A Hybrid, Data-Driven Causality Exploration Method for Exploring the Key Factors Affecting Mobile Payment Usage Intention

: Several methodologies for academically exploring causality have been addressed in recent years. The decision-making trial and evaluation laboratory (DEMATEL), one of the multiple criteria decision-making (MCDM) techniques, relies on expert judgements to construct an inﬂuential network relation map (INRM), revealing the mutual causes and effects of the criteria and dimensions for presentation of the results in a visual manner. The interactional impacts may be evaluated without considering the presumed hypotheses. The DEMATEL has been successfully utilized to assist in complex decision-making problems in various contexts. However, there is controversy about the reliance upon expert judgements, which could be subjective. Thus, this study seeks to overcome this dispute by developing a data-driven, concept-based novel hybrid model which the authors call SEM-DEMATEL. The model ﬁrst constructs the direct effects between indicators based on structural equation modeling (SEM) and then utilizes DEMATEL to conﬁrm the interdependence among the variables and identify their causes and effects. Finally, an empirical study exploring the key factors affecting mobile payment usage intention is further conducted to demonstrate the feasibility, validity, and reliability of the novel SEM-DEMATEL research approach. The results identify that the perceived value is the key inﬂuencing indicator of m-payment usage intention, and the objectivity and efﬁciency of the research results are compared.


Introduction
The importance of effective and precise exploration and clarification of causal relationships among variables or objects is undoubtedly essential in many branches of science. The effectiveness of decision-making is reliant upon accurate and efficacious causal analysis. Mathematical and statistical techniques have been applied to develop more efficient methods to understand and map out casual inferences [1]. These days, numerous causality exploration methods have been utilized in research studies, such as interpretive structural modeling (ISM) [2,3], Granger causality [4,5], transfer entropy [6,7], fuzzy cognitive mapping (FCM) [8], decision-making trial and evaluation laboratory (DEMATEL) [9,10], and structural equation modeling (SEM) [11,12]. Each method has its own specific features, advantages, and respective limitations. Among these, two approaches have become more Perceived value is defined as the consumer's overall evaluation of perceived benefits [57]. It has been proven that perceived value has a significantly positive effect on consumers' usage intention in the m-payment market [48,58]. Shaw and Sergueeva [59] employed the modified unified theory of acceptance and use of technology 2 (UTAUT2) model to explore the factors affecting usage intention in the Canadian m-payment market to find that hedonic motivation and perceived value greatly influenced usage intention. Liu et al. [48] regarded the factors of perceived value, perceived benefit, and perceived risk as the three critical criteria for m-payment usage intention.
Studies have been carried out to further comprehend consumers' behavioral intention and to explore and classify the essential elements of perceived value in relation to various markets or products [60]. Sheth et al. [61] regarded perceived value as a combination of economic, psychological and sociological perceptions and marketing aspects. They argued that perceived value can be broken down into five categories: functional, social, emotional, epistemic, and conditional value. Furthermore, one research investigated the retail market and categorized perceived value into four segments of emotional value, social value, quality, and price [62]. Holbrook [63] divided perceived value into three categories: self-oriented and other-oriented, intrinsic and extrinsic nature, and active and passive. In the studies examining the perceived value of e-commerce consumers, Overby and Lee [64] emphasized the significance of price saving, time saving, selection, and service; Peng and Liang [65] sorted the perceived value into social value, emotional value, price value, and functional value.
Gamification has been defined as "the use of game design elements, in a non-game context" [66] (p.1) and has been confirmed to increase the user's engagement and encourage purchasing [67]. Over the last decade, gamification has boomed and attracted a great deal of attention both academically and practically in many fields, such as learning and education [68], human resource management [69], marketing [70], and many others, certainly including the m-payment field. Wong et al. [46] emphasized that the effective application of gamification could stimulate the senior generation's adoption of mobile payment, with perceived enjoyment being the key factor to improve the effectiveness of gamification. Bùi and Bùi [42] used a modified UTAUT model to examine the consumer's usage intention of m-payment. They considered the factors of performance expectancy, expected effort, trust, and gamification to have a substantial impact on usage intention.
Theories developed to explain the increased popularity and consumer acceptance and motivation to use of technology include the technology acceptance model (TAM) [76], innovation of diffusion theory (IDT) [77], theory of planned behavior (TPB) [78], UTAUT [79], and many others. These have been combined [80] or extended one theory [81] to develop different research models. Among those theories, UTAUT, proposed by Venkatesh et al. [82], suggests that consumer behavior related to the adoption of new technological products or systems is impacted by four indicators: performance expectancy, expected effort, social influence, and facilitating conditions. Christian et al. [50] investigated the Indonesian NFC m-payment market. They applied the TAM model to examine the causal relationships among the dimensions of e-service quality, perceived usefulness, NFC indicators, perceived ease of use, and usage intention and declared user behavioral intention to be significantly affected by the NFC indicators of e-service quality. Lin et al. [41] combined TAM and the innovation resistance theory (IRT) to scrutinize the Taiwanese m-payment market and argued that perceived usefulness and ease of use positively impact usage intention, and innovation resistance negatively influences perceived usefulness and perceived ease of use. de Sena Abrahão et al. [43] utilized a modified UTAUT to inspect the Brazilian m-payment users' motivation and identified that social influence, effort expectation, performance expectation, and perceived risk were crucial influential factors Additionally, personal innovativeness, referring to the consumer's individual characteristics for the application of new technological products [50], has also be verified as one of the essential influencing factors of technological product applications [83]. Furthermore, with the world so harshly affected by the spread of COVID-19 since 2020, infection prevention has led consumers to change their payment behavior [36] and adopt contactless payment mode. People's preference for a contactless payment method has facilitated growth of the m-payment market [37]. Thus, infection prevention is also deemed as one of the criteria motivating consumers' usage intention of mobile payment.
Based on the afore-mentioned studies, the three dimensions of motivation [41], perceived value [59], and gamification [42,46] are selected to evaluate mobile payment usage intention in this empirical study. In addition, the following criteria are included: gamification, which involves immersion-related features, achievement-related features, socialrelatedness features, satisfaction of autonomy needs, competency needs, and relatedness needs; the criteria of motivation, consisting of COVID-19 infection prevention, performance expectancy, expected effort, social influence, and personal innovativeness; and the criteria of perceived value, including functional value, emotional value and social value.
To summarize, three dimensions of motivation (D 1 ), gamification (D 2 ), and perceived value (D 3 ) were identified; 14 criteria were selected (C 1 to C 14 ) within these three dimensions to verify the causal relationships among the dimensions and criteria affecting m-payment during the COVID-19 pandemic.

Methodology
This section provides an overview of the innovative SEM-DEMATEL method, how it overcomes the disadvantages of the original SEM and DEMATEL approaches, and demonstrates the operational procedures of this new method.

The Fitness of Combining SEM with DEMATEL
SEM has been widely employed to test hypothesized research models by collecting large-sized samples. The advantages of this approach are that all of the latent, exogenous, and endogenous variables can be estimated contemporaneously, verifying the reciprocal causal relations, and, in particular, revealing all of the direct or indirect effects among the variables and latent indicators. However, the requirements of entangled parameter estimation for model fitting lead to over-modification of the original research model so that it can only be applicable for that specific sample rather than generalizable. Furthermore, the entire SEM research framework has to be hypothesized based on previous studies; the issue of corroborating the trustworthiness of these over-modified assumptions should be of concern. This argument is corroborated by an empirical case in this study.
The DEMATEL is often utilized to develop a total influence relation matrix, which can be used to identify the causes and effects of various indicators. Certainly, this method can be used to confirm the interdependence among the criteria and domains, converting their causal relations into an intelligible structure, for visualization of the complicated causal relations in matrices and graphs. The DEMATEL can simplify intricate situations and problems; however, data are collected from expert judgements, which can be subjective.
The advantages and limitations of these methods complement each other. Thus, this study develops a novel method that combines the benefits of SEM and DEMATEL, which should be more convincing and facilely applicable.

