Structural Equation Model of Factors Influencing the Selection of Industrial Waste Disposal Service in Cement Kilns

Abstract

Industrial waste disposal in a cement kiln is an operation that includes waste disposal as well as the conversion of waste into renewable energy, which is a cement industry in many countries. This research studied business factors related to the intention to use co-processing industrial waste disposal service in cement kilns by surveying the data with questionnaires from 1251 customers nationwide. The objectives of this research were to study the relationship of business factors by using structural equation modeling to analyze factors influencing the selection of industrial waste disposal service in cement kilns. The study results found that customer attitude towards the following factors, including perceived ease of use, perceived usefulness, disposal price, service provider location, promotion, people, and a service provider’s infrastructure, influenced intention to use the service. The variables that customers gave importance to were the industrial waste disposal with zero wastes to landfill and the use of industrial waste relevant to the circular economy by using the industrial waste, which has a quality of renewable fuel in cement kiln as the renewable fuel of the cement furnace. According to the research results, service providers in cement kilns can potentially plan service strategies to achieve sustainability for further business operations in a highly competitive market.

1. Introduction

There have been various methods of industrial waste disposal, such as industrial wastes [1,2], sewage sludge [3,4,5], and hazardous waste [6,7]. Concurrently, there are studies on reducing industrial waste generation with the principle of waste management hierarchy [8,9,10,11]. Incidentally, reducing industrial waste is ineffective; however, industrial waste requires disposal. Nowadays, using sanitary landfills [12] and incineration [13] are disposal methods: however, they do not get benefits from industrial wastes [14]. At present, contrary to the old concept of merely eliminating the wastes, in addition to using waste more than conventional incinerator waste [15], the concept of sustainable waste management with a circular economy is the management of wastes from production and consumption by bringing produced and consumed raw materials into a new production process. With a circular economy waste management approach, this process creates value by turning from industrial wastes to converting waste to energy [14]. Its technology covers the utilization of heat from waste incineration and that of converting waste into renewable fuels in cement kilns, or the main fuels for electricity generation of waste-to-energy power plants.

Cement manufacturing companies potentially provide waste and industrial waste disposal services called “co-processing in a cement kiln”. The industrial waste which has a quality of renewable fuel in cement kiln is used as the renewable fuel of the cement furnace. It consequently reduces the use of coal, conserves resources, and simultaneously disposes of industrial wastes [16]. Moreover, the disposal does not have an impact on the environment [1]. It is a good choice for industrial waste disposal, environmental friendliness, and landfill reduction [2], being the renewable energy of the cement industry in many countries [17].

In Thailand, under strategic energy planning, the Ministry of Energy has prepared five energy master plans during B.E. 2558–2579. According to the declared plan, a promotion of using renewable energy in generating electricity results in more waste-to-energy power plants [18] which potentially manage industrial waste disposal. According to Commission [18], they have an advantage in transportation distances due to their proximity to industrial sites. Thus, the waste-to-energy power plants are abundantly built and they subsequently scatter throughout the country.

Industrial waste is sewage or unused materials produced by industrial plants. It requires disposal technology methods with specially designed management systems. Therefore, the hiring cost of industrial waste disposal is higher than that of ordinary waste. Such high management prices consequently cause the high competitive industrial waste disposal business in Thailand [19]. The cement kilns co-processing industrial waste disposal service providers are the early groups in the industrial waste disposal business. In addition, they are service providers potentially handling the tasks in large quantities with an international standardized management system [20]. However, due to more competitors increasingly entering the market, the former customers have switched to use the new competitors’ service.

From the previous studies, there have been studies on the feasibility of using different types of co-processing industrial waste disposal in cement kilns [4,21]. In reference to the literature review, the research studied the probability of using waste as a renewable fuel in cement kiln. Referring to literature review, it was found that there is experimental research where the waste has a calorific value equivalent to coal and is brought to replace the coal trial in cement kiln. In parallel, there have been studies on the impacts of industrial waste disposal on cement quality [22], and on the environment in industrial waste disposal management as well [3,7]. While Emmerich et al. [23] and Ndebele [24] conducted a study on the acceptance of renewable energy technologies without mentioning co-processing technology in cement kilns, there were some studies on waste disposal in terms of business, such as municipal solid waste in landfill [25]. However, these did not mention the co-processing of industrial waste disposal in the cement kiln business. As mentioned above, there have never been studies on factors influencing the selection of the co-processing industrial waste disposal in cement kilns services. The industrial waste disposal of cement companies use industrial waste as a renewable fuel to reduce coal consumption due to the coal shortage probability in the future [22]. In addition, waste disposal potentially yields a return on disposal costs [20]. Owing to the mentioned benefits, more competitors have been progressively emerging [19]. Thus, from the research gap, this research will supportively fulfill the related factors in terms of business by studying the relationship between the factors and the intention to use co-processing industrial waste disposal in cement kiln service using structural equation modeling. The research results potentially acknowledge the causal relationship of factors influencing service selection. With customers’ empirical data compared to the established model, the service providers can subsequently take these results to plan the business strategies for their business sustainability in the future.

2. Literature Review

2.1. Theoretical Framework

Due to the limited research on the causal factors related to the intention to use the service of co-processing industrial waste disposal in cement kilns, to find the solution, this research has studied a literature review and built a research framework starting from the large to the small one to find the factors. Beginning at the literature review, we divided its results into three main conceptual framework groups. The first group studied renewable technology acceptance, such as Ndebele [24], Bronfman et al. [26], and Park and Ohm [27], and studied using factors including intention to use renewable technology, renewable technology acceptance, perceived risk acceptance, perceived reward, and trust. The second group studied waste management, such as Cole et al. [11], Ghalehkhondabi et al. [28], and Vassanadumrongdee and Kittipongvises [29], using factors including waste disposal service providers, trust, perceived benefit of service usage, and waste disposal knowledge. The third group studied the waste-to-energy, such as Vassanadumrongdee et al. [29], Vrabie [30], and Jaworski and Kajda-Szcześniak [31], using factors including acceptance and benefit of usage. These conceptual framework groups are summarized and widely studied using theoretical frameworks including marketing mix, Theory of Planned Behavior, and the Technology Acceptance Model.

