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Benefits of Technological Innovation in Business


Advances in information systems and technology (IS/IT) are re1garded as major sources of improvement in the competitive position of firms and industries (Mitropoulos and Tatum, 2000). However, the benefits from technological advances depend on the extent to which these technologies are utilized. Indeed, information is becoming critically important in achieving strategic competitive advantage, particularly in today’s competitive environment (Claudia, 2005). This proclamation has led organizations to adopt the most advanced enterprise technology to innovate for a change because organizations that maximize and leverage their information assets have a strategic advantage over their competitors (Claudia, 2005). The ability to speed up making decisions, improving operations performance, managing customer profitability as well as increasing the level of control to management are the core benefits to be considered by decision makers when implementing IT/IS.

The rapid emergence of enterprise systems has made applications such as enterprise resource technology (ERP) to be among the most popular technologies used in the industries. Despite its importance to decision makers and also researchers in discovering how the emergence of enterprise systems contributes to organizational performance, there is uncertainty about IT payoff and accountants’ involvement in determining business and information strategy of an organization. The typical judgmental by organizations on investments of IS/IT is always to battle competition by improving productivity, profitability and quality of operations. Hence, to understand the organizations’ decisions to innovate always remain as the critical topic of discussion among IS/IT scholars particularly when it relates to the perceptions of accountants as the internal provider of information. Historically, organizational innovations were distinguished process from product innovations (Zmud, 1982; Robey, 1986; Swanson, 1994) and further differentiated between administrative and technological process innovations (Robey, 1986; Swanson 1994).

Accountants play a significant role as the internal provider of information for business operations and for competitive positions in the market. Accountants are also described as the gatekeeper of the financial markets (Wallman, 1995). Without information expertise of accountants, businesses would not be able to evaluate their cost and profit position, gauge product or business unit performance or to plan for future financial success (Brecht and Martin, 1996). Traditionally, accountants were trapped on standard financial reporting or financial-related information and having historical orientation (Mia, 1993) to support management in making decisions. However, as information technologies grow more advanced and competitive pressure for innovation increased, the responsibility of accountants to furnish decision makers with valuable information in making intelligent decision becomes very crucial. Therefore, accountants must quickly response to this evolving information environment to make sure on the efficient business, information strategy and competitive positions in the industry

Most of prior researches have extensively addressed and explained the phenomenon about IS/IT innovation (Rogers, 1983), the perspective of users acceptance of new technology (Davis, 1986) and its impact on organizational competitive advantage (Barney, 1991). Indeed, there are various literatures on IS/IT acceptance among researchers (Gallivan, 2001; Rogers, 2003; Swanson and Ramiller, 2004; Zhu, Kraemer and Xu, 2006) and IT-payoff (Brynjolfsson, 1996; Bharadwaj, Bharadwaj and Konsynski, 2000; Devaraj and Kohli, 2000). However, interdisciplinary research between two different schools of thought that discussed issues on information technology and accounting has been given less attention to date. Hence, this research is intended to discover, understand and explain the basis for enterprise systems innovation and accountants’ involvement in determining the information and business strategy of an organization. In this case, a grounded theory approach is adopted with the aim to explore the opportunities for accountants to contribute on enterprise systems innovation that leads to the following research questions:

  1. What drives organizations innovate for the latest technology?
  2. How does it give impact on competitive position of an organization?


The evolution of enterprise systems began in the 1950s as inventory control systems (Yen, Chou and Chang, 2001), where the manufacturing systems’ main focus was to handle inventory control in order to replace the traditional inventory concept. Later, bookkeeping, invoicing and reordering have been introduced to support business operations and management (Yen et al., 2001). Material requirement planning (MRP) was then developed in the 1960s with an objective to translate the master production schedule into requirements of raw material planning and procurement. Subsequently, manufacturing resource planning (MRPII) has evolved into a more advanced system with the objective to optimize the production process and distribution management (Yen et al. 2001). It has been extended to include areas such as corporate finance, personnel management, engineering process and business process management.

The robust development of MRP II has encouraged IT experts to develop more advanced technologies such as enterprise resource planning (ERP), supply-chain management (SCM) and customer relationship management (CRM) over some period of time to leverage information about strategic enterprise management, improving operations performance, managing customer profitability, human resource and supply chain information and improving direct/indirect business process (William and William, 2003). These technologies are more sophisticated and efficient in handling multiple business units such as sales and operations planning, inventory/materials management, manufacturing, purchasing, order processing, accounting and finance, human resources, customer relationship management, supply chain management and more. However, due to some limitations particularly in analytical decision-making, these systems could not facilitate the decision support function (Chou et al., 2005).