Innovations and Improvements of the SEM-DEMATEL Method
This SEM-DEMATEL method not only retains the advantages and characteristics of SEM and DEMATEL but also removes their limitations. First of all, the data are derived from the collected surveys. The reciprocal causal relations among criteria and dimensions are revealed by the SEM approach, an improvement over the subjective preferences of expert judgements, as frequently applied in DEMATEL. Secondly, the interdependence and causal relationships among the criteria and dimensions are clarified by the DEMATEL method. Thus, the restrictions on the essentialities of independent exogenous variables, literature-based hypothesized research framework, and complicated parameter estimation can be averted.

Analytical Processes of the SEM-DEMATEL Method
The proposed SEM-DEMATEL method combines both SEM and DEMATEL methods to reveal the direct path coefficients, confirm the interdependence of criteria and dimensions, and convert their causal relationships into an intelligible structure. In order to distinguish between the traditional DEMATEL approach and the SEM-DEMATEL method, please see Figure 1. The operation process of the traditional DEMATEL is indicated by the black dotted line, and the procedure of the novel method is shown by the green dotted line. The research method was divided into the following four phases:

1.
Preprocessing data: developing the survey, collecting and encoding data, and verifying the validity and reliability of the instrument; 2.
SEM approach: obtaining the direct relations by pairwise comparisons; 3.
DEMATEL approach: obtaining the degree of influence by infinite interactive influence; 4.
Constructing the INRM (influence network relationship map).  Traditional DEMATEL adopts expert judgements to assist in management decisionmaking and assumes that the viewpoints of experts from similar fields will be consistent; consensus is reached after digitizing the results of the experts' evaluations. The traditional DEMATEL can thus be regarded as a consensus-driven approach. In the new method proposed in this study, the data are directly collected by surveys. Thus, the validity and reliability tests of the instrument are required in this phase.

Step 1: Conduct Validity Tests
Haynes et al. [84] emphasized the significance of content validity and argued that the validity of a measurement illustrates the degree to which the instrument items are relevant to and representative of the measured constructs for a specific assessment purpose. The following process is used to confirm the validity in this study. First, the instrument is developed based on the literature. Second, a panel of experts is asked to assess the degree of representations of the variables; five participants are asked to confirm the clarity of the questionnaire content. Third, further pilot tests are executed and the Bartlett's test of sphericity conducted [85].

Phase 2: Obtaining the Direct Relations by Pairwise Comparisons
Step 1: Derive the Direct Influence Relations γ by Pairs Unlike the traditional DEMATEL method, which obtains the initial direct influence relations from the survey responses of the experts [88], this study is based on the datadriven concept where the direct influence relations γ between two criteria are derived by the application of Equations (1) and (2). Traditional DEMATEL adopts expert judgements to assist in management decisionmaking and assumes that the viewpoints of experts from similar fields will be consistent; consensus is reached after digitizing the results of the experts' evaluations. The traditional DEMATEL can thus be regarded as a consensus-driven approach. In the new method proposed in this study, the data are directly collected by surveys. Thus, the validity and reliability tests of the instrument are required in this phase.

Step 1: Conduct Validity Tests
Haynes et al. [84] emphasized the significance of content validity and argued that the validity of a measurement illustrates the degree to which the instrument items are relevant to and representative of the measured constructs for a specific assessment purpose. The following process is used to confirm the validity in this study. First, the instrument is developed based on the literature. Second, a panel of experts is asked to assess the degree of representations of the variables; five participants are asked to confirm the clarity of the questionnaire content. Third, further pilot tests are executed and the Bartlett's test of sphericity conducted [85].

Phase 2: Obtaining the Direct Relations by Pairwise Comparisons
Step 1: Derive the Direct Influence Relations γ by Pairs Unlike the traditional DEMATEL method, which obtains the initial direct influence relations from the survey responses of the experts [88], this study is based on the datadriven concept where the direct influence relations γ between two criteria are derived by the application of Equations (1) and (2). where η, γ, and ς, respectively, represent latent dependent variables, direct influence of variables, and error.
Step 2: Calculate the Direct Influence Relationship Matrix A Each criterion is set as independent or dependent variables, respectively, as shown in Matrix A. Thus, the direct influence relations for the whole research model can be obtained as follows: A case study is used for further illustration. For example, an evaluation system consists of three dimensions of D 1 , D 2 , and D 3 . First, D 1 is set as an independent variable and D 2 as a dependent variable. Using Equations (1) and (2), a 12 can be derived. Next, D 1 is set as an independent variable and D 3 as a dependent variable. Using Equations (1) and (2), a 13 can be derived, and so on. Six permutations of the direct influence relations (a 12, a 13, a 21, a 23, a 31, a 32 ) are systematically revealed, as depicted in Figure 2. The left side of Figure 2 shows the direct influence relations among three dimensions; the right side demonstrates the direct influence relationship matrix derived from the direct influence relations on the left.
where η, γ, and ς, respectively, represent latent dependent variables, direct influence of variables, and error.
Step 2: Calculate the Direct Influence Relationship Matrix A Each criterion is set as independent or dependent variables, respectively, as shown in Matrix A. Thus, the direct influence relations for the whole research model can be obtained as follows: 11 1 1 A case study is used for further illustration. For example, an evaluation system consists of three dimensions of D1, D2, and D3. First, D1 is set as an independent variable and D2 as a dependent variable. Using Equations (1) and (2), a12 can be derived. Next, D1 is set as an independent variable and D3 as a dependent variable. Using Equations (1) and (2), a13 can be derived, and so on. Six permutations of the direct influence relations (a12, a13, a21, a23, a31, a32) are systematically revealed, as depicted in Figure 2. The left side of Figure 2 shows the direct influence relations among three dimensions; the right side demonstrates the direct influence relationship matrix derived from the direct influence relations on the left.  Phase 3: Acquiring the Degree of Influence from the Infinite Interactive Influence Step 1: Develop the Normalized Direct Influence Relationship Matrix N Equations (4)-(6) are employed to de-unitize Matrix A to obtain the direct influence relationship Matrix N. Here, Ω represents the maximum sum of rows and columns; n ij denotes the normalized element from Matrix A.
Step 2: Obtain the Total Influence Relationship Matrix T Based on the Markov chain theory, the total influence relationship Matrix T can be generated by computing the infinite series of interactive effects for each criterion as follows: Step 3: Calculate the Influence Relations The influence relations of the criteria involve the influencing degree, the influenced degree, the total influence degree, and the net influence degree. In this step, Equation (9) is applied to accumulate the sum of the degree of influence of each criterion on the others, denoted as r.
The sum of the degree of influence that a criterion receives from the other criteria can be calculated using Equation (10) and is denoted as c.
The net influence degree, denoted as g, can be obtained by deducting the influenced degree from the influencing degree, as in Equations (11) and (12), and is recognized as the relation of that criterion. g = (g 1 , · · · , g i , · · · , g m ) = (g i ) m×1 (11) The total influence, denoted as p, can be derived by taking the sum of the influencing and influenced degrees received by a criterion from the other criteria, following Equations (13) and (14); this indicates the prominence of that criterion.
Phase 4: Drawing the Influence Network Relationship Map In this phase, the INRM is constructed according to the total influence and the net influence, as depicted as Figure 3. The horizontal and vertical axes represent the influence degree and the causality of the criteria, respectively; the black circle identifies (0, 0). In the next two steps, all of the criteria are projected onto the coordinate axis and the causality diagram constructed for comparison of the mutual influence, verified in the total relationship matrix. The arrow with the greater influence is shown on the right in Figure 3 [89]. The total influence, denoted as p, can be derived by taking the sum of the influencing and influenced degrees received by a criterion from the other criteria, following Equations (13) and (14); this indicates the prominence of that criterion.
, 1 ,2 , , Phase 4: Drawing the Influence Network Relationship Map In this phase, the INRM is constructed according to the total influence and the net influence, as depicted as Figure 3. The horizontal and vertical axes represent the influence degree and the causality of the criteria, respectively; the black circle identifies (0, 0). In the next two steps, all of the criteria are projected onto the coordinate axis and the causality diagram constructed for comparison of the mutual influence, verified in the total relationship matrix. The arrow with the greater influence is shown on the right in Figure 3 [89].