2.2. Marketing Mix

The main goals of this research are to study, discuss, and explain the casual business factors influencing the intention to use cement kilns co-processing industrial waste disposal service. The service providers can use the research results to plan the strategic business. Similar to the research of Xu et al. [32], it stated the cement kiln co-processing of industrial waste disposal should be concurrently considered a potential business in addition to the balance of the various types of industrial waste disposal. Many researchers have studied business factors: for example, Wonglakorn et al. [33] studied customer loyalty in logistics services [33] and Kwok et al. [34] argued that business operation must start from positioning the business in the market to differentiate through the use of marketing mix theory in management. This is similar to the study of Salman et al. [35], which additionally explained that applying the marketing mix theory into management would increase the business competitiveness. In the meantime, Othman et al. [36] and Pomering and Johnson [37] added that the marketing mix theory, consisting of factors including product, price, place, promotion, people, physical, process, corresponded to [38]. Moreover, there are several examples of researchers such as Wongleedee [39] who studied the marketing mix theory of selling products, while Bukova et al. [40] applied the marketing mix into service jobs by taking the mentioned theory to establish hypothesis and test with structural equation modeling (SEM). The research results found that the marketing mix theory resulted in the business operation success. Nowadays, the researchers increasingly use the marketing mix theory along with a structural equation modeling. For example Bukhari et al. [41], Lee and Jin [42], and Sheau-Ting et al. [43] used the marketing mix theory together with the structural equation modeling in merchandising and discovered that the customer purchase intention depends on the product. Menegaki [44] studied the renewable energy business and found that the customer intention to use service depends on the product, service, and price. Similar to Oflac et al. [45], they discovered that the customer’s intention to use service depends on service, price, place, promotion, physical, people, and process. Corresponding to Bukova et al. [40] and Blut et al. [46], they stated that the marketing mix is a fundamental tool for controlling the success in business operations. Therefore, it can be concluded that from the marketing mix theory, the frequently used factors comprising price, place, promotion, people, and physical are normally studied with structural equation modeling (SEM).

2.3. Theory of Planned Behavior (TPB)

The Theory of Planned Behavior (TPB) is the application of the limitation of The Theory of Reasoned Action (TRA)) [47] proposed by Ajzen [48]. He defined that the planned behavior resulted from the intention comprising three influencing factors, including attitudes towards behaviors [49], subject norm, and perceived behavioral control. Attitude towards behavior is an assessment of an individual’s overall behavior and its consequences. Weber et al. [50] added that the behavioral expression included both positive and negative aspects. In addition, Cheunkamon et al. [51] added that an individual’s positive behavior will lead to their positive attitudes towards behaviors. On the other hand, their negative behavior will result in their bad attitudes towards behaviors. Subject norm is an individual perception of a need or social expectations which put an impact on an individual. It can be a group of close people or an influencing group which has an impact on that person. Corresponding to [52,53] and Ali et al. [54], they described that the tendency of behavioral expression increases when an individual assesses that the influencing group requires it. Based on this concept, Nduneseokwu et al. [55] and Lou et al. [56] used the Theory of Planned Behavior (TPB) to study waste management. Ali et al. [54], Apipuchayakul, and Vassanadumrongdee [57] studied this theory with the intention to buy energy-saving equipment. In addition, Cheunkamon et al. [51] applied the Theory of Planned Behavior (TPB) to the structural equation modeling (SEM) to consider personal factors affecting the intention to use technology social media. In conclusion, with the Theory of Planned Behavior, the researchers collaboratively studied the factors, including attitudes toward behaviors, subject norm, and intention with structural equation modeling (SEM).

2.4. Technology Acceptance Mode (TAM)

Davis [58] developed the Technology Acceptance Model (TAM) from Ajzen’s TRA theory Ajzen [48], of which the introduction and development began in 1975. Later in 1985, the additional factors of belief and perceived behavioral control in decision-making were announced and consecutively developed to be the Theory of Planned Behavior (TPB) in 1989. The subsequent improvement grew into the theory of information technology acceptance and became the Technology Acceptance Model (TAM) [59]. This theory addresses the factors affecting the acceptance or use of technology. It consists of perceived ease of use, perceived usefulness, and attitude. Weber et al. [50] further explained that it is an expression of acceptance for forecasting personal attitudes among various current technologies corresponding to [60,61]. Cheunkamon et al. [51] clarified that it is a widely accepted and prevalent theory for researchers to explore the intention to use technology. In terms of application, there is a variety of studies using it. For example, Alam et al. [47] used this model to study households’ intention to use solar power, while Zhang et al. [62] and Chin et al. [60] studied this theory in the field of energy business. In terms of the factors that the researchers used to study in Technology Acceptance Model (TAM) [47], Cheunkamon et al. [51] and Chin et al. [60] used attitude, subject norm, perceived ease of use, and perceived usefulness. From the Technology Acceptance Model, it can be concluded that the factors the researchers generally used to study in the research with structural equation modeling (SEM) consisted of attitude, perceived ease of use, and perceived usefulness.

