A Study on SaaS ERP Continuous Use Intention of Fresh Food Distribution SME ()
1. Introduction
With the fast maturity of information technologies and the acceleration of digital transformation of enterprises, the cloud computing industry has ushered in a rapid development stage. The software as a service (SaaS) market is 53.8 billion yuan in 2020, with a year-on-year growth of 48.7%. Moreover, it is expected that the market will continue to expand at a compound growth rate of 34% in the next three years1.
SaaS ERP realizes the relevant functions of the ERP system in a new mode, which fundamentally solves the problems of the huge initial investment in traditional ERP, difficult system maintenance, and low implementation success rate. Especially for SMEs, the SaaS model allows customers to access software services with minimal installation costs and infrastructure, and enjoy maximum flexibility and scalability. Since SaaS adopts a subscription delivery method, and the supplier only charges the corresponding fee when the customer subscribes to the software regularly. Therefore, the continuous use of SaaS ERP is the key to supplier profitability.
Most of the current research on SaaS ERP focuses on the adoption of SaaS ERP, and several scholars have studied the conversion intention of SaaS ERP. But there are relatively few studies on the continuous use behavior of SaaS ERP. Based on the TAM model and the TOE framework, this paper constructs a research model on the continuous use intention of SaaS ERP of small and medium-sized fresh food distribution enterprises. On the one hand, it theoretically fills the research gap on continuous use intention in the field of SaaS ERP; On the other hand, in practice, based on the research results, some suggestions are given for software vendors to promote SaaS ERP and maintain long-term customer relationships, so as to improve the continuous payment rate of SaaS ERP.
This paper is structured as follows: 1) the literature review is in Section 2, 2) Research hypotheses are proposed in Section 3, 3) Research design is reported in Section 4, 4) Research results are discussed in Section 5, 5) Theoretical contribution and further research areas are presented in Section 6.
2. Literature Review
Unlike traditional ERP, SaaS ERP customers pay the service fee by subscription, and use the system through the Internet, while the supplier controls the application and data. Park & Jeong (2013) pointed out that SaaS ERP is a supplier hosting method of ERP, which allows customers to obtain services through the internet without purchasing software and installing it into their own computer system, thus reducing the cost of implementing ERP projects. SaaS ERP provided enterprises with the opportunity to use ERP at a lower cost (Lee et al., 2013) . Since the infrastructure investment and the operation and maintenance of the system are all borne by the supplier, the enterprise only needs to pay according to the functions used, which is only a small part of the purchase of a complete set of equipment, software or professional technology, thus greatly reducing the total cost of ownership of ERP.
Some scholars have studied the influencing factors on enterprise SaaS ERP adoption. Faasen et al. (2013) based on the TOE framework through semi-structured interviews with executives of small and medium-sized enterprises in South Africa to explore the factors that make them reluctant to adopt SaaS ERP. The results show that improved IT reliability and perceived cost reduction were seem as benefits they were dominated by other reasons. Reluctance to adopt was attributed to systems performance and availability risk; sunk cost and satisfaction with existing systems; data security risk; loss of control and lack of vendor trust; functionality fit and customization limitations. Das and Dayal (2016) synthesized innovation diffusion theory, technology acceptance model and task-technology matching theory to construct the adoption model of SaaS ERP system, and found 11 factors under the three themes of technology advantage, organizational fit and perceived social pressure have important impacts on SaaS ERP adoption. The results suggest that vendors should focus on providing cost-effective, reliable, secure, standardized, long-term, convenient, and better quality of service and support to clients. Moreover, they should provide free trials and customize their solutions while maintaining a balance between additional costs incurred and business value gained due to customization. The clients, in contrast, should determine organizational fit of the CLERP and train their employees to minimize resistance to its adoption.
