result could differ. This research provides basis for adoption and assimilation model for big data analysis, so future research should be done by various contexts. 4800

10 10. References [1] Swanson, E.B., "Information Systems Innovation among Organizations", Management Science, 40(9), 1994, pp [2] Chen, H., Chiang, R.H., and Storey, V.C., "Business Intelligence and Analytics: From Big Data to Big Impact", MIS quarterly, 36(4), 2012, [3] Boyd, D., and Crawford, K., "Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon", Information, Communication & Society, 15(5), 2012, pp [4] Elbashir, M.Z., Collier, P.A., and Davern, M.J., "Measuring the Effects of Business Intelligence Systems: The Relationship between Business Process and Organizational Performance", International Journal of Accounting Information Systems, 9(3), 2008, pp [5] Hannula, M., and Pirttimäki, V., "Business Intelligence Empirical Study on the Top 50 Finnish Companies", Journal of American Academy of Business, 2(2), 2003, pp [6] Tornatzky, L.G., Fleischer, M., and Chakrabarti, A.K., "Processes of Technological Innovation", 1990, [7] Zhu, K., and Kraemer, K.L., "Post-Adoption Variations in Usage and Value of E-Business by Organizations: Cross- Country Evidence from the Retail Industry", Information Systems Research, 16(1), 2005, pp [8] Zhu, K., Kraemer, K.L., and Xu, S., "The Process of Innovation Assimilation by Firms in Different Countries: A Technology Diffusion Perspective on E-Business", Management Science, 52(10), 2006, pp [9] Zhu, K., Kraemer, K., and Xu, S., "Electronic Business Adoption by European Firms: A Cross-Country Assessment of the Facilitators and Inhibitors", European Journal of Information Systems, 12(4), 2003, pp [10] Lee, C.-P., and Shim, J.P., "An Exploratory Study of Radio Frequency Identification (Rfid) Adoption in the Healthcare Industry", European Journal of Information Systems, 16(6), 2007, pp [11] Iacovou, C.L., Benbasat, I., and Dexter, A.S., "Electronic Data Interchange and Small Organizations: Adoption and Impact of Technology", MIS quarterly, 1995, pp [12] Thong, J.Y., "An Integrated Model of Information Systems Adoption in Small Businesses", Journal of Management Information Systems, 15(4), 1999, pp [13] Chatterjee, D., Grewal, R., and Sambamurthy, V., "Shaping up for E-Commerce: Institutional Enablers of the Organizational Assimilation of Web Technologies", MIS quarterly, 2002, pp [14] Cooper, R.B., and Zmud, R.W., "Information Technology Implementation Research: A Technological Diffusion Approach", Management Science, 36(2), 1990, pp [15] Fichman, R.G., and Kemerer, C.F., "The Assimilation of Software Process Innovations: An Organizational Learning Perspective", Management Science, 43(10), 1997, pp [16] Tornatzky, L.G., and Klein, K.J., "Innovation Characteristics and Innovation Adoption-Implementation: A Meta-Analysis of Findings", Engineering Management, IEEE Transactions on1), 1982, pp [17] Rogers, E.M., Diffusion of Innovations, Simon and Schuster, [18] Meyer, A.D., and Goes, J.B., "Organizational Assimilation of Innovations: A Multilevel Contextual Analysis", Academy of Management Journal, 31(4), 1988, pp [19] Thompson, V.A., "Bureaucracy and Innovation", Administrative science quarterly, 1965, pp [20] Fichman, R.G., and Kemerer, C.F., "The Illusory Diffusion of Innovation: An Examination of Assimilation Gaps", Information Systems Research, 10(3), 1999, pp [21] Leonard-Barton, D., and Deschamps, I., "Managerial Influence in the Implementation of New Technology", Management Science, 34(10), 1988, pp [22] Desanctis, G., and Poole, M.S., "Capturing the Complexity in Advanced Technology Use: Adaptive Structuration Theory", Organization Science, 1994, pp [23] Kraut, R.E., Rice, R.E., Cool, C., and Fish, R.S., "Varieties of Social Influence: The Role of Utility and Norms in the Success of a New Communication Medium", Organization Science, 9(4), 1998, pp [24] Kwon, T.H., and Zmud, R.W., "Unifying the Fragmented Models of Information Systems Implementation", in (Editor, 'ed.'^'eds.'): Book Unifying the Fragmented Models of Information Systems Implementation, John Wiley & Sons, Inc., 1987, pp [25] Delone, W.H., and Mclean, E.R., "Information Systems Success: The Quest for the Dependent Variable", Information Systems Research, 3(1), 1992, pp [26] Kuan, K.K., and Chau, P.Y., "A Perception-Based Model for Edi Adoption in Small Businesses Using a Technology Organization Environment Framework", Information & Management, 38(8), 2001, pp [27] Anderson, J.C., Narus, J.A., and Narayandas, D., Business Market Management: Understanding, Creating, and Delivering Value, Prentice Hall Upper Saddle River, NJ, [28] Smith, A.D., "Exploring Radio Frequency Identification Technology and Its Impact on Business Systems", Information Management & Computer Security, 13(1), 2005, pp [29] Zhu, K., "Information Transparency of Business-to- Business Electronic Markets: A Game-Theoretic Analysis", Management Science, 50(5), 2004, pp [30] Chau, P.Y., and Tam, K.Y., "Factors Affecting the Adoption of Open Systems: An Exploratory Study", MIS quarterly, 21(1), 1997, [31] Cragg, P.B., and King, M., "Small-Firm Computing: Motivators and Inhibitors", MIS quarterly, 17(1), 1993, pp [32] Bouchard, L., "Decision Criteria in the Adoption of Edi", 1993, [33] Fornell, C., and Bookstein, F.L., "Two Structural Equation Models: Lisrel and Pls Applied to Consumer Exit- Voice Theory", Journal of Marketing Research (JMR), 19(4), 1982, [34] Gefen, D., Straub, D.W., and Boudreau, M.-C., "And Regression: Guidelines for Research Practice", 2000, [35] Fornell, C., and Larcker, D.F., "Evaluating Structural Equation Models with Unobservable Variables and Measurement Error", Journal of Marketing Research (JMR), 18(1), 1981, [36] Fearon, C., and Philip, G., "Self Assessment as a Means of Measuring Strategic and Operational Benefits from Edi: The Development of a Conceptual Framework", European Journal of Information Systems, 7(1), 1998, pp [37] Kamien, M.I., Market Structure and Innovation, Cambridge University Press,