Introduction
Enterprise resource planning (ERP) is a paramount yet, to some extent, problematic topic in business research and practice. According to the majority of scholars, ERP implementation in firms boosts productivity in operations and supply chain management (SCM) due to a variety of factors including the cost- and time-saving, accuracy and scope (Tarhini, Ammar, Tarhini, and Masa’deh, 2015; Chofreh et al., 2016). Nonetheless, there is also a concern for the cost of introduction, integration, staff training and other downsides that may undermine the effectiveness of ERP solution in a company. The studies were conducted in a variety of designs and methods, but due to the continuous process of innovation and change in the business sphere, the generation of new data appears to be paramount. In light of the ERP high relevance for every corporation, the research in this sphere remains among the top priorities of both scholars and practitioners.
Supply chain construction and maintenance, as prompted by (Fredendall & Hill, 2016), also continues to present a critical area for improvement with the use of scientific data because modern examples of successful SCM are, unfortunately, a rarity. A range of problems persists with managerial commitment as certainly experienced executives tend to develop rigidity towards implementing innovation (Nwankpa & Roumani, 2014). In addition to that, the systematic nature of ERP and breadth of its influence on the operational and SCM processes occasionally require large-scale investments and time commitment. Given that notion, many executives doubt the worth of temporary disruption of network functioning for the sake of ERP implementation. Thus, there is a need for further investigation into the effectiveness of this concept for companies to provide managers with accurate data and assist their decision-making process. Precise measurement and data collection remain among core demands in this sphere of business research and development. Therefore, in this study, a mixed-methods analysis is proposed to measure the productivity of ERP in the setting of a single company with experience in implementing this technology.
Aims and Objectives
The core aim of this research is to ascertain the effectiveness of ERP for operations and SCM in an organization. To achieve this goal several objectives were put forward. They include the following:
- Determine the critical elements that influence the effectiveness of ERP in relation to the company’s operations;
- Understand the modality of ERP in the context of an organization’s SCM;
- Draw conclusions on the value and economic, organizational and other domains of the effectiveness of ERP in a business context.
Rationale for Study
The study is necessitated by the notion that an ERP system requires substantial investment, continuous maintenance, and upgrades that could be quite costly. The literature produces conflicting results on its effectiveness for companies’ operations and SCM due to methodological, sampling or other issues. Therefore, accurate measurement and assessment of the data require further research that involves both quantitative and qualitative tools for increased accuracy and depth.
Critical Review of Literature
Tarhini et al. (2015) suggest that qualitative analysis of firms’ performance with ERP system installed is a valid scientific initiative. Most of the studies implement quantitative data analysis to make sense of the numeric data emerging from annual reports, financial statements and other relevant documents containing information about the company. Multiple studies were devoted to the exploration of cost and time-saving issue. Costa, Ferreira, Bento, and Aparicio (2016) found that ERP is able to optimize communication between different departments, which eventually led to a substantial decrease in project time costs.
In addition to that, due to optimal communication and goal-oriented resource planning, the company was also able to cut spendings stemming from delays in decision-making. However, what remained unclear was the long-term effect of the system’s functioning. The sources of data chosen by Costa et al. (2016) are predominantly from companies whose’ experience of using ERP did not exceed 5 years. The same flaw could be noticed in studies assessing other factors of ERP influence such as operation speed and accuracy (Bahssas, AlBar, & Hoque, 2015; Shen, Chen, & Wang, 2016). In terms of scope, Chofreh et al. (2016) found that resource economy was the top priority factor that changed significantly after the ERP implementation. Given that, it could be assumed it also needs to be included in the present study.
In terms of operation speed, Gavidia (2017) identified a lack of cohesion and coordination in companies that underwent a recent modernization and implementation of an ERP solution. The tested model, as the researcher noted himself, was lacking validation which substantiates further research in this area. Shen et al. (2016) attribute the problem of quantitative assessment to the multidimensionality of ERP as a system for implementation. There are multiple parameters, which researchers tend to assess in a bulk format. Therefore, there appears to be a reason to concentrate on several parameters that could be quantified.
Qualitative analysis may be invaluable for assessing intangible results of ERP influence on operation and supply chain network (Bahssas et al., 2015). Among the critical flaws, the researchers tend to accentuate is employee satisfaction. Again, as previously noted, not many researchers concentrate on exploring long-term effects on this factor, which substantiates the current study effort. Another factor that cannot be quantitatively measured yet presents a critical outcome of ERP implementation is user experience and perceived convenience of the new functionality. Researchers find that this parameter is directly linked with employee productivity, which affects the benefits yielded by the ERP system (Das & Dayal, 2016). As perception and opinions are within the realm of intangible outcomes, there is a need for an interview-based measurement.
