Understanding consumers’ behavior to adopt self-service parcel services for last-mile delivery

Journal of Retailing and Consumer Services 52 (2020) 101911

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Understanding consumers’ behavior to adopt self-service parcel services for last-mile delivery

T

Min Zhoua,b,c,∗, Lindu Zhaob, Nan Kongc, Kathryn S. Campyd, Ge Xua, Guiju Zhua, Xianye Caoa, Song Wanga a

College of Business Administration, Hunan University of Technology and Business, Changsha, PR China School of Economics and Management, Southeast University, Nanjing, PR China c Weldon School of Biomedical Engineering, Purdue University, West Lafayette, USA d Center for Public Health Initiatives, University of Pennsylvania, Philadelphia, USA b

A R T I C LE I N FO

A B S T R A C T

Keywords: Self-service parcel delivery service Perceived risk Perceived satisfaction UTAUT China

Self-service parcel delivery service has been favored by E-commerce retailers as an effective solution to the “lastmile” delivery, and consumers' adoption behavior is the key point to apply this emerging technology. The aim of the present study is to empirically test the influence of psychological factors on online consumers’ behavioral intention to adopt it. An extended UTAUT model is developed and 525 structured questionnaires were collected. Empirical results demonstrate that performance expectancy, effort expectancy, social influence and facilitating conditions are positive determinants, while perceived risk was negatively factor to behavioral intention. The difference in the behavioral intention among demographic groups was examined as it might provide an opportunity for developing a new strategy to promote the application of self-service parcel delivery service for lastmile delivery.

1. Introduction In the era of e-commerce, the “last mile” delivery service (LMDS) is an important challenge of logistics service performance. In recent years, the online-commerce, especially the mobile commerce, provides a visual, convenient, personalized and diversified shopping experience to customers. Because online commerce has these significant advantages over traditional businesses, the worldly transaction volume by onlinecommerce has increased from $16.13 trillion in 2009 to $25.68 trillion in 2016 (Lee et al., 2017). Every day, millions of packages are need to be delivered to the online shoppers in a fast and low-cost way. In the traditional delivery service mode, the deliveryman needs to contact the customer and arrange the delivery time, as well as remedial measures for unexpected situations, such as traffic congestion, customers temporarily leaving. The last mile delivery takes up most of the time and costs among the whole logistics operations, and it became the most critical issue affecting the efficiency of the logistic service (Zhang et al., 2016). Self-service parcel delivery service has emerged as a new solution to hand this problem. Consumers can send out or pick up packages by automated parcel cabinets, instead of face-to-face transfer packages

with couriers. There are various advantages that self-service parcel delivery service possess over traditional home deliveries, including time and cost (Chen et al., 2018). Parcels can be deal with at any time and couriers can finish the delivery tasks in shorter time. As an alternative mode to the traditional home deliveries, it has attracted widespread attention from online retailers to delivery service providers. For Amazon, self-service package delivery service is an equally important strategic measure as automated warehouses to deal with the fierce competition in the future. FedEx has invested in building 19 fully automated sites and 69 redistribution centers in 2017, including selfservice parcel delivery projects. Several providers have built their selfservice parcel delivery networks in mainland China. As the third biggest e-commerce retailer in China, JD.com has built more than 1000 selfservice parcel pick-up machines in Beijing, Shanghai and Guangzhou. Hive-box Technology Company is the biggest self-service delivery service provider in mainland China, and it has covered 75,000 communities in more than 100 cities in China and built 8 million self-service parcel delivery cabinets at the end of 2017. China Post, Chinese ecommerce powerhouse Alibaba and its logistics arm Cainiao jointly acquired the “Express Easy” company and expanded it to be the second ranked self-service delivery service provider in China. As the self-

∗

Corresponding author. NO.569 Yuelu Avenue, College of Business Administration, Hunan University of Technology and Business, Changsha, 410205, PR China. E-mail addresses: [email protected] (M. Zhou), [email protected] (L. Zhao), [email protected] (N. Kong), [email protected] (K.S. Campy), [email protected] (G. Xu), [email protected] (G. Zhu), [email protected] (X. Cao), [email protected] (S. Wang). https://doi.org/10.1016/j.jretconser.2019.101911 Received 4 September 2018; Received in revised form 29 July 2019; Accepted 9 August 2019 0969-6989/ © 2019 Elsevier Ltd. All rights reserved.