Empirical Example
With the proliferation of advanced mobile devices and wireless network technologies, the penetration of mobile phones and internet users have increased considerably. This has further driven the mobile-payment (m-payment) market. Massive opportunities and development have led to the prediction of an immense increase in the size of the global m-payment market from $1180 billion in 2019 to up to $4573.8 billion by 2023 [90]. Furthermore, the current world situation has led to people tending to maintain physical distancing and to consider contactless payment as a safer transaction method. The coronavirus pandemic has altered consumers' payment behavior [36] so that, despite the current severe economic downturn, the m-payment market remains prosperous, and this is expected to continue.
According to the Global Mobile Payment Users Report [91], the top five highest mobile-payment countries are China (81.1%), Denmark (40.9%), India (37.0%), South Korea (36.7), and Sweden (36.2%). Compared with other countries, China has by far the highest mobile payment adoption rate, with 81.1% of smartphone users. Research and Markets [36] forecasts that the mobile-payment transaction amount in China will reach RMB 777.5 trillion in 2020. The two dominant m-payment platforms in China are Alipay and WeChat Pay, respectively, with more than 1.2 billion and 1.151 billion active users. Doubtless, the mobile payment business in China is not only extremely prosperous and successful but

Empirical Example
With the proliferation of advanced mobile devices and wireless network technologies, the penetration of mobile phones and internet users have increased considerably. This has further driven the mobile-payment (m-payment) market. Massive opportunities and development have led to the prediction of an immense increase in the size of the global m-payment market from $1180 billion in 2019 to up to $4573.8 billion by 2023 [90]. Furthermore, the current world situation has led to people tending to maintain physical distancing and to consider contactless payment as a safer transaction method. The coronavirus pandemic has altered consumers' payment behavior [36] so that, despite the current severe economic downturn, the m-payment market remains prosperous, and this is expected to continue.
According to the Global Mobile Payment Users Report [91], the top five highest mobilepayment countries are China (81.1%), Denmark (40.9%), India (37.0%), South Korea (36.7%), and Sweden (36.2%). Compared with other countries, China has by far the highest mobile payment adoption rate, with 81.1% of smartphone users. Research and Markets [36] forecasts that the mobile-payment transaction amount in China will reach RMB 777.5 trillion in 2020. The two dominant m-payment platforms in China are Alipay and WeChat Pay, respectively, with more than 1.2 billion and 1.151 billion active users. Doubtless, the mobile payment business in China is not only extremely prosperous and successful but also extremely competitive, particularly for the two m-payment platforms of Alipay and WeChat Pay.
In addition, for m-payment platforms to improve and sustain competitive advantages, it is crucial to comprehend the factors influencing consumers' usage intention. Efficient and accurate causality exploration leads to precise and advantageous decisions. Thus, this empirical research aims to identify the casual relationships among the criteria and dimensions of consumers' usage intention in the Chinese m-payment market.

Identification of Criteria to Construct the Evaluation Framework
The influencing factors on m-payment consumers' usage intentions have been discussed in numerous studies. Following the SEM approach, the research framework is initially developed based on an intensive review of the literature and then revised according to the results of three focus-group interviews and five individual interviews with academic experts. The research model includes 14 criteria under the three dimensions of motivation, gamification, and perceived value. Descriptions of the criteria appear in Table 1. Table 1. The constructed evaluation framework.

COVID-19 Infection Prevention (C 1 )
Refers to the degree to which the concept of prevention of COVID-19 infection affects user's usage intention [37] Performance Expectancy (C 2 ) Refers to the degree of performance which could be achieved by the m-payment platform [47,92] Expected effort (C 3 ) Refers to the degree of easiness perceived by mobile payment users [47] Social Influence (C 4 ) Refers to the degree of influence from other people such as friends, families, or other third parties on mobile payment user's usage intention [42] Personal Innovativeness (C 5 ) Refers to the consumer's characteristics and behavior regarding the adoption of new technology [42,92]

Immersion-Related Features (C 6 )
Refers to gamification features, such as avatars, role-play, story-telling, narrative structures, customization, etc., aimed at immersing the user in self-directed inquisitive activities [71,72] Achievement-Related Features (C 7 ) Refers to gamification features, such as points, badges, leaderboard, etc., for monitoring their performance and progress and further sharing their achievement and performance with other users [67,73] Social-Related Features (C 8 ) Refers to gamification features, such as messages, blogs, and connection paths to social networks, which could strengthen user's interpersonal relationships on e-commerce platforms [67,71] Autonomy Needs (C 9 ) Refers to the degree of satisfaction with user's sense of self-direction and using their own volition and willingness when participating in activities [75] Competency Needs (C 10 ) Refers to the degree of satisfaction for the user's sense of self-growth and self-mastery for conquering difficulties and challenges and developing skills [75,93] Relatedness Needs (C 11 ) Refers to the degree of satisfaction with the user's sense of belonging and involvement with social connections [93] Perceived Value (D 3 ) Functional Value (C 12 ) Refers to the perceived and expected functional performance of the m-payment platform [62] Emotional Value (C 13 ) Refers to the feelings or mental status generated through the adoption of the m-payment platform [62] Social Value (C 14 ) Refers to the perceived enhancement of social relationships through the adoption of the m-payment platform [62] The motivation dimension refers to the factors of influence on consumers' acceptance intentions of m-payment. This dimension comprises prevention of infection by COVID-19, performance expectancy, expected effort, social influence, and personal innovativeness. The gamification features dimension refers to game design elements that could stimulate consumers' usage intention. This dimension comprises immersion-related features, achievement-related features, social-related features, autonomy needs, competency needs, and relatedness needs. Perceived value refers to the consumer's overall evaluation of perceived benefits from the adoption of m-payments. It comprises functional value, emotional value, and social value.

The Data
The instrument used in this study is mainly designed based on previous research, with ratings made using a five-point Likert scale and verified by academic experts. A pilot test with 20 surveys was first conducted; the validity of the measurement was confirmed. For the main surveys, a total of 241 valid samples were assembled. The demographic information for the respondents is shown in Appendix A Table A2. The majority of the 241 respondents were aged 21 to 30 (68.0%), with only 7.1% below 20; most participants were university or college-educated or currently studying (87.1%) and earning a monthly income of less than RMB 4000 (51.9%).
We now discuss the encoding process, taking motivation (D 1 ) as an example. The dimension of motivation involves five criteria: prevention of COVID-19 (C 1 ), performance expectancy (C 2 ), expected effort (C 3 ), social influence (C 4 ), and personal innovativeness (C 5 ); each criterion has at least three items (see Table 2) [94]. Table 2. The encoded data.