2.5. Structural Equation Modeling (SEM)

Kline [63] and Hair [64] defined structural equation modeling (SEM) as the causal and correlation analysis with multivariate analysis that combines variable correlation techniques, variance, and correlation coefficient. The variables in structural equation modeling can be both independent and dependent variables. They can also examine the established model compared with the actual data. In this study, the business factors emerged from the reviewed literature were taken to formulate the hypotheses and subsequently tested by using a structural equation modeling entitled “Structural equation model of factors influencing the selection of industrial waste disposal service in cement kilns”. It consists of a measurement model, which describes the linear relationship of factors and exogenous variables, while using structural equation to confirm the structural correlation, which is established from the literature review compared to the empirical data to investigate whether they were consistent or not. The results will be further discussed.

2.6. Integration of Theories (Marketing Mix, TPB, TAM)

The relationship between TPB and TAM theories has been accepted and widely applied with their similarities. Regarding attitude, which provided both positive and negative results, the TAM theory also complements the TPB theory on perceived ease of use that affected satisfaction and intention [51]. Consistent with Alam et al. [47], they stated that the use of TAM impossibly reflects the total environment to be studied. Along with perceived ease of use and perceived usefulness factors to forecast attitude, it should simultaneously use the TPB theory with attitude and social norm to predict intention. The study is also consistent with Ali et al. [54], who believed that predicting intention must be used together with attitude and social norm. In addition, Mustafa et al. [65] had the same agreeable direction. Bukhari et al. [41] added that forecasting customer intention required to supportively study with attitude, social norm, price, and people. Furthermore, in terms of business, the researchers must study together with the marketing mix theory, which consists of the factors of price, place, promotion, people, and physical [43,44,45].

Therefore, this study concluded that the researchers have taken the literature review results to study the factors of price, place, promotion, people, and physical (the marketing mix theory) for explaining the causal relationship with the intention to choose the service. The factors of intention to choose this service has been collaboratively studied with social norm and attitude (Theory of Planned Behavior). In addition, with the structural equation modeling (SEM), the researchers studied the factors of perceived ease of use and perceived usefulness (Technology Acceptance Model) to describe a causal relationship with attitude structural equation modeling (SEM) to determine the causal and path relationship.

2.7. The Service Providers of Co-Processing in Cement Kilns in Thailand

Waste disposal is a challenging issue due to the increasing amount of waste generated by the population and daily activities [66], specifically, industrial waste produced by sewage or unused industrial products. Waste disposal requires specially assembled machinery and technology, with high investment costs as well as standardized management [32], leading to higher outsourcing costs than normal waste. Industrial waste disposal businesses in Thailand are highly competitive due to such high bills. According to the data from the Department of industrial works [19] and Commission [18], the existing nine service providers of co-processing waste disposal in cement are cement factories, with the majority of them located in Central Thailand. Currently, there are 137 service providers across the country’s Department of industrial works [67], including central waste treatment plants, covering central waste incineration plants (Incineration/Co-incineration). Therefore, the goal of this research is to design services according to customer needs to achieve sustainability in co-processing industrial waste disposal services under intense competition.

2.8. Hypothesis Development

Perceived ease of use is the simplicity of sending industrial waste disposal in the cement kilns because the group of service providers for co-processing industrial waste disposal service in cement kilns has a continually standard developed management system. The Department of industrial works has given service providers access to the Auto E-License system. Customers can use such a system to automatically send waste or unused materials out of the factory via an electronic system (Auto E-License). Through this way, customers can remove industrial waste disposal from the factory by reducing the waiting time for approval from the Department of Factory from 30 days in the case of ordinary permission to only two working days due to the standard waste disposal service specified by the Department of Factory, with a reference to the list of service providers. The list of service providers can refer to the Department of industrial works [67].

Perceived usefulness: the strength of co-processing industrial waste disposal in cement kilns is its perceived usefulness. According to Viczek et al. [2], industrial waste disposal in the cement kilns would be the raw materials for production, leaving no residues (zero wastes to landfills). Stafford et al. [68] demonstrated that the management of co-processing industrial waste disposal is standard. Furthermore, it is a disposal method of zero waste to landfills. Thus, it can simultaneously recycle industrial waste into renewable energy and industrial waste disposal as the delivery of waste disposal by the Auto E-License system interacts positively with perceived usefulness.

Subjective norm: According to Bukhari et al. [41], this is a personal decision based on other related parties, like a group of people or close friends, whereas Wan et al. [53] demonstrated that social norms and attitudes influence intention. Weber et al. [50] added that a social norm is a social drive to make something conformable or unacceptable.

Attitudes toward service use: Tu et al. [49] stated that purchase intention is an opportunity caused by personal factors linked with attitudes and social norms. Ali et al. [54] emphasized that attitudes are feelings depending on individual factors or circumstances.

Price: According to Kwok et al. [34], one of the marketing mixes is the price factor. It accomplishes this by setting prices that cover the business’s cost and added profits. For the highest profitability achievement, the set price should allow for customer acceptance. When a customer recognizes the value of the goods or services they receive, they feel that the price is worthwhile. However, the disposal cost must be profitable for the disposal service providers and affordable for the service users.

Place: The industrial waste disposal service organizations are the cement kiln companies, where most in Thailand have factories located in the central region, not near the industrial sites of the customers’ companies that produce the waste and scatters in all regions of the country. On the contrary, considering the positive aspects of the location, the service providers have factories located in the country’s central region with convenient transportation routes. In addition, many customer factories still use the service to transport industrial waste from distant locations.

Promotion: The promotion factor needs to be simultaneously combined with the price and place of service providers to attract service users through a communication channel to induce customers to utilize the service [43,45,69], stating that building a business image, not having an impact on the environment, potentially increases the customers’ confidence and their intention to use the service.

People of Service providers: Oflac et al. [45] stated that the personnel of service providers are an indispensable part of any organization that requires development. Daily servicing and continuous learning by those specialized people potentially builds credibility for customers and distinctiveness for business. Koľveková et al. [70] illustrated that customers considered the selection of a service based on the provider’s personnel. Hence, service providers should focus on service quality strategies.