In the 1990s, much adoption of IS/IT was focused on the enterprise systems. The benefits over decisions to adopt IS/IT are basically on cost reduction, transactional efficiency, internal process management, back and front end process automation and transactional status visibility. As businesses continue to use enterprise systems for a growing number of functions, they face the challenge of processing and analyzing huge amount of data into intelligent decision-making. Although current enterprise systems could integrate business transactions data for organizational planning, essentially, it would not support management particularly on analytical and decision support process. The changing of business requirements, new technologies and the software vendors’ development capabilities has enforced the enterprise applications continue to emerge. The emergence of Business Intelligence (BI) tools in the early 2000s, where its main function is to extract valuable information from existing enterprise systems, is anticipated to improve organizational performance and competitive advantage (Davis, 2002) and with its capability in conveying intelligent decisions for decision makers (Buytendijk, 2001; Golfareelli and Cella, 2004). Hence, the relevant and suitability of enterprise systems innovation towards competitive position of a firm remain favourable topics of discussion between scholars as it reflects IT-payoff or return on investment of an organization.


The literature provides different definitions of innovations: Rogers (1976) defines innovation as an idea, practice or object perceived as new by an individual or other relevant unit of adoption which is communicated through certain channels over time among the members of a social system. Tornatzky and Klein (1982) define it as an idea, practice or material artifact perceived to be new by the relevant unit of adoption. Swanson (1994) defines information system innovation as innovation in the organizational application of digital computer and communications technologies. Swanson (1994) added that organizational innovation refers to the adoption of an idea or behavior that is new to the organization that is adopting it (Daft, 1978). It is further defined as the first or early use of an idea by one set of organizations with similar goals (Becker and Whisler, 1967, quoted by Daft, 1978).

Meanwhile, in the year 2000s scholars have defined information system innovation as: Gordon and Tarafdar (2007) describe that innovation process comprised of three broad stages: initiation, development and implementation (Damanpour, 1991; Utterback, 1971; Zmud, 1982). Initiation involves activities leading to an organization’s decision to adopt or attempt to adopt an innovation. Motivation could be poor financial or operational performance (Kanter, 1982; Tushman and O’Reilly, 1997), internal self-criticism combined with a strategic focus on proactive business innovation (Nonaka, 1988; Tushman and Nadler, 1986). Development involves design and development of product and process innovations planned in the initiation stage. This stage has activities such as idea generation and problem solving (Tushman and O’Reilly, 1997), rapid information process and fast decision making (Eisenhardt and Tabrizi, 1995), new information is acquired from competitors (Tushman and O’Reilly, 1997) and customers (Drucker, 1998) and connected with existing knowledge (Galbraith, 1982) to create new product/processes. Implementation involves activities surrounding the adoption and assimilation of innovations designed and developed during the ‘development’ stage. Process and product redesign leads to changes in different processes and control systems (Davenport, 1993), effective and reasonably strict control systems are required for efficiently accomplishing the administration and co-ordination activities necessary for implementation of the innovation (Galbraith, 1982).

Innovating with IT, according to Swanson and Ramiller (2004), is a journey that involves four core processes: comprehension, adoption, implementation, and assimilation. First, organizations collect and interpret information from their environments about the existence and basic idea of an IT innovation. Second, this comprehension effort informs organizations’ decisions on whether to adopt the innovation, plus the articulation of supporting rationales. Third, where adoption is actually pursued, the innovation is deployed—hardware and software are installed, business processes are changed, users are trained, and so on. Fourth, in due course the innovation becomes assimilated into the routines of organizational work systems. Wang and Ramiller (2009) further define IT innovation as an information technology perceived as new by the adopting organization (Rogers 2003; Swanson 1994). Their perspective on innovation is oriented towards adopters and organizations innovate with IT by applying new IT to their business processes. Therefore, in this research, enterprise systems innovation could be defined as enterprise systems that comprised an integrated planning and resource management system that coordinates information across all enterprise functions (Bendoly et al, 2008) and the capability of the systems to provide valuable information for managements in determining the business and information strategy of an organization.