Respondent
No. intentions of m-payment. This dimension comprises prevention of infection by COVID-19, performance expectancy, expected effort, social influence, and personal innovativeness. The gamification features dimension refers to game design elements that could stimulate consumers' usage intention. This dimension comprises immersion-related features, achievement-related features, social-related features, autonomy needs, competency needs, and relatedness needs. Perceived value refers to the consumer's overall evaluation of perceived benefits from the adoption of m-payments. It comprises functional value, emotional value, and social value.

The Data
The instrument used in this study is mainly designed based on previous research, with ratings made using a five-point Likert scale and verified by academic experts. A pilot test with 20 surveys was first conducted; the validity of the measurement was confirmed. For the main surveys, a total of 241 valid samples were assembled. The demographic information for the respondents is shown in Table A2. The majority of the 241 respondents were aged 21 to 30 (68.0%), with only 7.1% below 20; most participants were university or college-educated or currently studying (87.1%) and earning a monthly income of less than RMB 4000 (51.9%).
To confirm the scale validity and reliability, the Bartlett's test of sphericity and Cronbach's alpha were applied. According to the rule of thumb, as suggested by Kaiser [85] and Field [95], the Kaiser-Meyer-Olkin (KMO) value of 0.891 and the Cronbach's alpha coefficients of dimensions ranging from 0.928 to 0.863 can be considered as good. The results confirmed that the survey items were relevant to and representative of the target constructs, and the multiple-question Likert scale measurement was reliable. In other words, the validity and reliability of this instrument were considered acceptable.

Acquiring the Direct and the Total Influence Relations of the Dimensions and Criteria
Unlike the traditional data collecting method of expert judgements used in DE-MATEL analysis, the data were collected directly from participants. After encoding the collected data, the direct influence relations were calculated by using Equations (1) and (2). Take dimensions as an example. First, D1 is defined as an exogenous variable, and the other two dimensions (D2 and D3) are defined as endogenous variables. SEM is applied to obtain the direct relations, illustrated as estimates in Table 3. That is, D1 to D2 (0.323) and D1 to D3 (0.435). Following the above process, the D2 and D3 are, respectively, defined as exogenous variables; the direct relations of D2 to D1 (0.297), D2 to D3 (0.462), D3 to D1 (0.526), and D3 to D2 (0.655) are acquired. By analogy, following the same procedures, the intentions of m-payment. This dimension comprises prevention of infection by COVID-19, performance expectancy, expected effort, social influence, and personal innovativeness. The gamification features dimension refers to game design elements that could stimulate consumers' usage intention. This dimension comprises immersion-related features, achievement-related features, social-related features, autonomy needs, competency needs, and relatedness needs. Perceived value refers to the consumer's overall evaluation of perceived benefits from the adoption of m-payments. It comprises functional value, emotional value, and social value.

The Data
The instrument used in this study is mainly designed based on previous research, with ratings made using a five-point Likert scale and verified by academic experts. A pilot test with 20 surveys was first conducted; the validity of the measurement was confirmed. For the main surveys, a total of 241 valid samples were assembled. The demographic information for the respondents is shown in Table A2. The majority of the 241 respondents were aged 21 to 30 (68.0%), with only 7.1% below 20; most participants were university or college-educated or currently studying (87.1%) and earning a monthly income of less than RMB 4000 (51.9%).
To confirm the scale validity and reliability, the Bartlett's test of sphericity and Cronbach's alpha were applied. According to the rule of thumb, as suggested by Kaiser [85] and Field [95], the Kaiser-Meyer-Olkin (KMO) value of 0.891 and the Cronbach's alpha coefficients of dimensions ranging from 0.928 to 0.863 can be considered as good. The results confirmed that the survey items were relevant to and representative of the target constructs, and the multiple-question Likert scale measurement was reliable. In other words, the validity and reliability of this instrument were considered acceptable.

Acquiring the Direct and the Total Influence Relations of the Dimensions and Criteria
Unlike the traditional data collecting method of expert judgements used in DE-MATEL analysis, the data were collected directly from participants. After encoding the collected data, the direct influence relations were calculated by using Equations (1) and (2). Take dimensions as an example. First, D1 is defined as an exogenous variable, and the other two dimensions (D2 and D3) are defined as endogenous variables. SEM is applied to obtain the direct relations, illustrated as estimates in Table 3. That is, D1 to D2 (0.323) and D1 to D3 (0.435). Following the above process, the D2 and D3 are, respectively, defined as exogenous variables; the direct relations of D2 to D1 (0.297), D2 to D3 (0.462), D3 to D1 (0.526), and D3 to D2 (0.655) are acquired. By analogy, following the same procedures, the intentions of m-payment. This dimension comprises prevention of infection by COVID-19, performance expectancy, expected effort, social influence, and personal innovativeness. The gamification features dimension refers to game design elements that could stimulate consumers' usage intention. This dimension comprises immersion-related features, achievement-related features, social-related features, autonomy needs, competency needs, and relatedness needs. Perceived value refers to the consumer's overall evaluation of perceived benefits from the adoption of m-payments. It comprises functional value, emotional value, and social value.

The Data
The instrument used in this study is mainly designed based on previous research, with ratings made using a five-point Likert scale and verified by academic experts. A pilot test with 20 surveys was first conducted; the validity of the measurement was confirmed. For the main surveys, a total of 241 valid samples were assembled. The demographic information for the respondents is shown in Table A2. The majority of the 241 respondents were aged 21 to 30 (68.0%), with only 7.1% below 20; most participants were university or college-educated or currently studying (87.1%) and earning a monthly income of less than RMB 4000 (51.9%).
To confirm the scale validity and reliability, the Bartlett's test of sphericity and Cronbach's alpha were applied. According to the rule of thumb, as suggested by Kaiser [85] and Field [95], the Kaiser-Meyer-Olkin (KMO) value of 0.891 and the Cronbach's alpha coefficients of dimensions ranging from 0.928 to 0.863 can be considered as good. The results confirmed that the survey items were relevant to and representative of the target constructs, and the multiple-question Likert scale measurement was reliable. In other words, the validity and reliability of this instrument were considered acceptable.

Acquiring the Direct and the Total Influence Relations of the Dimensions and Criteria
Unlike the traditional data collecting method of expert judgements used in DE-MATEL analysis, the data were collected directly from participants. After encoding the collected data, the direct influence relations were calculated by using Equations (1) and (2). Take dimensions as an example. First, D1 is defined as an exogenous variable, and the other two dimensions (D2 and D3) are defined as endogenous variables. SEM is applied to obtain the direct relations, illustrated as estimates in Table 3. That is, D1 to D2 (0.323) and D1 to D3 (0.435). Following the above process, the D2 and D3 are, respectively, defined as exogenous variables; the direct relations of D2 to D1 (0.297), D2 to D3 (0.462), D3 to D1 (0.526), and D3 to D2 (0.655) are acquired. By analogy, following the same procedures, the intentions of m-payment. This dimension comprises prevention of infection by COV 19, performance expectancy, expected effort, social influence, and personal innovati ness. The gamification features dimension refers to game design elements that could st ulate consumers' usage intention. This dimension comprises immersion-related featu achievement-related features, social-related features, autonomy needs, competen needs, and relatedness needs. Perceived value refers to the consumer's overall evaluat of perceived benefits from the adoption of m-payments. It comprises functional val emotional value, and social value.