Physical: physical factors, such as products or the service provider’s infrastructure systems, are tangible for customers. They potentially demonstrate the service quality and reflect the service provider image (Kwok et al.) [34]. Considering the co-processing industrial waste disposal service in cement kilns, it is a technology widely accepted and employed across the globe.

Based on the literature review in

Table 1. Research hypotheses.

Hypothesis/Relationship		Previous Studies
H1:	Perceived ease of use -> Perceived Usefulness	Cheunkamon et al. [51], Tian et al. [71], Alam et al. [47], Al-Rahmi et al. [72]
H2:	Perceived Usefulness -> Attitude	Cheunkamon et al. [51], Alam et al. [47], Tu et al. [49], Chin et al. [60], Al-Rahmi et al. [72], Mustafa et al. [65]
H3:	Perceived ease of use -> Attitude	Alam et al. [47], Chin et al. [60], Tu et al. [49], Mustafa et al. [65], Müller [73]
H4:	Subjective norm -> Attitude	Bukhari et al. [41], Wan et al. [53], Weber et al. [50]
H5:	Subjective norm -> Intention to use	Tu et al. [49], Alam et al. [47]
H6:	Attitude -> Intention to use	Cheunkamon et al. [51], Tu et al. [49], Ali et al. [54]
H7:	Price -> Intention to use	Bukhari et al. [41], Sheau-Ting et al. [43], Menegaki [44], Oflac et al. [45], Kwok et al. [34], Ghalehkhondabi et al. [28]
H8:	Place -> Intention to use	Sheau-Ting et al. [43], Menegaki [44]
H9:	Promotion -> Intention to use	Sheau-Ting et al. [43], Oflac et al. [45], Choi et al. [69]
H10:	People -> Intention to use	Oflac et al. [45], Koľveková et al. [70], Chonsalasin et al. [74]
H11:	Physical -> Intention to use	Kwok et al. [34], Salman et al. [35]

, price, place of service provider, promotion, people, physical, attitude, subject norm, perceived ease of use, and perceived usefulness are factors to be evaluated in this research. At this point, the connection between each factor was analyzed and hypotheses were formulated based on the research objectives for factors impacting the selection of industrial waste disposal service in cement kilns. The following are the results and details of the hypothesis.

Hypothesis 1 (H1).

Perceived ease of use influences perceived usefulness positively.

Hypothesis 2 (H2).

Perceived usefulness has a positive impact on attitudes toward the service.

Hypothesis 3 (H3).

Perceived ease of use influences attitudes toward service utilization positively.

Hypothesis 4 (H4).

Subjective norm has a positive effect on attitudes toward service use.

Hypothesis 5 (H5).

Subjective norm has a favorable impact on the service use intention.

Hypothesis 6 (H6).

An attitude toward service utilization has a positive influence on the intention to use service.

Hypothesis 7 (H7).

Price has a positive influence on the intention to use the service.

Hypothesis 8 (H8).

Place influences the intention to use service positively.

Hypothesis 9 (H9).

Promotion has a positive impact on the intention to use the service.

Hypothesis 10 (H10).

People have a positive influence on the intention to use the service.

Hypothesis 11 (H11).

Physical influences the intention to use service positively.

3. Materials and Methods

3.1. Data Collection

An online questionnaire in the Google form was used in this survey. The Customer Service Department and sales representative’s questionnaires (

Table A1. Questionnaires.

Variables	Indicators
PRI1	The cost of transportation is reasonable for the transportation distance.
PRI2	The price of industrial waste disposal is appropriate when compared to quality and service.
PR13	Price is the first consideration in selecting an industrial waste disposal service carrier.
PRI4	Price is the first consideration in selecting the industrial waste disposal service provider.
PLA1	Transportation distance is one of the factors you consider selecting the service use.
PLA2	Consider selecting the service, you consider selecting the waste disposal nearby your entrepreneurs.
PLA3	The place of co-processing waste disposal in cement kilns of service providers is appropriate.
PROM1	Communicate the industrial waste disposal types which can be disposed of in the co-processing in cement kilns.
PROM2	Helpful advice on the co-processing industrial waste disposal in cement kilns.
PROM3	Auto E-License system can reduce the time for requesting the permission of taking industrial waste disposal out of the factory.
PEO1	Sales representatives have knowledge and expertise in industrial waste management.
PEO2	Carriers are skillful and professional.
PEO3	Employees of the disposal plant are skillful and professional.
PHY1	The waste disposal service providers can help and advice on industrial waste disposal.
PHY2	The disposal plants have physical characteristics ready to dispose of industrial waste.
PHY3	The industrial waste transportation system is ready, and the monitoring system of vehicle conditions is available.
ITU1	Have the intention to continue using the co-processing industrial waste disposal service in cement kilns.
ITU2	Have the intention to use the co-processing industrial waste disposal service in cement kilns in the future.
ITU3	Will return to use the co-processing industrial waste disposal service in cement kilns.
ATT1	The service providers of co-processing in cement kilns can remove industrial waste effectively.
ATT2	The use of co-processing industrial waste disposal in cement kilns is more cost-effective than other industrial waste disposal methods.
ATT3	Your company will have a better image of waste disposal if you use co-processing industrial waste disposal in cement kilns.
SJN1	Choose the co-processing if the business groups similar to yours chose it.
SJN2	Choose the co-processing if the entrepreneurs nearby yours chose it.
SJN3	Choose co-processing if its technology is in a trend of a large number of users.
PU1	Can take the industrial waste out of the factory with the Auto E-License system.
PU2	Can reuse the industrial waste as renewable energy.
PU3	Can manage Zero wastes to landfill.
PEOU1	Have a clear standard for industrial waste disposal.
PEOU2	Reduce the time for requesting permission to take the industrial waste with Auto E-License system.
PEOU3	Ease of coordination and asking for industrial waste disposal information.