In addition, several scholars have studied the switching intention of SaaS ERP. Zhang et al. (2019) investigated the factors affecting enterprises’ switching intention to SaaS ERP systems in China. Their study found that information quality and system quality have a significant impact on perceived usefulness, and perceived ease of use has a significant impact on perceived usefulness, while perceived ease of use and perceived usefulness have a significant positive impact on enterprises’ switching intention to SaaS ERP systems. Chang (2020) investigated the enablers and inhibitors concerning switching to SaaS ERP systems in Taiwan, and found system quality, financial advantage, and industry pressure positively affect switching intention of cloud ERP system. However, the perceived risk of cloud ERP systems and satisfaction with and breadth of use of incumbent ERP systems negatively affect switching intention of SaaS ERP system.
In summary, the researches on SaaS ERP mainly focuses on the adoption intention and adoption behavior, as well as the switching intention, but lacks the researches on the continuous use intention of SaaS ERP. However, in the subscription charging model of SaaS ERP, regular renewal of the customer’s continuous use is particularly important for the revenue and development of the supplier. Secondly, s Scholars mostly discuss the direct influencing factors of enterprise behavior or intention, and lack the consideration of intermediate process and complete mechanism. Based on the TAM model and referring to the TOE framework, this paper constructs a research model on the continuous use intention of SaaS ERP of small and medium-sized fresh food distribution enterprises, based on the data of a leading domestic fresh food distribution SaaS ERP supplier (Company A).
3. Research Hypotheses
3.1. Technical Factors and Satisfaction
Perceived usefulness is defined by Davis (1989) as “the degree to which users subjectively feel that when using a particular system service, it will significantly contribute to the improvement of work results, such as performance growth, efficiency improvement, cost reduction, etc.” Bhattacherjee et al. (2008) points out that perceived usefulness applies not only to initial adoption studies of information systems, but also to user satisfaction and intention to continue using them. The core purposes of enterprises adopting SaaS ERP are to reduce costs and increase efficiency, and whether these purposes are achieved is determined by the perception of the organization’s users based on the expectations before purchase and the actual experience after purchase. The higher the positive perception of these purposes, the higher the satisfaction. Therefore, the following research hypothesis is proposed:
H1: Perceived usefulness has a significant positive effect on satisfaction
1) Perceived ease of use and satisfaction
Perceived ease of use means that when users use a particular system, they subjectively feel that it can make you easier, and save time. In the TAM model, perceived ease of use will affect the attitude of using the system. This attitude will affect the level of satisfaction with SaaS ERP. The employees in the fresh food distribution industry are generally old, with low education background and high turnover. They have a very clear demand for the ease of use of the SaaS ERP system. Therefore, the following research assumption is proposed:
H2: Perceived ease of use has a significant positive impact on satisfaction
2) Safety and satisfaction
Both large-scale enterprises and SME attach great importance to security. Many scholars have made empirical research and confirmed that safety is an important consideration in the intention to information system’s adoption. Lechesa et al. (2012) found that organizations’ concerns about the security of network infrastructure will significantly and negatively affect their intention to adopt SaaS ERP. This concern about security will impact the organization’s satisfaction with SaaS ERP. SaaS ERP has the features of cloud deployment and rapid iteration of SaaS software, but it uses public cloud deployment servers. The data that enterprises run on SaaS ERP can be viewed as the supplier of SaaS ERP. The public cloud itself is also prone to data loss or disclosure due to subjective and objective reasons of SaaS ERP providers or cloud server providers. The data security problem brought here is an important factor that affects whether SMEs are satisfied with the application of SaaS ERP. Therefore, the following research assumption is proposed:
H3: Safety has a significant positive impact on satisfaction
3.2. Satisfaction and Continuous Use Intention
Kotler et al. (2008) argues that customer satisfaction is the degree to which perceived quality differs from the desired outcome, and this feeling and state will influence the intention and behavior of an organization or individual. Oliver (1980) states in the ECM model that satisfaction affects an organization’s continuous use intention. After using SaaS ERP, fresh food distribution companies will form a perceptual judgment comparing with the original expectations, which will have an impact on the intention to continue use SaaS ERP system. Based on this, the following research hypothesis is proposed:
H7: Satisfaction has a significant positive impact on the intention to continue using SaaS ERP.