The Data
The instrument used in this study is mainly designed based on previous resear with ratings made using a five-point Likert scale and verified by academic experts. A p test with 20 surveys was first conducted; the validity of the measurement was confirm For the main surveys, a total of 241 valid samples were assembled. The demographic formation for the respondents is shown in Table A2. The majority of the 241 responde were aged 21 to 30 (68.0%), with only 7.1% below 20; most participants were university college-educated or currently studying (87.1%) and earning a monthly income of less th RMB 4000 (51.9%).
To confirm the scale validity and reliability, the Bartlett's test of sphericity a Cronbach's alpha were applied. According to the rule of thumb, as suggested by Kai [85] and Field [95], the Kaiser-Meyer-Olkin (KMO) value of 0.891 and the Cronbac alpha coefficients of dimensions ranging from 0.928 to 0.863 can be considered as go The results confirmed that the survey items were relevant to and representative of target constructs, and the multiple-question Likert scale measurement was reliable other words, the validity and reliability of this instrument were considered acceptable

Acquiring the Direct and the Total Influence Relations of the Dimensions and Criteria
Unlike the traditional data collecting method of expert judgements used in D MATEL analysis, the data were collected directly from participants. After encoding collected data, the direct influence relations were calculated by using Equations (1) a (2). Take dimensions as an example. First, D1 is defined as an exogenous variable, and other two dimensions (D2 and D3) are defined as endogenous variables. SEM is applied obtain the direct relations, illustrated as estimates in Table 3. That is, D1 to D2 (0.323) a D1 to D3 (0.435). Following the above process, the D2 and D3 are, respectively, defined exogenous variables; the direct relations of D2 to D1 (0.297), D2 to D3 (0.462), D3 to (0.526), and D3 to D2 (0.655) are acquired. By analogy, following the same procedures, intentions of m-payment. This dimension comprises prevention of infection b 19, performance expectancy, expected effort, social influence, and personal i ness. The gamification features dimension refers to game design elements that c ulate consumers' usage intention. This dimension comprises immersion-relate achievement-related features, social-related features, autonomy needs, co needs, and relatedness needs. Perceived value refers to the consumer's overall of perceived benefits from the adoption of m-payments. It comprises functio emotional value, and social value.

The Data
The instrument used in this study is mainly designed based on previou with ratings made using a five-point Likert scale and verified by academic expe test with 20 surveys was first conducted; the validity of the measurement was For the main surveys, a total of 241 valid samples were assembled. The demog formation for the respondents is shown in Table A2. The majority of the 241 re were aged 21 to 30 (68.0%), with only 7.1% below 20; most participants were un college-educated or currently studying (87.1%) and earning a monthly income o RMB 4000 (51.9%).
To confirm the scale validity and reliability, the Bartlett's test of sphe Cronbach's alpha were applied. According to the rule of thumb, as suggested [85] and Field [95], the Kaiser-Meyer-Olkin (KMO) value of 0.891 and the C alpha coefficients of dimensions ranging from 0.928 to 0.863 can be considere The results confirmed that the survey items were relevant to and representa target constructs, and the multiple-question Likert scale measurement was other words, the validity and reliability of this instrument were considered acc

Acquiring the Direct and the Total Influence Relations of the Dimensions and Cri
Unlike the traditional data collecting method of expert judgements us MATEL analysis, the data were collected directly from participants. After en collected data, the direct influence relations were calculated by using Equatio (2). Take dimensions as an example. First, D1 is defined as an exogenous variab other two dimensions (D2 and D3) are defined as endogenous variables. SEM is obtain the direct relations, illustrated as estimates in Table 3. That is, D1 to D2 D1 to D3 (0.435). Following the above process, the D2 and D3 are, respectively, exogenous variables; the direct relations of D2 to D1 (0.297), D2 to D3 (0.462 (0.526), and D3 to D2 (0.655) are acquired. By analogy, following the same proce intentions of m-payment. This dimension comprises prevention of infe 19, performance expectancy, expected effort, social influence, and pers ness. The gamification features dimension refers to game design element ulate consumers' usage intention. This dimension comprises immersion achievement-related features, social-related features, autonomy nee needs, and relatedness needs. Perceived value refers to the consumer's o of perceived benefits from the adoption of m-payments. It comprises emotional value, and social value.

The Data
The instrument used in this study is mainly designed based on p with ratings made using a five-point Likert scale and verified by academ test with 20 surveys was first conducted; the validity of the measuremen For the main surveys, a total of 241 valid samples were assembled. The formation for the respondents is shown in Table A2. The majority of the were aged 21 to 30 (68.0%), with only 7.1% below 20; most participants w college-educated or currently studying (87.1%) and earning a monthly in RMB 4000 (51.9%).
To confirm the scale validity and reliability, the Bartlett's test o Cronbach's alpha were applied. According to the rule of thumb, as sug [85] and Field [95], the Kaiser-Meyer-Olkin (KMO) value of 0.891 and alpha coefficients of dimensions ranging from 0.928 to 0.863 can be con The results confirmed that the survey items were relevant to and repr target constructs, and the multiple-question Likert scale measuremen other words, the validity and reliability of this instrument were conside

Acquiring the Direct and the Total Influence Relations of the Dimensions
Unlike the traditional data collecting method of expert judgeme MATEL analysis, the data were collected directly from participants. A collected data, the direct influence relations were calculated by using E (2). Take dimensions as an example. First, D1 is defined as an exogenous other two dimensions (D2 and D3) are defined as endogenous variables. obtain the direct relations, illustrated as estimates in Table 3. That is, D1 D1 to D3 (0.435). Following the above process, the D2 and D3 are, respec exogenous variables; the direct relations of D2 to D1 (0.297), D2 to D3 (0.526), and D3 to D2 (0.655) are acquired. By analogy, following the sam intentions of m-payment. This dimension comprises prevention 19, performance expectancy, expected effort, social influence, an ness. The gamification features dimension refers to game design e ulate consumers' usage intention. This dimension comprises imm achievement-related features, social-related features, autonom needs, and relatedness needs. Perceived value refers to the consum of perceived benefits from the adoption of m-payments. It com emotional value, and social value.

The Data
The instrument used in this study is mainly designed base with ratings made using a five-point Likert scale and verified by a test with 20 surveys was first conducted; the validity of the measu For the main surveys, a total of 241 valid samples were assemble formation for the respondents is shown in Table A2. The majority were aged 21 to 30 (68.0%), with only 7.1% below 20; most particip college-educated or currently studying (87.1%) and earning a mon RMB 4000 (51.9%).
We now discuss the encoding process, taking motivation ( dimension of motivation involves five criteria: prevention of COV expectancy (C2), expected effort (C3), social influence (C4), and p (C5); each criterion has at least three items (see Table 2) [94].
To confirm the scale validity and reliability, the Bartlett's Cronbach's alpha were applied. According to the rule of thumb, [85] and Field [95], the Kaiser-Meyer-Olkin (KMO) value of 0.8 alpha coefficients of dimensions ranging from 0.928 to 0.863 can The results confirmed that the survey items were relevant to an target constructs, and the multiple-question Likert scale measu other words, the validity and reliability of this instrument were c

Acquiring the Direct and the Total Influence Relations of the Dime
Unlike the traditional data collecting method of expert ju MATEL analysis, the data were collected directly from participa collected data, the direct influence relations were calculated by u (2). Take dimensions as an example. First, D1 is defined as an exog other two dimensions (D2 and D3) are defined as endogenous vari obtain the direct relations, illustrated as estimates in Table 3. Tha D1 to D3 (0.435). Following the above process, the D2 and D3 are, exogenous variables; the direct relations of D2 to D1 (0.297), D2 (0.526), and D3 to D2 (0.655) are acquired. By analogy, following th intentions of m-payment. This dimension comprises prev 19, performance expectancy, expected effort, social influ ness. The gamification features dimension refers to game d ulate consumers' usage intention. This dimension compri achievement-related features, social-related features, a needs, and relatedness needs. Perceived value refers to the of perceived benefits from the adoption of m-payments. emotional value, and social value.