) via email and application lines to customers were acquired from the customer list of an industrial waste disposal service provider, who is the leader in co-processing waste management in cement kilns. The target groups are current customers and those who employ the service, including industrial plants, warehouses, companies, government agencies, and educational institutions covering all regions of Thailand. The surveyed data collection was between April 2021 and July 2021.

3.2. Data Analysis

First, the literature reviews yielded a model specification [75] which included the following factors: price, place, promotion, people and physical, attitude, subject norm, perceived ease of use, and perceived usefulness in-service use.

Second, take the factors to create a path diagram showing the correlation between the variables relevant to the research hypotheses to test the causal relationship [33], as depicted in Figure 1.

Third, checking the data distribution from the questionnaire according to Kline [76], the skewness should be less than three, and the kurtosis must be less than ten. The maximum likelihood estimate can estimate the model parameters in the case of the normally distributed data.

Fourth, model estimate [51]. In this research, the regression coefficient estimation, factor loadings, variance, and covariance were analyzed with the program Mplus seven using maximum likelihood estimate [51,75,77,78,79].

Fifth, check the goodness-of-fit model, as detailed in

Table 2. Model fit indicated.

Model Fit Indicated	Target
${\chi }^2$/df	<3
SRMR	0.05
RMSEA	0.05
CFI	>0.95
TLI	>0.95

Note: Reference; [63,75,77,79,80,81,82,83].

, to explain how well the hypothesis model is consistent with the empirical data by examining the following values, including (1) the ratio between (${\chi }^2$/df) should not exceed 3, (2) the Root Mean Square Error of Approximation (RMSEA) should be less than 0.05, (3) Standardized Root Mean Square Residual (SRMR) should not exceed 0.05, (4) Comparative Fit Index (CFI) should be greater than 0.95, and (5) the test results of goodness-of-fit model with Tucker–Lewis Fit Index (TLI) must be greater than 0.95. If the results of the goodness-of-fit model can pass all requirements, it can confirm consistency between the model and the empirical data.

The data from the questionnaire requires reliability and validity test before analyzing with the structural equation modeling. The reliability test employed Cronbach’s alpha, as shown in Equation (1) [84], assessed the internal consistency. In other words, it checke whether the respondents consistently answer the same subjects or not. The responses are consistent if the reliability test result is more than 0.7 [85]. The formula is as follows:

$$\alpha =\frac{\mathrm{K}}{\mathrm{K}-1} (1-\frac{{{\displaystyle \sum }}_{i=1}^k{\sigma }_{yi}^2}{{\sigma }_x^2})$$

(1)

where K denotes the number, ${\sigma }_x^2$ represents the mean of the variance of the questions, and ${\sigma }_{yi}^2$, shows the mean of the covariance of the questions.

The next process is to examine the Average Variance Extraction (AVE) and Constructed Reliability (CR) [86], which are the index values for the same factor’s capacity. When checking the threshold, the CR value should be higher than 0.7 and the AVE value should be greater than 0.5 [58,85]. The formulas are as follows:

$$\mathrm{AVE}=\frac{\left({\sum }_{i=1}^{\mathrm{n}}{\mathsf{\lambda }}_i\right)}{\mathrm{n}}$$

(2)

$$\mathrm{CR}=\frac{{\left({\sum }_{i=1}^p{\mathsf{\lambda }}_i\right)}^2}{{\left({\sum }_{i=1}^p{\mathsf{\lambda }}_i\right)}^2+{\sum }_i^p\mathrm{V}\left(\delta \right)}$$

(3)

where ${\mathsf{\lambda }}_i$ = completely standardized loading for the i_th indicator, V(δ_i) = variance of the error term for the i_th indicator, p = number of indicators.

4. Results

4.1. Descriptive Statistics

According to the data from

Table 3. Sample characteristics.

Characteristics	Category	Frequency
Gender	Male	687
	Female	564
Age	20–30	293
	31–40	489
	41–50	369
	51–60	98
	>61	2
Education	High school/vocational	4
	Vocational Certificate/Technical Certificate/Diploma	91
	Bachelor’s degree	791
	Master’s degree	362
	Doctoral degree	2

and

Table 4. Descriptive statistics.

Item	Adapted from	Variables	Mean	SD	Skewness	Kurtosis
Price	[35]	PRI1	4.02	0.023	−0.583	0.111
	[39]	PRI2	4.11	0.026	−0.654	0.286
	[45]	PR13	3.95	0.027	−0.826	0.416
	[87]	PR14	3.98	0.024	−0.829	0.375
Place	[88]	PLA1	3.94	0.024	−0.609	0.102
	[89]	PLA2	3.92	0.029	−0.397	−0.696
	[34]	PLA3	3.73	0.026	−0.710	0.688
Promotion	[90]	PROM1	3.99	0.023	−0.660	0.654
	[44]	PROM2	3.98	0.031	−0.869	1.565
	[43]	PROM3	4.25	0.025	−0.906	0.638
	[88]	PEO1	4.20	0.020	−0.481	−0.407
People	[88]	PEO2	4.18	0.028	−0.492	−0.534
	[88]	PEO3	4.20	0.020	−0.447	−0.494
Physical	[43]	PHY1	4.28	0.020	−0.614	−0.102
	[91]	PHY2	4.29	0.021	−0.771	0.217
	[92]	PHY3	4.25	0.023	−0.713	0.087
Intention to use co-processing	[55]	ITU1	4.22	0.030	−0.495	−0.536
	[47]	ITU2	4.30	0.027	−0.658	−0.465
	[54]	ITU3	4.23	0.024	−0.557	−0.399
Attitude	[6]	ATT1	4.27	0.020	−0.52	−0.475
	[32]	ATT2	4.21	0.021	−0.564	−0.308
	[74]	ATT3	4.23	0.020	−0.47	−0.51
Subjective Norm	[53]	SJN1	4.12	0.022	−0.695	0.508
	[53]	SJN2	4.02	0.025	−0.847	0.685
	[53]	SJN3	4.07	0.024	−0.650	0.076
Perceived usefulness	[61]	PU1	4.20	0.023	−0.898	0.747
	[20]	PU2	4.33	0.022	−0.993	0.805
	[1]	PU3	4.39	0.020	−0.968	0.483
Perceived Ease of use	[68]	PEOU1	4.28	0.020	−0.646	−0.123
	[51]	PEOU2	4.22	0.023	−1.017	1.451
	[74]	PEOU3	4.21	0.021	−0.420	−0.796