3.3. Organizational Factors and Continuous Use Intention
Top management’s support and continuous use intention
For fresh food distribution enterprises, the top management is directly responsible for the enterprise informatization and business operation efficiency. In particular, the top management is the final decision makers for the adoption and continuous use of SaaS ERP. Many scholars’ research indicates that the top management of an organization is an important factor affecting the use of information technology in an organization. Therefore, this paper proposes the following research assumption:
H4: Top management’s support has a significant positive impact on the intention to continue using SaaS ERP
3.4. Environmental Factors and Continuous Use Intention
1) Industry competitive pressure and continuous use intention
Misra & Mondal (2011) found that the more competitive the industry is, the higher the intention of the organization to adopt technology. The research of relevant scholars has pointed out that the competitive pressure in the industry will affect the SaaS ERP adoption behavior of enterprises (Jia et al., 2018) . The pressure of industry competitiveness is the key environmental factor to promote the small and medium-sized fresh food distribution enterprises to accelerate the informatization transformation, adopt and continue to use SaaS ERP. By SaaS ERP, fresh food distribution SME can obtain the advantages of cost reduction, operational efficiency improvement and other comprehensive competitiveness improvement. Therefore, the following research assumption is proposed:
H5: Industry competitive pressure has a significant positive impact on the intention to continue using SaaS ERP
2) Industry cooperation pressure and continuous use intention
Jia et al. (2018) found that if ERP can meet the personalized needs of partners or customers, it would have a positive impact on the adoption of cloud ERP by enterprises. Fresh food distribution has a higher requirement for close cooperation between upstream and downstream. The adoption of SaaS ERP can make collaboration more efficient. This will directly affect the distributor’s continuous use of SaaS ERP. Based on this, the following research assumption is proposed:
H6: Industry cooperation pressure has a significant positive impact on the intention to continue using SaaS ERP
In summary, the research model constructed in this study is as follows (Figure 1):
4. Research Design
4.1. Variable Measurement
The variables were measured with a mature scale with good reliability and validity, and the items were moderately modified in combination with the characteristics of SaaS ERP, the application scenarios of small and medium-sized distribution enterprises.
All variable measurement items were quantified using Likert 7-level scale. According to the degree of recognition of the respondents, they were divided into very disagree, disagree, some disagree, neutral, some agree, agree, very agree, corresponding to 1 - 7 points (Table 1).
4.2. Sample and Data Collection
This study adopts the convenient sampling method and takes the customers of Company A as the survey object (Company A is a leading domestic fresh food distribution SaaS ERP supplier). The respondents are all senior leaders of fresh food distribution SME. In order to improve the reliability and extensiveness of the survey content, the selected survey objects cover different regions, different years of operation, and different cumulative continuous payment times. In this study, 30 senior leaders of fresh food distribution SMEs were selected for pre-survey on January 10, 2022. Through discussion with practitioners and professors, the questionnaire is constantly improved to make it concise and easy to understand.
The study distributed the questionnaire through the Questionnaire Star online platform, which was distributed for 10 days from January 14 to 24, 2022. A total of 173 questionnaires were sent out in this survey, and 115 valid questionnaires were finally returned, with an effective rate of 66.5%.
4.3. Reliability and Validity Tests
In this paper, the reliability of the measurement scale was assessed using the internal consistency coefficient (Cronbach α value) and the combined reliability (CR). The results are shown in Table 2, and the α and CR values of each factor in this study are higher than the required level of 0.7, so this measurement scale has good reliability.
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Table 1. Variables and their resources.
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Table 2. Reliability test of the scale.
Most of the measurement scales of this paper come from the mature scales. Three senior professors in the IT field and three senior executives of Company A reviewed the initial questionnaire, revised the questionnaire according to relevant suggestions, eliminated some questions, and then distributed the questionnaire to 30 companies, conducted a pre-questionnaire test, and adjusted the questionnaire according to the analysis of the pre-test results. Therefore, the questionnaire has good content validity.