The Data
The instrument used in this study is mainly designe with ratings made using a five-point Likert scale and verifi test with 20 surveys was first conducted; the validity of th For the main surveys, a total of 241 valid samples were as formation for the respondents is shown in Table A2. The m were aged 21 to 30 (68.0%), with only 7.1% below 20; most college-educated or currently studying (87.1%) and earnin RMB 4000 (51.9%).
We now discuss the encoding process, taking motiv dimension of motivation involves five criteria: prevention expectancy (C2), expected effort (C3), social influence (C4 (C5); each criterion has at least three items (see Table 2) [94 To confirm the scale validity and reliability, the B Cronbach's alpha were applied. According to the rule of [85] and Field [95], the Kaiser-Meyer-Olkin (KMO) valu alpha coefficients of dimensions ranging from 0.928 to 0. The results confirmed that the survey items were releva target constructs, and the multiple-question Likert scale other words, the validity and reliability of this instrument

Acquiring the Direct and the Total Influence Relations of t
Unlike the traditional data collecting method of ex MATEL analysis, the data were collected directly from p collected data, the direct influence relations were calcula (2). Take dimensions as an example. First, D1 is defined as other two dimensions (D2 and D3) are defined as endogeno obtain the direct relations, illustrated as estimates in Table  D1 to D3 (0.435). Following the above process, the D2 and exogenous variables; the direct relations of D2 to D1 (0.2 (0.526), and D3 to D2 (0.655) are acquired. By analogy, follo intentions of m-payment. This dimension compris 19, performance expectancy, expected effort, soci ness. The gamification features dimension refers to ulate consumers' usage intention. This dimension achievement-related features, social-related feat needs, and relatedness needs. Perceived value refe of perceived benefits from the adoption of m-pay emotional value, and social value.

The Data
The instrument used in this study is mainly with ratings made using a five-point Likert scale an test with 20 surveys was first conducted; the validi For the main surveys, a total of 241 valid samples formation for the respondents is shown in Table A were aged 21 to 30 (68.0%), with only 7.1% below 2 college-educated or currently studying (87.1%) and RMB 4000 (51.9%).
We now discuss the encoding process, takin dimension of motivation involves five criteria: prev expectancy (C2), expected effort (C3), social influe (C5); each criterion has at least three items (see Tab To confirm the scale validity and reliability Cronbach's alpha were applied. According to the [85] and Field [95], the Kaiser-Meyer-Olkin (KM alpha coefficients of dimensions ranging from 0. 9 The results confirmed that the survey items were target constructs, and the multiple-question Like other words, the validity and reliability of this inst

Acquiring the Direct and the Total Influence Relat
Unlike the traditional data collecting metho MATEL analysis, the data were collected directly collected data, the direct influence relations were (2). Take dimensions as an example. First, D1 is def other two dimensions (D2 and D3) are defined as en obtain the direct relations, illustrated as estimates D1 to D3 (0.435). Following the above process, the exogenous variables; the direct relations of D2 to (0.526), and D3 to D2 (0.655) are acquired. By analo intentions of m-payment. This dimension 19, performance expectancy, expected effo ness. The gamification features dimension r ulate consumers' usage intention. This dim achievement-related features, social-relat needs, and relatedness needs. Perceived va of perceived benefits from the adoption o emotional value, and social value.

The Data
The instrument used in this study is with ratings made using a five-point Likert test with 20 surveys was first conducted; th For the main surveys, a total of 241 valid s formation for the respondents is shown in were aged 21 to 30 (68.0%), with only 7.1% college-educated or currently studying (87. RMB 4000 (51.9%).
We now discuss the encoding proces dimension of motivation involves five crite expectancy (C2), expected effort (C3), socia (C5); each criterion has at least three items ( To confirm the scale validity and re Cronbach's alpha were applied. According [85] and Field [95], the Kaiser-Meyer-Olk alpha coefficients of dimensions ranging fr The results confirmed that the survey item target constructs, and the multiple-questio other words, the validity and reliability of t

Acquiring the Direct and the Total Influen
Unlike the traditional data collecting MATEL analysis, the data were collected d collected data, the direct influence relation (2). Take dimensions as an example. First, D other two dimensions (D2 and D3) are defin obtain the direct relations, illustrated as est D1 to D3 (0.435). Following the above proce exogenous variables; the direct relations o (0.526), and D3 to D2 (0.655) are acquired. B intentions of m-payment. This dim 19, performance expectancy, expec ness. The gamification features dim ulate consumers' usage intention. T achievement-related features, soc needs, and relatedness needs. Perce of perceived benefits from the ado emotional value, and social value.

The Data
The instrument used in this st with ratings made using a five-poin test with 20 surveys was first condu For the main surveys, a total of 241 formation for the respondents is sho were aged 21 to 30 (68.0%), with onl college-educated or currently study RMB 4000 (51.9%).
We now discuss the encoding dimension of motivation involves fi expectancy (C2), expected effort (C (C5); each criterion has at least three To confirm the scale validity Cronbach's alpha were applied. Ac [85] and Field [95], the Kaiser-Mey alpha coefficients of dimensions ra The results confirmed that the surv target constructs, and the multiple other words, the validity and reliab

Acquiring the Direct and the Total
Unlike the traditional data co MATEL analysis, the data were col collected data, the direct influence (2). Take dimensions as an example other two dimensions (D2 and D3) a obtain the direct relations, illustrate D1 to D3 (0.435). Following the abov exogenous variables; the direct rel (0.526), and D3 to D2 (0.655) are acqu intentions of m-payment. Th 19, performance expectancy, ness. The gamification featur ulate consumers' usage inten achievement-related feature needs, and relatedness needs of perceived benefits from t emotional value, and social v

The Data
The instrument used in with ratings made using a fiv test with 20 surveys was first For the main surveys, a total formation for the respondent were aged 21 to 30 (68.0%), w college-educated or currently RMB 4000 (51.9%).
We now discuss the en dimension of motivation invo expectancy (C2), expected ef (C5); each criterion has at leas To confirm the scale v Cronbach's alpha were appli [85] and Field [95], the Kais alpha coefficients of dimensi The results confirmed that t target constructs, and the m other words, the validity and

Acquiring the Direct and th
Unlike the traditional d MATEL analysis, the data w collected data, the direct infl (2). Take dimensions as an ex other two dimensions (D2 and obtain the direct relations, ill D1 to D3 (0.435). Following th exogenous variables; the dir (0.526), and D3 to D2 (0.655) a intentions of m-payme 19, performance expec ness. The gamification ulate consumers' usag achievement-related f needs, and relatedness of perceived benefits emotional value, and s

The Data
The instrument u with ratings made usin test with 20 surveys w For the main surveys, formation for the resp were aged 21 to 30 (68. college-educated or cu RMB 4000 (51.9%).
We now discuss dimension of motivati expectancy (C2), expec (C5); each criterion has To confirm the s Cronbach's alpha wer [85] and Field [95], th alpha coefficients of d The results confirmed target constructs, and other words, the valid

Acquiring the Direc
Unlike the tradit MATEL analysis, the collected data, the dir (2). Take dimensions a other two dimensions obtain the direct relati D1 to D3 (0.435). Follow exogenous variables; (0.526), and D3 to D2 (0 intentions of m 19, performance ness. The gamif ulate consumer achievement-re needs, and relat of perceived be emotional value

The Data
The instrum with ratings ma test with 20 surv For the main su formation for th were aged 21 to college-educate RMB 4000 (51. 9 We now d dimension of m expectancy (C2) (C5); each criteri To confirm Cronbach's alph [85]   To confirm the scale validity and reliability, the Bartlett's test of sphericity and Cronbach's alpha were applied. According to the rule of thumb, as suggested by Kaiser [85] and Field [95], the Kaiser-Meyer-Olkin (KMO) value of 0.891 and the Cronbach's alpha coefficients of dimensions ranging from 0.928 to 0.863 can be considered as good. The results confirmed that the survey items were relevant to and representative of the target constructs, and the multiple-question Likert scale measurement was reliable. In other words, the validity and reliability of this instrument were considered acceptable.

