“Inventory management and customers` satisfaction in the public health sector in Delta State, Nigeria: marketing analysis”

ARTICLE INFO Emmanuel Mitaire Tarurhor and Henry Osahon Osazevbaru (2021). Inventory management and customers` satisfaction in the public health sector in Delta State, Nigeria: marketing analysis. Innovative Marketing , 17(2), 69-78. doi:10.21511/im.17(2).2021.07 DOI http://dx.doi.org/10.21511/im.17(2).2021.07 RELEASED ON Friday, 21 May 2021 RECEIVED ON Sunday, 31 January 2021 ACCEPTED ON Friday, 07 May 2021


INTRODUCTION
In the past, the primary goal of establishing most organization was to maximize profit without considering corporate social responsibilities and its impacts on customers. However, recent studies had shown that organizational focus had shifted from the holistic approach of maximizing profit to customer satisfaction (Jones et al., 1995;Rad, 2008). These findings had prompted business managers to strategize on how to meet customers' demand. Mehfooz et al. (2012) asserted that customer satisfaction is a very difficult task because of individual differences or personality. However, it was concluded that customer satisfaction could be achieved through effective inventory management.
Most of the studies on inventory management and customer satisfaction have been carried out in the manufacturing sector ( Cacioppo (2000) opined that customers spread information to other prospective customers if they derive satisfaction or dissatisfaction from the organizational products and services. The study revealed that dissatisfied customers tell nine other potential customers, while satisfied customers tend to tell five potential customers about the corporate products or services. This study believes that dissatisfied customers can tell millions of people within a minute via social media about their dissatisfaction with the products or services and vice-versa. Therefore, organizational management should note that customers can either 'make or mar' their businesses.

Inventory management
Studies on inventory management have shown that the success of any firm, the fate of the economy, and how an organization survives in the future depends on how inventory is being managed (Umair et al., 2019). Inventory management is a term employed by firms to monitor and evaluate their investments in inventory (Stevenson, 2010

Strategic supplier partnership
The strategic supplier partnership concept was developed in the 1980s to reduce waste and shortening lead times of inventories from the suppliers to the customers (Bicheno, 1996). This concept can only achieve its purpose if there is a good working relationship between the major actors: customers, and suppliers (Mukopi & Iravo, 2015;Ogonu et al., 2016). Furthermore, the strategic supplier partnership recommends that the relationship between the customer and the supplier should not be short-term but long-term. The span of a period is necessary to give the supplier opportunity to understudy the customer to build up relationships and confidence, which will, in turn, propel investment and encourage improved efficient service delivery. In recent times, the concept of strategic supplier partnership has metamorphosed into Vendor Managed Inventory (VMI), where the supplier displays the inventory on a store shelf in the site or close to its end-users (customers) (Mehfooz et al. 2012). Its application has shifted the costs ought to be borne by the customer, such as ordering, holding, and storage costs to the supplier, which directly boosts the customers' profit margin (Loughrin, 2008

Lean inventory
The concept of lean inventory has been introduced into the inventory management literature. It can be traced to Krafcik (1988), who developed the way to correct the weaknesses of 'buffered approaches' of inventory management. Krafcik (1988) asserted that keeping excess inventories and workers is a waste of resources, which will increase the prices of the products or services and adversely affects customer satisfaction. Howell (2001) also defines lean as a target to avail customers what they wanted and deliver immediately without wasting any waste. Thus, 'lean' helped organization management to guide against waste and ensure customer satisfaction as a priority. Womack et al. (1990) pioneered the principle and application of lean production. It was noted that storing inventories in the form of safety stock affects the firm's profit since it can lead to an increase in storage fees, handling, and waste of materials.  2016) found a strong positive relationship between lean inventory management and customer satisfaction. It was noted that the correlation coefficient between customer satisfaction and lean inventory management is 0.795, which is the highest among the variables used in the study. Furthermore, the correlation coefficient between customer satisfaction and information technology, and strategic supplier partnership were 0.725 and 0.352, respectively.

Information technology
Information technology assists inventory managers in accurately calculating the re-order level, minimum stock level, maximum stock level, and delivery period. This helps to guide against shortages and stock out, negatively impacting customers' satisfaction (Ogonu et al., 2016;Mohamed, 2018). Thus, Lyson (1996) proved that the use of technology (Electronic Point of Sales) assisted the organization in restricting stocks to customers' demand, reduced the risk associated with obsolescence and deterioration of inventories. The study concluded that the use of technology has led to improved services rendered to the customers. Ogonu  Furthermore, viewing ICT and inventory management, Eckert (2007) carried out a study on the effects of inventory management on customer satisfaction in a small grocery business. The study establishes that clients were not fully satisfied with the organizational performance. Within 12 months, the organization ran out of stock on 20 different occasions when they were subjected to a manual approach of inventory management control system. Thus, when the organization introduced technology to manage inventory, there were no more stock shortages; customers were happy for six months under review when the inventory management control equipment was being used. Impliedly, information technology has a direct effect on customer satisfaction.

Lead time
The mediating role of lead time on inventory management has scantly appeared in the literature, which warrants further investigation. Lead time in this context can be referred to as the period order is placed for inventories and when they are received. It has become a recurrent problem for the organization since management cannot accurately predict the expected numbers of customers daily to plan against stockout properly. Furthermore, most managers lack proper planning for inventories and cannot accurately determine the distance between hospitals and suppliers of their stocks (drugs). Therefore, organizational management should adopt adequate strategies to manage lead time to guide against shortages of inventories which would, in turn, lead to satisfying the customer's needs (Darko et  This study aimed to examine the impact of the proxies of inventory management (supplier part-nership, lean inventory, and information technology) and moderating variable of lead time on customer satisfaction.
In the light of the preceding review, the hypotheses proposed in this study are: H1a: Strategic supplier partnership, lean inventory, and information technology have a significant and positive effect on customer satisfaction.
H2a: Lead time mediates the significant and positive effect of Strategic supplier partnership, lean inventory, and information technology on customer satisfaction.