In this study, exploratory factor analysis is used to test the construction validity of questionnaire. Firstly, KMO and Bartlett sphere tests are carried out before the analysis to determine whether the data are suitable for factor analysis, and the test results show that the KMO values of each measured latent variable are greater than 0.9, and the Bartlett test is significant at the level of 0.001, so exploratory factor analysis is appropriate for the data. The results of the test show that the questionnaire used in this study has good construction validity.
According to Fornell & Larcker (1981) , if the mean extraction variance (AVE) of each variable is greater than 0.5, the convergence validity of the measurement is good; The square root of AVE of each variable is greater than the correlation coefficient of this variable with other variables, indicating good discriminant validity. As shown in Table 3, the measurements meet above criteria, that indicate that this measurement has good convergence and discriminant validity.
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Table 3. Correlation coefficient matrix and square root of AVE value.
Note: The number on the diagonal is the square root of AVE.
In this study, variables were measured through self-reported questionnaire data of respondents, which may have a common method variance (CMV). In this study, the control unmeasured latent method factor method was used to test the CMV (Liang et al., 2007) . The authors added a common method factor containing all the items in the research model, and then calculated the substantial factor load and method factor load of each item separately. The variance interpretation rate of the mean substantial factor was 0.781 and the variance interpretation rate of the mean common method was 0.020, the ratio of the two was about 39:1, and the load of most of the method factors was not significant, indicating that there was no serious common method deviation.
In this study, the variance inflation factor (VIF) is used to diagnose the collinearity of items. Montgomery et al. (2009) believe that when VIF is greater than or equal to 5, it indicates that there is a multiline problem between the measurement items. The VIF of all measurement items in this study are all less than 5, indicating that there is no multicollinearity problem.
5. Research Results
The PLS (Partial Least Squares) method is used to test the structural equation model to verify the hypothesis proposed in this paper. PLS does not require high sample size and residual distribution, and it is suitable for testing complex models, and is more suitable for exploratory research questions rather than confirmatory research questions (Fornell & Bookstein, 1982) . Since the research question in this paper is exploratory, SmartPLS4 is used as the analytical tool of the PLS method, and the results are shown in Figure 2. This model explains the variance of 69.4% of the continuous use intention, which has strong explanatory power.
Table 4 shows the path coefficients of the model, the t-value used to test the significance level of the path coefficients, and the corresponding hypothesis test results.
1) The path coefficient of “perceived usefulness” to “satisfaction” is 0.540, p < 0.001, indicating that perceived usefulness has a significant positive impact on
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Figure 2. Research results. Note: *means p < 0.05, **means p < 0.01, ****means p < 0.001 (all are bilateral tests). The number of samples was 115, and Bootstrapping repeatedly sampled 5000 samples for parameter estimation.
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Table 4. Path coefficients and hypothesis test results.
satisfaction. This shows that if the SaaS ERP system can truly meet the needs of users, it can achieve customer satisfaction. Therefore, hypothesis1 is supported.
2) The path coefficient between “perceived ease of use” and “satisfaction” was 0.277, p < 0.01, indicating that perceived ease of use also had a significant positive impact on satisfaction. This shows that if the SaaS ERP supplier needs to make the design of the SaaS ERP system as simple and understandable as possible, the better the interaction experience, the better the customer satisfaction. Therefore, hypothesis 2 is supported.
3) The path coefficient of “safety” to “satisfaction” is −0.010, p > 0.1, indicating that the impact of safety on satisfaction is not significant. Therefore, hypothesis 3 is not supported. The possible reason is that the SaaS ERP system of Company A does not include sensitive information of fresh food distribution customers; Secondly, the SaaS ERP system of Company A has strong business process modules and weak financial modules. Most enterprises will use special financial software to synchronize business data for business analysis. The above reasons may cause most fresh food distribution enterprises to have no obvious demand for the security of SaaS ERP system.
4) The path coefficient of “satisfaction” to “continuous use intention” is 0.574, p < 0.001, indicating that satisfaction has a significant positive impact on the intention to continue using SaaS ERP. This shows that the satisfaction of using SaaS ERP in this year will positively affect the organization’s intention to continue using SaaS ERP in the next year. Therefore, hypothesis 7 is supported.