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2. Literature review and theoretical framework

service express delivery network keeps growing, it is becoming more accessible and attractive to collect and deliver packages. However, the consumers’ acceptance of this new technology is not as sanguine as the service provider anticipates. For example, the utilization rate of self-service express facilities of most express delivery companies in mainland China is less than 65% (Chen et al., 2018), while the comprehensive utilization rate is equal to the utilization rate divided by 2–3 waves per day and less than 30%. The low comprehensive utilization rate has led to a difficult situation for self-service express delivery service companies. During the period from January 2017 to May 2018, the accumulated operating income of Hive-box Technology Company was 596 million yuan (about 86 million USD), but it had a cumulative loss of 634 million yuan (about 92 million USD). It is necessary to determine the psychological factors which influence the online consumers' decision-making to raise their interest in using this new technology. Previous studies focused on facility technology issues for self-service delivery services, but research on customer acceptance of this technology appears to be scarce (Lee and Whang, 2001; Michalowska et al., 2015; Wang et al., 2014; Zhang et al., 2016). Some studies provide little knowledge about the customer perspective of self-service delivery services based on focus group interviews (Vakulenko et al., 2018), Innovation Diffusion Theory (IDT) (Wang et al., 2018; Yuen et al., 2018). However, previous studies have rarely used customer psychological perception as a decisive factor influencing their technical acceptance behavior, and this is the main motivation for current manuscript research. Many literature have developed some theoretical models for understanding the customers' acceptance to new technology from a variety of theoretical perspectives, including the Unified Acceptance and Use of Technology (UTAUT), Theory of Planning Behavior (TPB) and Technology Acceptance Model (TAM) (Venkatesh et al., 2003; Venkatesh, Thong and Xu, 2012a). The UTAUT model has strong explanatory capabilities and has been widely used in different fields: the behavioral intent of older people using the mHealth system (Hoque and Sorwar, 2017), research on security-related factors for NFC based mobile payment in the restaurant industry (Khalilzadeh et al., 2017), Acceptance of Automated Road Transport Systems (ARTS) (Madigan et al., 2016), and customers' intentions and adoption of Internet banking (Alalwan et al., 2018). However, few literature use UTAUT to examine the customer's intention to use logistics technology or delivery service. Performance Expectancy, Effort Expectancy and Social Influence were assumed as constructs that directly affect behavioral intention in UTAUT model (Kijsanayotin et al., 2009). However, E-consumers are more aware of risk, and perceived risk is an important variable that cannot be ignored (Tandon et al., 2018). Therefore, Perceived Risk was added as moderator variable to present an extended UTAUT model in this study, and for understanding the online consumers' intention to use self-service parcel delivery service. This study contributes to provide further understanding of the online consumers’ behavioral intention for self-service parcel delivery services, with emphasis on the impact of perceived risks. Therefore, two key questions will be answered in the present research: Whether the Perceived Risk affects the relationship between behavioral intention and usage behavior significantly as a moderator variable? Which factors are the decisive factors that influence consumers' behavioral intention among the following constructions: performance expectancy, effort expectancy, social influence and facilitating conditions? The rest of this paper is organized as follows. First, a review of the contemporary literature on self-service deliveries and UTAUT was conducted. Then, research model and hypotheses were formulated. That is followed by an explanation of the research methodology we applied, including surveys and data collection procedures. Next, data were analyzed and hypotheses were tested using structural equation model. Finally, the theoretical and practical contributions were then discussed and conclusions are drawn based on the results.