, the skewness value should be less than three and the kurtosis must be less than 10 when testing the data distribution. The skewness for all observed values ranged from −0.796 to 1.565, and the kurtosis values ranged from 0.411 to 0.896. Due to the acceptable range, it confirms the normal distribution data and potentially performs parameter estimation in the subsequent model based on these values. The data from the questionnaire, the complete questionnaires obtained from 1251 samples were divided by their characteristics into 687 (55%) males and 564 (45%) females. For respondents’ age, 293 (23%) respondents were between the ages of 20 and 30, 489 (39%) were between the ages of 31, and 40, 369 (29%) were between the ages of 41–50, 98 (7.8%) were between the ages of 51–60, and 2 (0.2%) were over 61. For the education level of the respondents, 4 respondents (0.3%) graduated from high school/vocational certificate, 91 samples (7%), 362 samples (29%) had vocational diploma/technical certificate/diploma, 791 samples (63%) had a bachelor’s degree, 362 samples (29%) had a Master’s degree, and 2 samples (0.2%) had a Doctoral degree.

4.2. Reliability and Validity

The test findings for each factor found the Cronbach’s alpha values between 0.739 and 0.931, confirming the data consistency. The test results of each factor showed the CR value ranging from 0.969 to 0.995, and the AVE value was between 0.912 and 0.985. Thus, the statistical results confirmed that the data passed the criteria.

4.3. Measurement Model

Table 5. Measurement model results.

Item	Variables	Loading	t-Value	Error- Variance	Cronbach’s Alpha	CR	AVE
Price	PRI1	0.757	45.267	0.017	0.820	0.992	0.971
	PRI2	0.819	60.779	0.013
	PR13	0.772	49.749	0.016
	PR14	0.610	29.523	0.021
Place	PLA1	0.721	25.548	0.027	0.705	0.989	0.969
	PLA2	0.742	22.395	0.031
	PLA3	0.712	22.647	0.027
Promotion	PROM1	0.627	16.316	0.032	0.751	0.969	0.912
	PROM2	0.511	11.899	0.035
	PROM3	0.621	18.563	0.033
	PEO1	0.722	24.585	0.026	0.864	0.984	0.952
People	PEO2	0.718	23.932	0.025
	PEO3	0.709	21.782	0.026
Physical	PHY1	0.851	86.939	0.010	0.859	0.995	0.984
	PHY2	0.790	64.474	0.012
	PHY3	0.833	78.808	0.011
Intention to use co-processing	ITU1	0.896	96.808	0.009	0.931	0.994	0.985
	ITU2	0.825	72.014	0.011
	ITU3	0.789	61.027	0.013
Attitude	ATT1	0.781	40.295	0.019	0.874	0.988	0.966
	ATT2	0.779	46.644	0.017
	ATT3	0.695	22.541	0.024
Subjective Norm	SJN1	0.832	74.880	0.011	0.883	0.994	0.981
	SJN2	0.760	52.996	0.014
	SJN3	0.814	67.667	0.012
Perceived usefulness	PU1	0.697	33.056	0.021	0.831	0.988	0.966
	PU2	0.679	38.047	0.018
	PU3	0.812	45.475	0.018
Perceived Ease of use	PEOU1	0.808	41.809	0.019	0.851	0.986	0.961
	PEOU2	0.706	31.899	0.022
	PEOU3	0.617	29.306	0.021

depicts the results of the data measurement model from the questionnaires comprising 10 factors and 31 observed variables. The obtained findings confirmed the goodness-of-fit of the structural equation model of factors, influencing the intention to use co-processing industrial waste disposal service in cement kilns.

The measurement model results can show that the model is consistent with the empirical data and confirms that the exogenous variables from the questionnaire can indicate the independent variables obtained from the literature review. All the observed variables are statistically significant (p < 0.001). The first three highest factor loadings and the least factor loadings are as follows:

• The factors of intention to use industrial waste disposal service in cement kilns measured from three observed variables (ITU1, ITU2, ITU3) found that the variable ITU1, “Have the intention to continue using co-processing industrial waste disposal in cement kilns”, had the highest factor loading value (Factor loading = 0.896).
• The physical factor of the industrial waste disposal service provider measured by three observed variables (PHY1, PHY2, PHY3) found that the variable PHY1 “The waste disposal service providers can help and give advice in industrial waste disposal”, had the second factor loading value (Factor loading = 0.851).
• The physical factor of the industrial waste disposal service provider, measured by three observed variables (PHY1, PHY2, PHY3) found that the variable PHY 3 “The industrial waste transportation system is ready and the monitoring system of vehicle condition is available” had the third factor loading (Factor loading = 0.833).
• The factor with the least factor loadings is the price factor of the industrial waste disposal cost, measured by four observable variables (PRI1, PRI2, PRI3, PRI4), which found that the variable PRI4 “Price is the first consideration in selecting the industrial waste disposal service provider” had the lowest factor loading (Factor loading = 0.610).