5) The path coefficient of “top management’s support” to “continuous use intention” is 0.223, p < 0.01, indicating that top management’s support has a significant positive impact on the intention to continue using SaaS ERP of fresh food distribution enterprises. This shows that senior managers’ cognition and attitude towards SaaS ERP directly affect whether SaaS ERP can be used continuously. Therefore, hypothesis4 is supported.
6) The path coefficient of “cooperation pressure” to “continuous use intention” is 0.039, p > 0.1, indicating that the impact of cooperation pressure on the intention to continue using SaaS ERP is not significant. Therefore, hypothesis 5 is not supported. The reason may be that the SaaS ERP of fresh food distribution is still in the initial promotion stage, and the penetration rate of SaaS ERP of fresh food distribution enterprises is not high.
7) The path coefficient of “competitive pressure” to “continuous use intention” is 0.100, p > 0.1, indicating that the impact of competitive pressure on the intention to continue using SaaS ERP is not significant. Therefore, hypothesis 6 is not supported. The reason may be that 70% of the respondents in this study are from the canteen of the distribution company. This kind of business pays more attention to obtaining business based on social resources, and the competitive advantages brought by its own informatization are not highly valued by these enterprises.
6. Research Conclusion
Based on the TAM model and referring to the TOE framework, this paper constructs a research model of SaaS ERP continuous use intention of fresh food distribution SME. The results of this study show that:
1) The higher the perceived ease of use of SaaS ERP, the higher the satisfaction of using SaaS ERP. The employees in the fresh food distribution SMEs have low overall education background and high mobility. Whether SaaS ERP is easy to learn has a direct and significant impact on the organization’s decision whether to continue to pay for use. The system is easy to learn and use, and the cost that enterprises need to spend on operation training can be greatly reduced. The feedback of users in the organization on the ease of use of the system will affect the organization’s continuous payment decision.
2) The higher the perceived usefulness of SaaS ERP, the higher the satisfaction of using SaaS ERP. The purpose of the enterprise purchase system is to reduce costs and increase efficiency, and to solve problems such as internal processes and efficiency. The greater the role of SaaS ERP in related issues, the higher the satisfaction of enterprises with the use of SaaS ERP.
3) The higher the satisfaction of fresh food distribution SMEs with the use of SaaS ERP, the higher the intention to continue to pay for the use of SaaS ERP.
4) The greater the support of the top managers of fresh food distribution SMEs, the higher the intention to continue to pay for the use of SaaS ERP. Information system projects of SMEs are often “top projects”. If the top management recognizes, supports and is willing to invest financial and human resources for it, then SaaS ERP can be guaranteed to be continuously implemented within the enterprise.
5) For small and medium-sized fresh food distribution enterprises, the impact of security, cooperation pressure and competition pressure on the SaaS ERP continuous intention is not significant.
This study enriches the research on the intention to continue using and its process mechanism in the SaaS ERP field. This study verifies that the fresh food distribution SaaS ERP software can improve user satisfaction by improving perceived ease of use and perceived usefulness, thus improving user’s continuous use intention; At the same time, the strong support of the top management of the enterprise will also improve the enterprise’s intention to continue using SaaS ERP for fresh food distribution.
This study also has certain limitations. First of all, the research model of this study is relatively simple. There are many other factors that affect the intention of fresh food distribution SMEs to continue to use SaaS ERP, such as employee support, product update iteration speed, etc. Secondly, due to the limitations of objective conditions, this study cannot conduct a real large-scale sampling survey, which will affect the universality of the research conclusions of this study. Third, this study adopts a static cross-sectional research method. The data collected is mainly information at a certain point in time. It is worth noting that the intention of fresh food distribution SMEs to continue to use SaaS ERP will be affected by multiple dynamic factors. These dynamic conditions are not considered in this study. In the future, longitudinal research can be considered to reveal the decision-making process of enterprises using SaaS ERP and continuous payment, in this way, we can better understand the impact mechanism of other factors on the continuous use of SaaS ERP.
NOTES
1Data source: iResearch Consulting, China’s enterprise-level SaaS industry research report in 2021.