2.1. Self-service delivery Previous literature has proposed some self-service delivery models. In the early stages, the reception box and delivery box concepts were deemed to be feasible approaches for unattended delivery (Lee and Whang, 2001). The reception box is a “refrigerated, customer-specific receiving box installed in the customer's garage or home courtyard” and the delivery box is an “insulated safe” box equipped with a docking mechanism (Ewedairo et al., 2018). As to operational efficiency, it is 2.8 times higher than home delivery and can save 55–66% in delivery costs. However, these methods are merely temporary devices and cannot be widely used, whether reception box or delivery box (Punakivi and Hinkka, 2006). The shared reception boxes significantly increase the cost-effectiveness of the last mile delivery. Although the demand for such services is unpredictable at the time, the researchers suggested future research for its feasibility and acceptance of self-collection services. Self-service delivery has many advantages over traditional delivery, environmental protection, cost savings and flexible arrangements. First, unnecessary traffic generated by the last mile home delivery attracted consumers' attention (Lucas, 2012). The self-service delivery is performed by establishing an effective large-scale mobile crowd-tasking model and using a large pool of citizen workers. To efficiently solve the model, various pruning techniques were made and the network size was reduced significantly (Wang et al., 2016). Cost saving is also the focus of customer attention. The results show that providing a 3-h delivery window is 30–45% more expensive than delivering an unattended (9-h delivery window) delivery (Boyer, Prud'homme, & Chung, 2009). When home delivery costs $3 more than self-collection services, more than 50% of consumers are willing to switch to self-collection services(Yuen et al., 2018). The last one is about the flexibility of picking up or mailing time. Customers are increasingly willing to use self-delivery service because the waiting line for couriers grows longer and the receipt schedule becomes inflexible. This effect is influenced by perceived usefulness, perceived quality of self-service technology, interactive needs and technical anxiety (Kokkinou and Cranage, 2015). Besides research on the concept and practice of self-service delivery, there are also plenty of theoretical models for optimization of delivery operations management, including delivery path optimization (Liu et al., 2017), information systems(Gal-Tzur et al., 2014), and facility sharing (Zhou et al., 2017). The application necessary of self-service parcel delivery service in online commerce was certified and the technology was developed in allegro way. However, the previous literature is the limited discussion of the determinants of consumer psychological preferences, and the lack of a theoretical framework to examine the interactive influence of various factors. 2.2. Unified theory of acceptance and use of technology The Unified Theory of Acceptance and Use of Technology (UTAUT) has been widely used since it was proposed by Venkatesh et al. (2003), which integrates eight models on the key elements of technology acceptance behavior intention: the Theory of Planned Behavior (TPB), the Theory of Reasoned Action (TRA), the Technology Acceptance Model (TAM), the motivational model, the combined TAM and TPB model, the model of Personal Computer (PC) utilization, Innovation Diffusion Theory (IDT), Social Cognitive Theory (SCT) (Kijsanayotin et al., 2009; Venkatesh et al., 2003). The UTAUT model has been validated in different countries and cultures, and empirical research reports that its determinants explain about 70% of variances in behavioral intention (Jewer, 2018). After controlling the age and gender variables, effort expectancy and facilitating conditions were the only determinants that positively predicted tablet use intentions (Magsamen-Conrad et al., 2015). The determinants of near-field communication (NFC) based 2

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environment of ubiquitous sharing online information, the behavior of online consumers is easily influenced by the opinions from others (Venkatesh et al., 2003). Self-service delivery service as a new technology and business model, its controversial views are rich in the network (Lee et al., 2017). In this study, social influence was defined as the degree of other people's option that believes users should try self-service delivery service. Previous empirical studies of the UTAUT model have shown that performance expectancy, operation expectancy and social influence are significant determinants of the behavioral intention, especially in the context of e-commerce (Bhatiasevi, 2016; Venkatesh et al., 2003). Based on the above analysis, three hypotheses are proposed as follows:

mobile payment (MP) technology acceptance in the restaurant industry include risk, security and trust, and these factors have the most substantial impact on customers' behavioral intention (Khalilzadeh et al., 2017). An empirical study in the context of patient acceptance of the Emergency Department (ED) website showed that significant effects of performance expectancy and facilitating conditions on behavioral intention (Jewer, 2018). The positive factors affecting older users’ acceptance of Home Telehealth Services (HTS) include performance expectancy, effort expectancy, facilitating conditions, and perceived security, while the computer anxiety is a strong negative factor (Cimperman et al., 2016). In the context of self-service delivery service, online consumers' acceptance of this new delivery technology will be affected by multiple psychological factors. The present study adopted six psychological factors in the UTAUT model (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, usage behavior) and extended two new constructs (perceived risk and perceived satisfaction). This study will integrate perceived satisfaction as an independent variable that determines behavioral intention and usage behavior. If online consumers’ perceive satisfaction of using selfservice delivery service is high, they will have a greater willingness to use it (Yuen et al., 2018). Therefore, this study assumed that increasing satisfaction can increase behavioral intention and usage behavior. The conceptual framework is shown in Fig. 1.