4.4. Structural Model

The results are shown in Figure 2 and

Table 6. Testing hypotheses results with structural equation modeling.

Hypothesis	Relationship	Loading	Standard Error	t-Value	Result
H1	Perceived ease of use -> Perceived Usefulness	0.814 **	0.023	35.205	Supported
H2	Perceived Usefulness -> Attitude	0.693 **	0.096	7.239	Supported
H3	Perceived ease of use -> Attitude	0.388 **	0.082	4.753	Supported
H4	Subjective norm -> Attitude	0.162 **	0.042	3.864	Supported
H5	Subjective norm -> Intention to use	0.054	0.457	0.118	Not Supported
H6	Attitude -> Intention to use	0.532 **	0.106	4.997	Supported
H7	Price -> Intention to use	0.136 **	0.083	1.635	Supported
H8	Place -> Intention to use	0.018 **	0.010	1.855	Supported
H9	Promotion -> Intention to use	0.326 **	0.184	1.775	Supported
H10	People -> Intention to use	0.748 **	0.359	2.081	Supported
H11	Physical -> Intention to use	0.386 **	0.125	3.088	Supported

Note: ** Significant at p < 0.05.

. The values of the model fit are as follows: = 745.239, df = 287, p < 0.01, ${\chi }^2$/df = 2.595, RMSEA = 0.036, CFI = 0.980, TLI = 0.968, and SRMR = 0.031. Therefore, this can confirm that the empirical data matches the hypothetical model. Based on the results of eleven research hypotheses, the hypothesis testing results found that the perceived ease of use has a positive influence on the perceived usefulness (Factor loading = 0.814, p < 0.05), supporting the Hypothesis H1. Instantaneously, the perceived usefulness of service use had a positive influence on the attitude towards service use (Factor loading = 0.693, p < 0.05). The obtained result supported the Hypothesis H2. This is in line with the attitude towards service use, which was positively influenced by the perceived ease of use (Factor loading = 0.388, p < 0.05), supporting the Hypothesis H3. Regarding the social norm hypothesis and attitude towards the intention to use service, the social norm factor had a positive influence on the customer attitude towards the service providers (Factor loading = 0.162, p < 0.05), supporting the H4 hypothesis, but it was not consistent with the intention to use factor that was also positively influenced by the social norm factor (Factor loading = 0.054, p > 0.05), but not statistically significant, thus rejecting the Hypothesis H5. From the hypothesis on attitude to intention to use service, it was found that it was positively influenced by the attitude towards service use (Factor loading = 0.532, p < 0.05), thus supporting the Hypothesis H6. The price factor had a positive influence on the intention to use the service (Factor loading = 0.136, p < 0.05), thus supporting the Hypothesis H7. This was consistent with the factor of service provider’s place, which had a positive influence on intention to use service (Factor loading = 0.018, p < 0.05), thus supporting the hypothesis H8. This was in line with the factor of service providers’ promotion which had a positive influence on intention to use service (Factor loading = 0.326, p < 0.05), thus supporting the Hypothesis H9. In addition, the intention to use factor was positively influenced by the factor of people of the co-processing service providers in cement kilns (Factor loading = 0.748, p < 0.05), thus supporting the Hypothesis H10. In addition, the service provider’s physical factor had a positive influence on the intention to use service (Factor loading = 0.386, p < 0.05), thus supporting the Hypothesis H11.

5. Discussion

Hypothesis H1, perceived ease of use of the service, had a positive influence on the perceived usefulness with a factor loading = 0.814. Therefore, the findings of this research confirm the results in line with [47,51,71], which revealed that the perceived ease of use factor has a positive impact on the perceived usefulness factor.

Hypothesis H2, perceived usefulness, has a beneficial influence on attitudes toward service providers, with factor loading = 0.693. The research results are the same as the customers’ recommendations from the questionnaire, providing that “co-processing industrial waste disposal can assist reduce the usual fuel consumption of cement kilns”. In the terms of perceived usefulness and attitude, the association between perceived usefulness and attitude was also consistent with the work of [49,65,73]. Hence, it can be concluded that this study results potentially confirmed the findings were consistent with [47,49,51,60,65,72], discovering that the perceived usefulness influences attitude factor positively.

Hypothesis H3, perceived ease of use, has a positive influence on attitudes toward service use with a factor loading of 0.388. From the questionnaires, the customers commented that “the first choice for selection of the co-processing waste disposal in cement kilns is good servicing with full-service management”. As a result, the study verified the findings consistent with [47,49,60,65,73], which revealed that the perceived ease of use factor has a positive influence on the attitude factor.

Hypothesis H4 aimed to determine whether a social norm has a positive influence on attitudes toward service providers. With factor loading = 0.16., the customers’ opinions from questionnaires stated that “this waste disposal method can compete with others”. Therefore, the result of this study confirmed the findings of [41,50,53], which discovered that a social norm has a positive impact on attitude.

Hypothesis H5, subjective norm, influences the intention to use service positively with a factor loading of 0.054. The results revealed that it positively influenced the intention to use the service, but it was not statistically significant, as customers want to hire a disposal contractor who can accept all sorts of customers’ waste since the waste disposal or unused industrial waste contain various types, including office waste, canteen waste, and waste. In other words, the decision to utilize the service depends on each company, not the subjective norms mentioned above. When considering the group of waste disposal service providers in the country, there are many groups such as landfills, kilns, and waste power plants. Some brokers accept all forms of waste, sort it, and find a legal disposal contractor, while industrial waste disposal in cement kilns cannot. Thus, the study results contradict [47,49], and it was discovered that the social norm is statistically significant and influences intention to use.