METHODS
The case study research design is adopted in this study. It concentrates only on the public health sector to gather data from its stakeholders on inventory management and customer satisfaction.
In applying quantitative approach in gathering this study data, the questionnaires were modified in line with existing literatures to capture the interest of the Patients. Data on inventory management, strategic supplier partnership, lean inventory, lead time and information technology were gathered designated to the health workers. Two hundred and sixty-five questionnaires were distributed to both health workers and Patients among the selected hospitals in Delta State. Out of which, one hundred and five was distributed among Medical Doctors, Nurses, and Pharmacist Laboratory Scientists. However, customer satisfaction data were collected from one hundred and sixty questionnaires administered to Patients.
The research assistants were employed. They were trained to explain the importance of this study and the questionnaire content to the respondents before administering it. Arising from the above, two hundred and forty-nine were collected and found useful; this response rate of 94 percent is considered adequate for this analysis.
The model specification for this study is formulated as: where CUSAT -Customer satisfaction, SSP -Strategic supplier partnership, LI -Lean inventory, IT -Information technology, LT -Lead time.

Data analysis
The study adopts a structural equation model (SEM) that deal with the relationship between the dependent and explanatory variables, including a moderating variable (Tarurhor & Emudainohwo, 2020;Tarurhor, 2017;Civelek, 2018). Thus, structural equation modeling is most suitable and adequate for this study since there is a presence of moderating variable (lead time), which interface between the dependent variable (customer satisfaction) and the independent variables (strategic supplier partnership, lean inventory, and information technology) because of its relational nature (Kumar & Upadhaya, 2017).
This study considers the determination of minimum numbers of respondents used when structural equation modeling analysis is adopted in a study. For instance, Sumwarno (2002) stated that the number of structural equation modeling analysis samples that would provide a reasonably stable result is between 200 to 600 respondents. Bentler and Chou (1987) suggested a minimum of 150, while Celik and Yilmaz (2013) argued that the sample size should be between ≥ 200 and ≤ 500. Kumar and Upadhaya (2017) noted that a sample size of fewer than 500 respondents could not capture indirect effects in a structural equation modeling analysis. Arising from above, this study implies multiple regression analysis to the structural equation model analysis to establish the impact of the moderating variable as the sample size is two hundred and sixty-five (265).

RESULTS AND DISCUSSIONS
265 questionnaires were distributed, out of which 258 were retrieved, but nine (9) had incomplete information. Impliedly, only 249 questionnaires were found valid and used for this analysis, as shown in Table 1. This number satisfies the minimum benchmark for a sample size of studies where SEM is used for analysis.  Table 2 shows that lead time is the most influential variable that affects customer satisfaction reporting 4.08 mean value, comparing to information technology having 3.77 and thus being the lowest contributing indicator to customer satisfaction.   Similarly, when variable moderation was excluded, as shown in model 1, the study result showed a negative relationship of -0.0283682 between information technology and customer satisfaction, while a positive relationship of 0.0271497 when lead time was included in model 2. Furthermore, lead time possesses a moderator role in the relationship between the proxies of inventory management and customer satisfaction. Table 4 reports the fit indices or fit statistics used to ascertain whether the model is suitable for using SEM in the study. Kline (2005) suggested that studies using SEM should report Chi-square, Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), and SRMR. These results, shown in Table 4, confirmed that the model is suitable for this study because they satisfied the acceptable benchmark. The diagram in Figure 1 and SEM results in Table 5 shows that strategic supplier partnership (SSP) has a positive effect (0.048) on lead time (LT) (model 3), while lean inventory(li) also indicates a positive statistically significant effect of 0.53 on the lead time at 1% level of significance. In the same vein, the lead time relationship with information technology (IT) shows a negative statistically significant relationship at 5% significantly (-0.17). This result reaffirms the significant effect of moderating role, as it is statistically significant with explanatory variables of SSP and IT. The LR test is statistically significant, reporting a Chi-square value of 241.08 with a P-value of 0.000.
This is a confirmation that the SEM model is valid for this study.

CONCLUSION
This study has expanded literature on the impact of inventory management, including a moderating variable of lead time on customer satisfaction in government-owned hospitals in Delta State. The findings established a positive and statistically significant relationship between strategic supplier partnership, lean inventory, lead time, and customer satisfaction at a 1% level of significance. However, the result shows a non-significant relationship between information technology and customer satisfaction.
Lead time possesses the characteristics of a moderating role in this study. Thus, the presence of lead time in model 2 reports that R 2 of 56.41% and its exclusion, as shown in model 1, had R 2 of 43.43%. This means that the moderating variable had contributed about 12.98% impacts on customer satisfaction. Besides, lead time had caused information technology to positively affect customer satisfaction as against a negative effect (-0.0283682) when excluded from model 1. This study recommends the government to come out with a policy framework on the maintenance of lead time that hospitals managements should key into as this will guide against shortages of inventories in the public health sector.
Notwithstanding the efforts made on this study, it is not without some limitations. Firstly, the research focuses only on government-owned hospitals including privately owned hospitals associated with a lot of unqualified staff and shortages of inventories. Furthermore, the sample size to capture direct and indirect effects by SEM is less than 500; hence, the study could not measure these.