Acquiring the Direct and the Total Influence Relations of the Dimensions and Criteria
Unlike the traditional data collecting method of expert judgements used in DEMATEL analysis, the data were collected directly from participants. After encoding the collected data, the direct influence relations were calculated by using Equations (1) and (2). Take dimensions as an example. First, D 1 is defined as an exogenous variable, and the other two dimensions (D 2 and D 3 ) are defined as endogenous variables. SEM is applied to obtain the direct relations, illustrated as estimates in Tables 3 and 4. That is, D 1 to D 2 (0.323) and D 1 to D 3 (0.435). Following the above process, the D 2 and D 3 are, respectively, defined as exogenous variables; the direct relations of D 2 to D 1 (0.297), D 2 to D 3 (0.462), D 3 to D 1 (0.526), and D 3 to D 2 (0.655) are acquired. By analogy, following the same procedures, the direct relations of the criteria for each dimension can be gained by filling in the estimates in the corresponding positions, as depicted in Tables 5-7, labeled as Matrix A, and systematically assorted in Table A3. The results further confirm that the direct relations of the dimensions and criteria are statistically significant (p-Value < 0.05).   Table 5. The direct and total influence relations matrix of D 1-motivation.  Table 6. The direct and total influence relations matrix of D 2-gamification.
A C 6 C 7 C 8 C 9 C 10 C 11 T C 6 C 7 C 8 C 9 C 10 C 11  The total influence relations, depicted in Tables 4-7 and labeled Matrix T, are obtained by the application of Equations (4)- (8). The influence relations of the dimensions and criteria shown in Matrix A and Matrix T vary. Instead of assuming that all the exogenous variables are independent, which is not in accordance with the real world, DEMATEL considers the interdependent relationships of the factors when exploring the causality. The outcome can be regarded as corresponding more closely to reality and beneficial to practical decision-makers. Furthermore, unlike SEM, DEMATEL, computing the total interactive effects of each dimension and criterion, can provide more factual influencing degrees of factors.
Among the dimensions, as shown in Matrix A, D 1-motivation impacts D 3 -perceived value more than D 2 -gamification (shown in Table 4); however, as indicated in Matrix T, D 1-motivation impacts D 2 -gamification and D 3 -perceived value to the same degree. Furthermore, the effective strength of D 1 -motivation compared to D 2 -gamification increases from 0.320 in Matrix A to 1.041 in Matrix T. The degree of influence of hidden interactive effects can be discovered, and this issue should be addressed.
The direct and total relations among the criteria of D 1 -motivation are summarized in Table 5. Based on the order of the effective strengths, the top two in Matrix A are C 1 -prevention of COVID-19 infection to C 4 -social influence, followed by C 4 -social influence to C 2 -performance expectancy; however, according to the results in Matrix T, C 4 -social influence affects C 2 -performance expectancy the most, followed by the effect of C 1 -COVID-19 infection prevention on C 4 -social influence. In other words, in D 1 -motivation, the influence relations are altered because of the interactive effects among the criteria.
The direct and the total relations among the criteria of D 2-gamification are summarized in Table 6. Based on the order of the effective strengths, the top three in Matrix A are C 7 -achievement features to C 8 -social features, followed by C 6 -immersion features to C 7 -achievement features and C 10 -competency needs to C 11 -relatedness needs; however, according to the results in Matrix T, C 8 -social features affects C 7 -achievement features the most, followed by C 7 -achievement features to C 11 -relatedness needs and C 9 -autonomy needs to C 8 -social features.
The direct and the total relations among the criteria of D 3-perceived value are clarified in Table 7. In this dimension, the results in Matrix A and Matrix T are similar, both identifying that the greatest impact is from C 13 -emotional values to C 14 -social value, followed by C 12 -functional value to C 14 -social value. Table 8 identifies the influence degree (r), the influenced degree (c), the total influence degree (p), and the net influence degree (g) of the dimensions and the criteria. Among the dimensions, according to the r values, D 3 has the highest influence degree, followed by D 2 and D 3 ; the results for the c values confirm that D 2 has the highest influenced degree; as to the total influence degree and the net influence degree, D 1 has both the highest p value and g value. That is, perceived value (D 3 ) has the highest prominence (6.959) and is the cause influencing the other two dimensions of gamification (D 2 ; −0.537) and motivation (D 1 , −0.136). The dimension of motivation (D 1 ) consists of five criteria: COVID-19 infection prevention (C 1 ), performance expectancy (C 2 ), expected effort (C 3 ), social influence (C 4 ), and personal innovativeness (C 5 ). Based on the p values, the criteria are ranked in the following order of prominence: social influence (C 4 ; 7.594), expected effort (C 3 ; 6.657), COVID-19 infection prevention (C 1 ; 6.445), performance expectancy (C 2 ; 6.429), and personal innovativeness (C 5 ; 4.113). Meanwhile, the g values show that net causes include COVID-19 infection prevention (C 1 ; 0.947), expected effort (C 3 ; 0.572), and social influence (C4; 0.272).
The dimension of gamification (D 3 ) includes the three criteria of functional value (C 12 ), emotional value (C 13 ), and social value (C 14 ). Based on the p values, they are ranked in order of prominence as follows: social value (C 14 ; 3.634), emotional value (C 13 ; 3.516), and functional value (C 12 ; 3.059). The g values confirm that the causes are functional value (C 12 ; 0.630) and emotional value (C 13 ; 0.498).
Certainly, incorrect causality exploration will result in an improper decision. The INRM helps the decision-maker avoid causal errors by visualizing the interrelationship between indicators and identifying causal relationships among the factors. The INRMs clearly illustrated the causality based on the total influence and the net influence of the dimensions and the criteria as represented in Figure 4.
In   In this empirical case, the findings conclude that the causality of the original literaturedriven hypotheses proposed in the SEM model and the results of the SEM-DEMATEL method are different.

Discussion
This section involves comparing the discrepancies in the pre-hypothesized literaturedriven SEM research model and the results by SEM-DEMATEL method, canvassing the theoretical implications and contributions of the SEM-DEMATEL method, discussing the managerial implications, and proffering suggestions to m-payment platforms.

Comparison of the Presumed Hypotheses SEM Model and SEM-DEMATEL Method Results
In SEM, the research framework is constructed based on previous research [21]. The proposed model constructed after an intensive literature review is shown on the left of Figure 5. Three hypotheses are proposed. Hypothesis 2. Perceived Value has a positive effect on motivation [97].
However, according to the net influence values obtained in this empirical study, perceived value is confirmed as a causal dimension, influencing the other two dimensions of motivation and gamification, as depicted on the right of Figure 5. Note that this changes the initially proposed research framework as follows: 1. Perceived value has a positive effect on gamification; 2. Perceived value has a positive effect on motivation; 3. Motivation has a positive effect on gamification.