In both quantitative and qualitative assessment, there seems to be a need to focus on studying companies that have been operating with ERP installed for a substantial period of time to demonstrate reliable results. Thus, the studies that used companies that used ERP systems for less than five years tend to provide less reliable results which are indicated by the researchers themselves. They note that further long-term effects need to be measured (Costa et al., 2016; Shen et al., 2016; Tarhini et al., 2015). Thus, it would be reasonable to undertake a study in a company that has been operating with ERP for more than 5 years. Such a company could be Texas Instruments. In an earlier case study performed by Sarkisa and Sundarrajb (2003), it is suggested that the company that has been in business since 1930 started the transition to ERP in 1996 and finished it in 1999. This fact presents an opportunity to explore the vast experience of Texas Instruments from both a quantitative and qualitative perspective. Although Sarkisa and Sundarrajb (2003) provided a variety of insights into the process of ERP implementation, they analyzed the performance factors after only 3 years of implementation, which provides ground for further exploration.
Research Design and Strategies
Research Philosophy
Philosophical orientation is an important aspect of research because it provides a more pronounced link to theoretical knowledge and concepts allowing clarity and coherence among all elements of the study. In accordance with Saunders, Lewis, and Thornhill (2009) there are two core philosophical paradigms applied to research such as interpretivism and positivism. The former is based on the premise that the researcher and the participants can be subjects and instruments of the research and therefore favours the use of the qualitative methodology. Transformation of the information under such a paradigm means knowing, which gives the data collected through interviews and observation a valid scientific basis.
Positivism, on the other hand, relies on the natural phenomena, their interactions with other objects as the source of knowledge, which can be understood and studied through various cognitive strategies (Hair et al., 2015). Under this philosophical framework, quantitative methods are favoured as more effective. Given the nature of the present research and the necessity to study the ERP from different facets to reveal its effectiveness or the lack thereof, there is a need to use both paradigms. Interpretivism will be employed as the leading philosophy for collecting and analyzing data through a qualitative part of the present study, while positivism could be applied to the qualitative section of the paper.
Research Approach
Deductive and Inductive approaches are some of the most popular among researchers in the business sphere. The first presupposes using existing theories, paradigms and frameworks to explain and deepen the knowledge of a certain phenomenon. It could be useful as it can add value to the existing practical and theoretical knowledge (Bell, Bryman, & Harley, 2018). Inductive reasoning in studies, in contrast, could assist in the broader exploration of an area that is either poorly researched in general or has specific gaps that need more knowledge. Since the topic of ERP is rather well-researched but still has some gaps to address, deductive reasoning could be best applicable. This approach could also be instrumental as there are certain claims of ERP’s effectiveness, yet for increased objectivity, it seems more reason not to adopt that premise (Bell et al., 2018). Thus, due to the freedoms such as a right for the mistake, unbiasedness and breadth of view inductive reasoning will be chosen.
Research Strategy and Choices
Among prominent research strategies for a mixed-methods design, there could be grounded theory, case study, multiple case study or documentary research. The grounded theory could be applied to studies that aim to derive meaning from data using the inductive approach. According to (Saunders et al., 2009) it also allows decreasing the influence of researcher bias because no initial framing through hypothesis is performed. One limitation is that it could require a substantial amount of data to produce significant results. In the case of this mixed-methods research, this drawback will be addressed as both qualitative and quantitative data will be collected. The company to conduct a qualitative part of the study will be Texas Instruments. It has ERP installed for many years, so it could be a prominent source of insights for the project. The survey could be done among the company’s lower-ranking personnel so to capture the perceptions of a broader audience.
Research Timeline
The estimated time required for the whole research project is estimated at 4 months. The first month will be dedicated to preparing questionnaires and interview questions, validating them. The second and third month will be allocated to data collection as company executives at Texas Instruments may be preoccupied and sufficient time will be needed to reach them. Firstly, the qualitative part will unfold to capture the recurrent themes that reflect key elements of ERP effectiveness or its weakness. Secondly, the quantitative research will follow for Texas Instruments employees that will capture their attitude towards the identified themes and reinforce the qualitative data reliability. The last month will be allocated to data analysis and finalization of the research report.