In recent years, there are a number of researches that examine the organizational adoption of IS/IT, IT payoff and its impact on organizational performance. IT adoption contributes to various competing models that have been tested in several industries (either services or non-services) and are different in terms of methodological approach, conceptual models and constructs, such as a research model on user acceptance of citation database interface (Lin, 2009), mobile wireless (Kim, 2009; Qi, 2009), internet banking (Lee, 2009a), online trading (Lee, 2009b) and more. Indeed, there are various literatures on IT adoption and acceptance among researchers (Gallivan, 2001; Rogers, 2003; Swanson, 2004; Zhu, Kraemer and Xu, 2006, Qi et al, 2009; Kim and Garrison, 2009) and IT-payoff (Brynjolfsson, 1996; Bharadwaj et. al, 2000; Devaraj et. al, 2000). Within this broad area of investigation, there are several streams of research. One stream of research focuses on individual acceptance of technology by using behavioural intention as a dependent variable (e.g Davis, 1989; Bhattacherjee, 2001; Bhacttacherjee and Premkumar, 2004; Zhu, 2006). The other streams have focused on implementation success at the organizational levels (Grover, 1998; Karahanna, 1999) and task technology fit (Goodhue and Thompson, 1995). However, due to the nature of the research designs employed, these streams of research have not attributed the effect of usefulness of information from enterprise systems innovation and its impact on organizational performance.

Furthermore, scholars have documented many studies that examine the relationship between investments in technology and its payoff in terms of enhanced organizational performance (Brynjolfsson and Yang, 1996; Kohli and Devaraj, 2003). There is evidence that there are significant differences among studies in terms of the level of analyses, methodologies employed, variables and contexts examined. Many economic studies (Roach, 1987; Morrision and Berndt, 1991) observed a negative relationship between technology-related variables and performance. At the industry level, the results were mixed with some studies documenting a positive impact of technological investment (Kelley, 1994; Siegel and Griliches, 1992) while other studies by Berdnt and Morrison (1995) and Koski (1999) detect no significant advantage to IT investment. At a more detailed organizational level, Diewert and Smith (1994), Hitt and Brynjolfsson (1995) and Dewan and Min (1997) present results indicating a positive relationship between technology and performance.

In this research, information use is tightly related to the technology that provides access to such information. The limitations of the enterprise systems as well as resource constraints on managerial time devoted to information search such as accessing, understanding, transforming and consolidating the information would give the impact on how effectively information use can be converted into strategic results (Bendoly and Cotteleer, 2008). Indeed, IS/IT research concerned with how to design more useful IS for organization (Legris, Ingham and Collerette, 2003; Elbeltagi, 2005; Jeyaraj, Rottman and Lacity, 2006). However, a useful IS/IT is not one that is simply used by individuals or organizations or the one that possesses specific desirable characteristics (such as output information quality, functionality or interface structure). Rather a useful IS/IT is one which can and does support collective action through the nature of the relationship between technological attributes, individual users and organizationally situated tasks (Diez and McIntosh, 2008).

Consequently, many prior researchers have struggled to show the direct impact of IT with other disciplines such as accounting on organizational performance. However, several recent studies have shown that the fit between accounting and IT has significant impact on performance (Chan et al, 1991; Cragg et al, 2002) where firms that consider their IT strategy with business strategy perform better than those who do not. Raymond et al (1995) found that firms that align their organizational structure and IT structure also perform better than firms that do not. In another study, Bergeron et al. (2001) found that fit between strategic orientation, organizational structure, and strategic IT management had an impact on firm performance. The issues of matching information requirements and enterprise systems capabilities and also the impact of this matching on performance are important questions which are part of a general debate in accounting information system field (e.g. Galbraith, 1973; Tushman and Nadler, 1978; Van de Ven and Drazin, 1985). Accountants are the internal providers of information to decision makers and accountants must adapt to the competitive pressure and increase their ability to leverage information assets in order to contribute for more effectively to managerial decision making. Therefore, as IS/IT grows more advanced, accountants must react quickly to the changes and need to create and apply non-financial information to achieve organizational performance. Hence, this research will discover the impact of usefulness of information through enterprise system innovation and to investigate the accountants’ involvement in determining the information strategy of an organization.


The classification of this research is mainly a grounded theory approach as it seeks to understand and explain social phenomenon related to the involvements of accountants on enterprise system innovations in determining the business and information strategy of an organization. This research is not to predict as used by positivists or just to have a subjective explanation or interpretation, but this research is expected to come out with unique explanations that constitute to the theory building and/or to come out with a variation of existing theories for modification to be able to fit into the context of the phenomenon of interest. In order to discover the ontological and epistemological aspects of the social inquiry, the method used in this research is important to be realized. In this study, the epistemology adopted is interpretivism and the qualitative research methodology is used to generate explanations on the phenomenon under study.