Theoretical Implications
The SEM and DEMATEL methods have been widely applied and verified as efficient causality exploration research approaches. Both methods have their strengths and weaknesses. SEM has the advantage of being able to acquire estimates of the latent, exogenous, and endogenous variables and the manifestation of causalities between indicators comparatively effortlessly but has the limitation of requiring complicated parameter estimations and literature-driven presumed hypotheses to construct the research framework. DEMATEL, with the features of confirmation of interdependence and the simplification and visualization of confused causal relationships among the criteria and the dimensions, can effectively solve entangled and intricate problems but is dependent upon the subjective preferences of experts.
Significantly, the SEM and DEMATEL methods complement each other, and, therefore, we propose the SEM-DEMATEL research method. This novel method can not only

Hypothesis 2.
Perceived Value has a positive effect on motivation [97].
However, according to the net influence values obtained in this empirical study, perceived value is confirmed as a causal dimension, influencing the other two dimensions of motivation and gamification, as depicted on the right of Figure 5. Note that this changes the initially proposed research framework as follows:

1.
Perceived value has a positive effect on gamification; 2.
Perceived value has a positive effect on motivation; 3.
Motivation has a positive effect on gamification.

Theoretical Implications
The SEM and DEMATEL methods have been widely applied and verified as efficient causality exploration research approaches. Both methods have their strengths and weaknesses. SEM has the advantage of being able to acquire estimates of the latent, exogenous, and endogenous variables and the manifestation of causalities between indicators comparatively effortlessly but has the limitation of requiring complicated parameter estimations and literature-driven presumed hypotheses to construct the research framework. DEMATEL, with the features of confirmation of interdependence and the simplification and visualization of confused causal relationships among the criteria and the dimensions, can effectively solve entangled and intricate problems but is dependent upon the subjective preferences of experts.
Significantly, the SEM and DEMATEL methods complement each other, and, therefore, we propose the SEM-DEMATEL research method. This novel method can not only maintain the respective advantages of both of SEM and DEMATEL but also compensate for their ineluctable limitations. This has theoretical implications.

Managerial Implications
It has been verified that DEMATEL can simplify complicated problems by visualizing complex causality relationships. These advantages assist decision-makers in identifying imperfections in existing systems and allocating limited resources for the most efficient improvement. Thus, based on the findings, the following suggestions are proposed.
Among the three dimensions, the results identify perceived value (D 3 ) as a causal dimension, which simultaneously affects motivation (D 1 ) and gamification (D 2 ). In other words, perceived value (D 3 ) is the most essential influencing indicator of m-payment usage intention. It is recommended that m-payment platforms endeavor to develop effective strategies to increase user satisfaction with perceived value. Meanwhile, the INRM for perceived value (D 3 ) indicates that the criteria of emotional value (C 13 ) and functional value (C 12 ) are causes, which affect social value. In particular, emotional value (C 13 ) has the highest total influence degree and should be considered as a crucial element for the m-platform to maintain its competitive advantages. These findings are consistent with the research of Sheth et al. [61].
In order to increase the m-payment user's level of satisfaction and emotional value (C 13 ) and further reinforce their loyalty and usage intention, it is suggested that m-payment platforms develop more attractive, entertaining, and novel components by implementing innovative artificial intelligence (AI) technologies like virtual reality (VR), designing more interesting games, providing more interesting interactive activities, or avatar role-playing during the application process. Emotional value can be improved by increased user enjoyment and stimulating their curiosity. To improve functional value (C 12 ), it is suggested that the platform can enrich the functions of the m-payment system with more innovative and creative AI systems, such as offering an online virtual assistant to aid consumers with handling a variety of electricity, water, tax, or credit card bill payments; offering bill due reminder services; and providing consumers with abundant information about traveling, shopping, health-care, or updated infection prevention knowledge; augmenting a variety of new services to raise m-payment user's perception of functional value.
Within the dimension of motivation (D 1 ), the net influence values show that the criteria of COVID-19 infection prevention (C 1 ), expected effort (C 3 ), and social influence (C 4 ) to be causes, with social influence (C 4 ) being the most influential criterion. In other words, among the criteria in the dimension of motivation (D 1 ), social influence (C 4 ) is the most essential factor that influences usage intention. Therefore, it is suggested that m-payment platforms either create their own apps or use widely applied social platforms, such as Facebook, YouTube, and Instagram to develop strategies, such as the use of incentive rewards and emotional provocation and encouraging users to generate contents, to push ratings, and to increase network traffic.
Within the dimension of gamification (D 2 ), the net influence values indicated that the three criteria of immersion features (C 6 ), achievement features (C 7 ), and autonomy needs (C 9 ) are causal factors. According to the total influence values, achievement features (C 7 ) is the most influential criterion. This result is consistent with the results of Suh et al. [73]. Thus, it is suggested that the m-payment platforms introduce gamification elements such as trophies, badges, leaderboards, and points during the process of accessing m-payment; furthermore, m-payment platforms could also hold competitions regularly and reward winners with points, prizes, discounts, or medals.
To summarize, the results identify the key causes and the most influential criteria for the whole research model and the three dimensions. Among the dimensions, perceived value (D 3 ) plays the most significant role in reinforcing the consumer's m-payment usage intention. In the dimension of perceived value (D 3 ), social value is the most important factor for improving usage intention. In addition, achievement features (C 7 ) and social influence (C 4 ) are the key factors in gamification (D 2 ) and motivation (D 1 ), respectively.
Precise causality exploration is a prerequisite for efficacious decision-making. The results of this empirical study indicate the causal relationships between the dimensions and the criteria to assist decision-makers in the development of strategies for improvement and practical suggestions for the development of m-payment platforms.

Conclusions
A precise causality exploration method is the foundation of effective decision-making. Both SEM and DEMATEL have been confirmed as efficient tools for the verification of causal relationships between complicated variables, but each has its advantages, distinguishing features, and limitations. The SEM method can be used to make estimates of latent, exogenous, and endogenous variables, and verification of the reciprocal causal relations between indicators can be completed simultaneously and facilely. The data-driven results are comparatively convincing. However, this method also requires complex parameter estimations or over-trimming of the initially proposed literature-driven model. The DEMATEL approach allows rigorous confirmations of interdependence and clear visualization of the confused causal relationships among criteria attract; however, it is reliant upon subjective expert judgements for preferences.
This study explores the SEM-DEMATEL method, which combines the advantages of SEM and DEMATEL while overcoming their limitations. The results of causality exploration by this novel method differ from those obtained with the traditional literature-driven SEM research model. Without the complex parameter estimation requirements, this SEM-DEMATEL method is more operationally facile, making it easier for the practitioner to use, and there is no need for over-trimming of the models, so the causality explorations can be more precise.
This novel method was employed in an empirical case study to explore the key factors of m-payment usage intention. The dimension and criteria were categorized into two cause and effect groups based on the net influence degree results. The key influencing factors were identified after exploring the total influence degrees of the dimensions and criteria in the causal groups. The results differed from those obtained with the presumed hypotheses research model and confirm that perceived value simultaneously positively impacts both motivation and gamification, while motivation positively affects gamification. In other words, perceived value plays a significant role in affecting the usage intention of m-payment users, while emotional value is the most crucial factor for improving the consumer's perceived value, followed by functional value. Based on the findings, some practical suggestions were also proposed. This study makes valuable contributions academically and practically.
Some limitations remain. Firstly, this novel SEM-DEMATEL method was only employed on one case; this could be one of the limitations for verifying the advantages and limitations of this research method. Secondly, the target participants were Alipay and WeChat Pay users only. Thus, obtaining questionnaire responses from more countries and a more diverse group of users would be necessary to generalize the results to global m-payment businesses and allow for a more multifaceted approach.
Future empirical research is suggested to experiment with and make improvements to this new method. Furthermore, while adhering to the core concepts of the SEM-DEMATEL method, different approaches could be added, such as a modified VIKOR method, to clarify the gap between the performance and aspiration levels of the criteria/dimensions. The results of future research can be compared with those described above.

Conflicts of Interest:
The authors declare no conflict of interest.