Purchasing decision optimization strategy by service quality innovation through purchase intention

This study aims to understand the behavior of housing buyers. The data collected in this study use a survey method, using the purposive sampling technique to generate 40 respondents. Structural equation modeling (SEM) showed that service quality innovation did not improve purchasing decisions but purchasing intention mediating service innovation and purchasing decision.


Introduction
Currently, there are many developers, especially in the Ponorogo area, East Java, in providing property and competing in providing housing that can be of interest and according to the needs of buyers. Purchasing decisions occur when consumers recognize the problem, seek information about the product, and evaluate each of these alternatives properly to solve the problem, leading to a purchase decision (Senggetang. et al., 2019).
Everyone can get complete information from their area and various remote areas in the digital era. Digital technology has become part of the lifestyle of the Indonesian people (Senggetang. et al., 2019). The opportunity for entrepreneurs to escalate their business is enormous because people's purchase intention is higher (Saputra. et al., 2020). Social media is an integral part of a more extensive and more complete sales, service, communication, and marketing strategy (Gera. et al., 2017). It adapts to the market coupled with an attractive and innovative delivery that will make potential users interested in the advertisements or information conveyed. In this case, the Indraprasta Ponorogo Permai housing adds marketing media with social media, such as Instagram, Facebook, and the marketplace, Olx. Apart from social media, the marketer also collaborates with agents on the Web to sell their housing. They also provide a Call Center that serves during working hours in communication with customers, making it more accessible.
The quality of internal services applied to the housing marketing process is essential from the point of view of potential buyers (Padlee. et al., 2019). Buyer trust is a kind of emotional reflection for trading. It depends on the level of fulfillment of the expected product or service benefits and the level of consistency of expectations and actual results (Joudeh and Dandis, 2018). Service innovation also dramatically influences the buying decision of prospective buyers because prospective buyers are an assessment of a product being offered (Alauddin. et al., 2019).
According to Alauddin. et al. (2019), purchasing intention is defined as the stage of the respondent's tendency to act before making a purchase decision. Just as there are some customers after receiving information by searching for housing in Indraprasta Ponorogo Permai, they will see and consider whether they will buy shortly, prioritize the housing, or pass the information on to their relatives and friends.
Indraprasta Ponorogo Permai housing is under the auspices of PT. Indraprasta Ponorogo has been running since July 2020 until now. Indraprasta Ponorogo Permai housing has 220 units consisting of 10 shophouses, 30 twostory houses, and the rest are 1-story houses. Estimated sales are 80 units in the first year have been achieved. However, from April to October 2021, there was a sharp sell declination. Maybe it was caused by Covid Pandemic; the management is required to innovate in service quality, and it is necessary to read consumer purchase intention.

Method
The type of research in this study is quantitative, aims to examine a particular population or sample, data collection using research instruments (Haryani, 2019). The variables in this study are Service Quality Innovation (X), Purchase intention (X), and Purchase Decision (Y). The research location is in the Indraprasta Ponorogo Permai housing, Ponorogo City.
The subject of this research is the buyer who has bought the residential house. The data collected in this study was conducted using a survey method. Researchers collect data by distributing questionnaires, tests, and structured interviews. The sampling technique used is purposive sampling, where this technique is used to take samples by taking into account the considerations made by the researcher. So the sample in this study was 40 respondents. The measurement scale used is a Likert scale.
Meanwhile, for data analysis, researchers used the SmartPLS version 3.3.3 application. Data analysis was carried out using the Partial Lease Square (PLS) method. PLS is a multivariate statistical technique that makes comparisons between multiple dependent variables and multiple independent variables. PLS is a variant-based SEM (Structural Equation Modeling) statistical method designed to solve various regressions when data-specific problems occur, such as small research sample sizes, missing data, and multicollinearity.

Empirical Result
The Measurement Model (Outer Model) is confirmatory factor analysis (CFA) by testing the validity and reliability of latent constructs (Lestari and Hasibuan, 2021). The concurrent validity test is assessed based on the load factor (correlation between item scores/component scores and construct scores) indicators that measure these constructs. According to Akly. et al. (2021), the reflective measure is high if it correlates more than 0.70 with the construct to be measured for confirmatory research. In contrast, for explanatory research, it is worth 0.6-0.7, and Cronbach's alpha is above 0.6. In this study, all indicators that measure each variable are valid. Parameters for discriminant validity tests can be assessed by looking at the calculation of the cross-loading. In this study, all indicators of service quality innovation variables, purchase intention, and purchasing decisions have a higher loading factor value than the cross-loading on other variables. Therefore, it can be concluded that each indicator can explain the variables that correspond to them so that there is no problem of discriminative validity in the model being tested. The reliability test in PLS can use two methods. Namely, the Cronbach's alpha value must be > 0.6, and the Composite reliability value must be > 0.7, although the value of 0.6 is still acceptable Rizal. et all (2018).
Based on Table 1, Cronbach's alpha value of the Service Quality Innovation variable is 0.986; Purchase intention variable is 0.974, and Purchase Decision is 0.975. In addition, it can be seen that the Composite reliability value of the Service Quality Innovation variable is 0.987, the Purchase intention variable is 0.978, and Purchase Decision is 0.978. From the calculation results, it can be seen that all indicators are reliable in measuring the latent variables. The structural model in PLS is evaluated using R2 for the dependent construct, the path or inner model coefficient scores indicated by the T-statistic value must be above 1.96 for the two-tailed hypothesis and above 1.64 for the one-tailed hypothesis ( one-tailed) for hypothesis testing at 5 percent alpha and 80 percent power (Lubis and Hidayat, 2019).

Service quality innovation
Based on the test results in Table 2, we can see that the t-statistic value of the relationship between service quality innovation and purchasing decisions is 0.890, with a probability of 0.374. The test results show a probability value level of significance above 0.05; this indicates no significant effect between service quality innovation and purchasing decisions. Also, Based on the test results in Table 2, we can find that the t-statistic value of the relationship between service quality innovation and purchase intention is 0.837, with a probability of purchasing decisions of 0.315. The result indicates no significant effect between service quality innovation and purchase intention. Although hypotheses 1 and 2 are rejected. Still, the service quality innovation provides a positive direction for purchasing intention and decision.

Purchase intention
Based on the test results contained in Table 2, it can be seen that the t-statistic value of the relationship between purchase intention and purchasing decisions is 2.443, with a probability for purchasing decisions of 0.015. This result indicates a significant influence between purchase intention and purchasing decisions. Furthermore, hypothesis 3 is accepted.

Service quality innovation on housing purchasing through purchase intention
Based on the test results contained in Table 2, it can be seen that the t-statistic value of the relationship between service quality innovation and purchasing decisions through purchase intention is equal to 1.872 with a probability for a purchase decision of 0.015. This indicates a significant influence between purchase intention and purchasing decisions through purchase intention. Thus, hypothesis 4 is accepted.

Conclusions
Service quality innovation does not improve the purchasing decisions made by customers; purchase intention plays a vital role in buying a house in the Indraprasta Ponorogo Permai housing. In addition, purchase intention mediates the relationship between service quality innovation and purchasing decisions. Therefore, we suggest the need for an evaluation in marketing, especially by prioritizing promotions and delivering other advantages, such as location, design, public facilities provided, and various other things that can increase consumer buying interest. Emphasizing "excellence" will mediate to convert service innovations into purchases. Purchase intention has a role in mediating the relationship of service quality innovation to purchasing decisions; it can be seen that most homeowners in Indraprasta housing are by what they are interested in and following the criteria of the homeowner, so this purchase intention is the basis for homeowners to decide to buy the house.