Grounded theory was first developed by Glaser and Strauss (1967) and could be best defined as a qualitative research method that uses a systematic set of procedures to develop and inductively derive grounded theory about a phenomenon (Strauss & Corbin, 1990). In such a way, grounded theory is suggested to be inductive rather than deductive. Basically, the purpose of grounded theory is to organize many ideas from analysis of the data (Strauss, 1967) and to build a theory that is faithful to and justified the area under study (Strauss and Corbin, 1990). The theory developed is not necessarily intended to stand-alone but could be related to existing theories within a field and therefore it will strengthen the current understandings of the phenomena in question. Strauss (1967) summarized grounded theory procedures as the systematic analysis of documents, interview notes or field notes by continually coding and comparing data that produced a well-constructed theory. Hence, Strauss and Corbin (1994) noted that the major difference between this methodology and other approaches to qualitative research was its emphasis upon theory development.

Although the collaboration works between Glaser and Strauss have contributed to the development of grounded theory, they show some differences on the epistemological aspects between them (Glaser, 1978, 1992; Strauss, 1987; Strauss & Corbin, 1990), which have resulted in the ‘Straussian’ and ‘Glaserian’ models (Stern, 1994). The Glaserian approach on qualitative data analysis was said to have the preconceptions or positive perspective on doing grounded theory while Straussian approach has a realistic epistemology into empirical inquiry through grounded theory. Furthermore, Glaserian beliefs were to be more positivism about the objective and external reality, while Straussian beliefs were based on the assumption of having an unbiased position in collecting data and use certain technical procedures to ensure the participants express their own perception (Glaser, 1992; Strauss & Corbin, 1990). Based on these two beliefs of grounded theory, the author has chosen Straussian approach as the qualitative data analysis method in her research due to the following reasons: i) this research did not use comparative methods in the development and understanding of grounded theory as introduced by Glaser (2001); ii) to construct a theory by looking at the perceptions of the participants, analysis of the data and to understand what they tell or the participants realities; iii) Strauss views on human beings as the active agents in their lives and brought notions for human agency, emergent processes, social and subjective meanings, problem-solving and the open-ended study of action to grounded theory (Charmaz, 2007).

Moreover, qualitative approach adopted in this research also seeks answers to a question, uncovers social behavior, and understands the interaction between organizations and technology that produces findings which are not determined in advance. Qualitative enquiry examines data which are narrative and non-numeric that emphasize on the qualities of entities, on process and meanings that are not experimentally examined or measured in terms of quantity, amount, intensity or frequency (Denzin and Lincoln, 2005). Cassel and Symon (2004) cited that qualitative research is used when researchers would like to understand a circumstance in terms of how and why it occurs. The aim of qualitative methodology is to described and analyze the culture and behavior of humans and their groups from the point of view of those being studied and to collect and analyze data which is uncountable (Cassell and Symon, 2004). In this research, enterprise systems innovation is an emerging issue in the business environment. The unique characteristics of the system, for example, a system for data analysis and reporting that provides managers with better analytical and reporting functions which enable them to make intelligent decisions for strategic positioning should be discovered. In view of the above, interpretive research has gained increasing acceptance in the information technology research (Sahay, 1997; Klein and Myers, 1999) as it focuses on producing an understanding of the context of the information systems and the process whereby the information systems influence and is influenced by the context (Walsham, 1993). Therefore, the rationale for choosing the qualitative methodology and grounded theory approach in this research is again reflected to the purpose of the study.


In this research, grounded theory was developed through data obtained from case studies, involving two private sector companies in Klang Valley, Malaysia. The selection of companies were based on recent technologies adopted in the organizations such as SAP systems and these companies were classified as among the active users of the enterprise application systems. The purpose of using case study as a method of data collection is because the researcher would like to achieve deeper understanding on the process within and outside of the context. According to Yin (1994), data collection for case study may come in a variety of sources for examples documents, archival records, interviews, direct observation, participant-observation and physical artefacts and in-depth interviews are the most important source of case study information (Yin, 1994). The strength of an interview is that it focuses directly on the topic to be discovered (i.e the enterprise systems innovation and accountants involvement in determining the business and information strategy of an organization, as opposed to survey method).

Glaser (2001) stated that grounded theory is mainly used for qualitative research. However, when combining methods like grounded theory and case study as data collection method, the utmost care must be exercised to ensure that the norms of case study research do not distort true emergence for theory generation (Glaser, 1998). For example, Yin (1994) stated that theory development prior to the collection of any case study data is an essential step in doing case studies. Based on the statement addressed by Yin (1994), it contravenes from the principle of grounded theory whereby data collection and analysis as a procedure on theory development. Therefore, when combining grounded theory and case study as a way of collecting data, the methodology driving the investigation should be clearly specified.