Hypothesis H6: attitude toward service application has a positive influence on the intention to use service. According to the study results, the relationship between these two factors had a factor loading = 0.532, relevant to the customer feedback from the questionnaire stating that “this approach is reliable, and take waste to beneficially utilize in cement kilns”. Therefore, it can conclude that the results of this study confirmed the findings of [49,51,54], discovering that the intention to use had a causal connection with the attitude factor.

Hypothesis H7, with factor loading = 0.136, was that the price factor was statistically significant on the intention to use the service. Consistent with the customer’s proposal and suggestions from the questionnaire, it stated that “In the case of covering the customer’s waste, the high price is acceptable”. Studies have shown that price was statistically important with in-service decision-making. Relevant to [46,93], this research finding confirmed the results consistent with [28,34,41], discovering that the price factor influences the intention to use.

Hypothesis H8, the place factor, has a positive effect on the intention to use the service, with factor loading = 0.018. Similar to customers’ recommendations from the questionnaire, they denoted to “set up an intermediary, organize marketing promotion to reduce transportation distances, and leverage every cement plant in Thailand to benefit every kiln, every trademark”. As a result, it concluded that the study result confirmed the findings compatible with [43,44,46], discovering that the place of the service providers influences the intention to utilize service.

Hypothesis H9, Promotion, had a positive influence on the intention to use service, with factor loading = 0.326. Similar to the recommendations from the customer in the questionnaire “the service providers should promote the co-processing waste disposal via online to increase the customer perceptions and understandings”. Therefore, it can be concluded that the finding of this research confirmed the results consistent with [43,44,45,69], discovering that promotion has a positive impact on the intention to use factor.

Hypothesis H10, the study result, indicated that people have a positive influence on the intention to use service, with factor loading = 0.748. Comparable with the customers’ recommendation from questionnaires, they illustrated that “there are various waste types. In terms of the quality of the sludge itself, industrial waste disposal in a cement kiln is limited. Hence, the sales representatives have to closely collaborate with the factories to dispose of industrial waste without any obstacles”. The mentioned reasons confirmed that the intention to use is positively influenced by the service providers’ people. As a result, it can be concluded that this research finding confirmed similar results with [45,70,94], discovering that people influenced the intention to use factor.

Hypothesis H11, Physical, has a positive impact on the intention to utilize service, with factor loading = 0.386. This fact is also related to customer recommendations from a questionnaire, stating that “call for the service provider to receive a variety of waste and a higher daily amount”. Therefore, it can be concluded that the research result confirmed the consistent findings with [34,35,94], which found an effect between physical factors and the intention to use.

6. Conclusions

The study found that customers’ intention to utilize the service from factor customers gave importance to being a waste disposal with zero waste to landfill in cement kiln and used the service because of its relevance to the circular economy by using wastes as renewable fuels in cement kilns. The procedures for building a strategic plan to employ cement kilns’ industrial waste disposal services are as follows.

First, the research results revealed that customers perceived ease of use of service offered by the service providers as starting from the clear standard for considering industrial waste, allowing customers to know the waste types that can or cannot be disposed of in the cement kilns. The communication channels and their coordination are convenient with the service providers’ standardized management for reducing the request time for permission to remove the customers’ industrial waste from the factory with the Auto E-License system. Only a few service providers of co-processing industrial waste disposal service in cement kilns receive this privilege. The system allows customers to reduce the time it takes to remove waste out of the factory from one month to less than two business days. The study results confirmed that the mentioned reasons have a positive influence on the perceived usefulness of service use. Consequently, service providers must focus on the business strategy, quick response, and continually improve developmental support and customer assistance. When customers perceive ease of use, they will get perceived usefulness from applying the service to maintain existing customers and acquire new customers.

Second, customers perceived the usefulness of industrial waste disposal services in cement kilns because the industrial waste disposal can be renewable fuel and will not cause waste to be disposed of (zero waste to landfill), according to the research findings. Customers’ attitudes toward using the service are influenced by such strength. When more waste is disposed of in the cement kilns, it helps to highly reduce the waste from landfills and the environmental impact, as waste disposal in cement kilns has environmentally friendly disposal. Based on research findings, service providers have to use a strategy building that emphasizes distinctiveness by focusing on niche markets that prefer to dispose of waste in a zero-waste-to-landfill method. Waste disposal in cement kilns can be called a win-win solution, since the customers can get rid of waste without harming the environment, and service providers benefit from industrial waste as renewable fuels. Moreover, the research results also indicate that those profits must be promoted to acknowledge customers’ strong points, which are considered the selling point of this disposal method to expand the increasing number of customers.

Third, the perceived ease of use of the service and the perceived usefulness from utilizing the service, as mentioned above, had a beneficial influence on positive attitudes toward using the service. Customers’ attitude toward the effective method of co-processing industrial waste disposal in cement kilns and the service provider’s image in terms of waste management was confirmed by the experiment results. As a result, to enhance their business growth, service providers have to use a proactive growth business strategy to develop services and communicate strengths to customers to gain their confidence, expand the number of customers, and build further business growth.

Fourth, attitude is the most influential factor positively influencing intention to use service according to the research results. From the various strategies mentioned to increase customers, service providers must keep in mind and pay attention to maintaining the loyalty of old customers. It is based on actual service satisfaction when compared to the service expectations, as well as the service values. Due to the current economic recession, service providers must use a stable business strategy. Thus, it is essential to maintain the existing customer base by improving the efficiency of service providers’ internal processes.

This research is not with limitations. Although considerable factors were uncovered in terms of their influence on the selection of industrial waste disposal service in cement kilns in Thailand, other differently relevant factor, such as technology acceptances of industrial waste disposal service, transport distances, trust in technology, and customer’s trust in industrial waste disposal service providers, should be considered for further research in other regions of service providers from different countries. Therefore, future research may use the current study as a basis for improving the investigation of factors, influencing the selection of industrial waste disposal service in cement kilns by additionally considering the above-mentioned factors.