Population and Sample
The study population will consist of three to four executives, managers, and employees of Texas Instruments. The non-probability sampling technique could be useful to the qualitative section of the project. It allows targeting a specific audience with concrete knowledge that is aimed for analysis. In this case, the research goal of the qualitative section is to capture the perceptions of ERP’s effectiveness among higher-ranking personnel, which requires careful choice of persons. Such individuals will be required to have at least a department manager position and have experience or knowledge of the way the company operated before the ERP was introduced. No other restrictions will be imposed on the sample. The downside is that it does not allow to relate to the “wisdom of the crowd,” but it will be addressed in the quantitative part by the survey.
The questionnaire will collect data through probability sampling as it lets to receive a more complete picture of the ERP effectiveness at all corporate levels. The sample will consist of at least 60 people of all age, gender, and status categories with the only limiting criteria being the experience of working in the company no less than 5 months. Such limitation is imposed in order not to undermine the quality of results by mixing in the responses of new employees, not fully acquainted with the ERP system.
Data Types and Sources
Primary data types will be used for both qualitative and quantitative parts of the research. This category allows for capturing the latest trends which studying secondary data can rarely provide. Its advantage is also in relevance to the topic and the researcher usually chooses what information to collect. However, the accuracy might be diminished as gathering large samples could be problematic and costly. There is also a risk of understanding questions in the way the researcher perceives them. Yet, the reliability it provides to the study stems from the notion that the majority of the sampled people in the company possess an adequate level of knowledge about the company and its ERP system. The workers and executives of Texas Instruments will serve as sources of information about ERP functioning in the domains of SCM and operations.
Data Collection Techniques
The main data collection technique for the qualitative assessment will be an interview. Interviews allow for in-depth exploration and, therefore, a larger chance of receiving the data required. In addition, an interview provides the opportunity to discover the details about the speaker’s thought process which allows exploring the underlying reasons for the particular response (Bell et al., 2018). On the other hand, this method of data collection might be difficult to set up, having in mind the desired interviewees’ positions.
The questionnaire will be the data collection tool for the quantitative research section. It has the potential to deliver the desired outputs in a sufficient quantity for more straightforward and simple analysis. In addition, this tool is most likely familiar to the participants so that few difficulties could arise from receiving low-quality results. Finally, the tool is rather inexpensive and easy to administer online. The limitation might be the complicated design of questions and their content that adequately measures the topic. The questionnaire will consist of multiple-choice, open-ended items, and Likert-scale questions to establish the breadth of measuring employees’ perceptions.
Method of Analysis
The key method for making sense of qualitative research will be content analysis. This framework consists of identifying key themes and concepts in the transcribed text through the coding process (Erlingsson & Brysiewicz, 2017). The revealed codes and themes will then be sorted into categories, the prevalence and relative strength of which will be measured by the number of similar recurrent items in the category (Bell et al., 2018). Presumably, the approach will allow for key elements of ERP effectiveness to emerge from open-ended questions. The key advantages of this method are the depth of textual analysis and emphasis on human thought that is invaluable in measuring perceived and actual effectiveness (Lightfoot, Curran, Jarvis, & Kitching, 2000). This method will also provide the opportunity to measure the codes quantitatively allowing for additional insights.
The quantitative part will be analyzed through the use of descriptive statistics. It is required to establish the numeric support for the prevalent metrics as well as capture the accuracy, speed, time, and cost of the ERP. This will assist in exploring the effectiveness domain and link it to specific parameters. Mean, median, and standard deviation will be the key statistics to pursue when measuring the responses (Bell et al., 2018). These tools are the core qualitative assessments used in most of the studies that provide a chance to make sense of the gathered data through visualization. In addition, descriptives are one of the simplest measurements in statistics, which means that using them will allow disseminating the scientific results to a wider audience. The downside of using them alone is the low potential to argue for the statistical significance of the study. To mitigate that, the research will use a T-test to measure the significance of differences between mean scores. SPSS statistics will be used for automating the process of analysis.
Limitations of Proposed Methodology
The key limitation of this methodology is its design complexity. Mixed methods studies are rather difficult to the device due to the necessity to construct two data analysis tools, and analyzing large volumes of data, which is also time-consuming (Bell et al., 2018). Such a problem will be reduced by using the assistance of fellow students, scientific advisor’s feedback and other sources of help. Another limitation is the sample size of key personas for the qualitative part of the project. Limited availability of resources does not allow the project to interview more people. This will be compensated for by supporting qualitative data with quantitative results so the research will add more value to the field of studying ERP. There is also an uncertainty in the willingness of key people in Texas Instruments to participate in the study, so to ensure their cooperation, one might use university support.
References
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