In view of the above, grounded theory was used as an overall methodology to study data obtained from case studies and to drive data acquisition activities within the case study. Indeed, the reasons for using the grounded theory approach were consistent with the three main reasons suggested by Benbasat (1987) for using a case study strategy in information systems research as follows: i) The research can study information systems in a natural setting, learn the state of the art, and generate theories from practice; ii) The researcher can answer the questions that lead to an understanding of the nature and complexity of the processes taking place; and iii) It is an appropriate way to research a previously little studied area. For these reasons, seeking to generate theory grounded in case study data was a particularly appropriate strategy in this research.

Table 1 provides some detailed information about the cases. The interviews were held with Chief Financial Officer, Chief Technology Officer, Finance Manager, Accountants and Information Technology Officer. Interviewees were selected to ensure both varieties across disciplines and consistency across cases. They were also selected on the basis that each had an important role with respect to enterprise systems innovations and accountants’ involvement in making the implementation a success. Meanwhile, the sampling technique used in this research was purposeful sampling. Patton (1990) stated that a qualitative inquiry typically focuses in depth on relatively small samples and uses purposeful sampling, as opposed to quantitative methods that typically depend on larger samples selected randomly. Patton (1990) added that the logic and power of purposeful sampling, is that one can learn a great deal about issues of central importance to the purpose of the research. The unit of analysis in this research is the organizations and holistic (according to Yin 1994, holistic is a single unit of analysis). The selection of organizations as unit of analysis instead of individuals, dyads or groups is to ensure that the answers to research question will be achieved.

In this research, literal replication and multiple cases with holistic design were used to allow for more generalizability and transferability rather than the single case design. The reason of selecting literal replication was due to the researcher’s wishes to obtain as much information as possible in investigating the phenomenon of enterprise systems innovations and the accountants’ involvement in determining the business and information strategy of an organization until no new information emerges. The appropriate sample size for qualitative research was answered by ‘theoretical saturation’ (Glaser & Strauss, 1967; Strauss & Corbin, 1998). Theoretical saturation, according to Glaser & Strauss (1967) and Strauss & Corbin (1998), occurs when no new or relevant data seems to emerge regarding a category where the category is well developed in terms of its properties and dimensions demonstrating variation and the relationships among categories are well established and validated (Strauss & Corbin, 1998).

A schedule of interviewees is provided in Table II. In total, six interviews were undertaken. The interviews lasted about fourty five minutes to an hour on an average. Each interview was preceded by a brief explanation on the purpose of the research and the broad area of interest. The key instruments that were used for collecting evidence were open-ended questions and were asked in a naturalistic manner. These were designed to draw participants’ interpretations of their day to day actions as they affected and were affected by their perceptions on enterprise systems innovations and the accountants’ involvement in ensuring the business and information strategy of an organization. With the consent from the interviewees, all interviews were tape-recorded. Tape recording helps to prevent the researcher from being too occupied writing notes during the interview so that the researcher could concentrate on the issues discussed (Yin, 1994). Short notes during the interviews were taken and six interviews were transcribed.

Table I: Company Profiles

Location Industry No. of Employees
Company A Putrajaya, WP Property Developer, Hotel and Tourism
Company B Kuala Lumpur, WP Hotel and Tourism

Table II: Interviewee Details

Interviewee Designation Company
1. CFO1 Chief Financial Officer CA1
2. CTO1 Chief Technology Officer CA1
3. FM1 Finance Manager CA1
4. ACC1 Accountant CB1
5. ACC2 Accountant CB1
6. ITO1 Information Technology Officer CB1


Using Strauss and Corbin’s (1990) approach, data was analyzed through various stages of coding to produce an ordered data set which was integrated into a theory. The process of deriving the categories from the interviews was driven by the criteria of open, axial and selective coding (Strauss and Corbin, 1990). Open coding is the early conceptual names assigned to data fragments (Lockee, 2001) and is the process of selecting and naming categories from the analysis of the data. This initial stage of data acquisition would describe the overall features of the phenomenon under study. In this research, the categories emerged from the open coding of interview were identified mostly through line by line analysis. Variables involved in the phenomenon were then identified, labeled and categorized in an outline form so that the researcher could see and understand the processes. To ensure the internal consistency, the emerging categories were compared between interviewees and notes being taken.

The next step of coding process is axial coding. According to Strauss and Corbin (1990), axial coding is the process that relates the categories to subcategories. In axial coding, data were put together in new ways and this was achieved by utilizing a coding paradigm (i.e. a system of coding that seeks to identify causal relationships between categories). The aim of the coding paradigm is to make explicit connections between categories and sub-categories. This process is often referred to as the ‘paradigm model’ and involves explaining and understanding relationships between categories in order to understand the phenomenon to which they relate (Strauss and Corbin, 1990).

The final procedure was the process of selective coding. Selective coding involves the process of selecting and

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