H1. Performance expectancy has a positive influence on online consumers' behavioral intention to use self-service delivery service. H2. Effort expectancy has a positive influence on online consumers' behavioral intention to use self-service delivery service. H3. Social influence has a positive influence on online consumers' behavioral intention to use self-service delivery service.

2.4. Perceived risk and behavioral intention Based on the theoretical framework of emotional risk, perceived risk is defined as the emotional effects experienced in decisions making. Emotional reactions to dangerous situations are often different from cognitive assessments of these risks, and when such disagreements occur, emotional responses tend to drive behavior (Loewenstein et al., 2001). Perceived risks can be divided into five dimensions: financial risk, security/privacy risk, performance risk, social risk and time risk. Financial risk is the potential monetary loss associated with the payment price and subsequent maintenance costs of the product or service (Pollet et al., 2012). The security/privacy risk is a common risk in Internet business, and it is specifically represented by the potential loss associated with the improper use of personal information, especially in the big data environment (Lee, 2009). The performance risk is the possibility of the product or service that cannot achieve the expected performance, which is common in the promotion of new technology (Andres Quintero et al., 2012). The social risk is that consumers potentially lose their status in the social group due to the purchase of products or services. The time risk is that consumers are wasting time in waiting or wrong buying behavior (Parimbelli et al., 2018). Based on the above discussion, perceived risk in this study is defined as the potential loss incurred by consumers using self-service delivery service, and that is the difference between actual performance and expected benefits.

2.3. Performance expectancy, effort expectancy, social influence and behavioral intention In the UTAUT model, performance expectancy is the degree to which user believes that using a new technology or system will help to accomplish tasks with good performance. It is an important determinant of the behavioral intention and five constructs was integrated, including extrinsic motivation (Motivational Model), perceived usefulness (TAM), job-fit (Model of PC Utilization), relative advantage (IDT) and outcome expectations (SCT) (Davis and Venkatesh, 1996; Venkatesh et al., 2003). In this study, the definition of performance expectancy is the degree to which online consumers consider that using the self-service delivery service can help them achieve the goal of sending or receiving an express parcel in the security and quick way. Effort expectancy is defined as the degree of ease related to use the system (Venkatesh et al., 2003). For online consumers, there is limited time to spend on shopping and follow-up affairs. The reason why they may prefer to use self-service delivery service is mainly because the time-saving and convenience of this approach. To fit the online shopping context in this paper, the definition of effort expectancy is the ease of use self-service delivery service to deal with express packages. In the

Fig. 1. Conceptual framework. 3

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one of the important variables of the UTAUT model, and nearly half of previous studies which based on the theoretical framework of UTAUT have confirmed the significant relationship between facilitating conditions and usage behavior (Jewer, 2018; Morosan and DeFranco, 2016; Venkatesh, Thong and Xu, 2012b). Based on literature perspectives and survey data, we assume that facilitating conditions are important variables that influence usage behavior. In addition, the literature on consumer behavior supports that facilitating conditions affect their perceived satisfaction (Koufteros et al., 2014). In the context of the selfservice delivery service, facility conditions include guidance and knowledge to use it, walking distance, complexity of express processing steps (Hume and Mort, 2010; Wang et al., 2018). We assume that there is a positive correlation between facilitating conditions and perceived satisfaction, so, the facilitating conditions are assumed to be significant variables that affect both usage behavior and perceived satisfaction. As mentioned earlier in this paragraph, perceived satisfaction is assumed to be a direct variable that affects usage behavior. In summary, on the path from perceived satisfaction to usage behavior, the facilitating conditions exert influence on the variables at both ends. In other words, the facilitating conditions are assumed to be the moderator between perceived satisfaction and usage behavior. In the study of online retail and follow-up research, behavioral intention was also found to have a positive correlation with usage behavior. Hence, we hypothesize the following:

The perceived risk becomes key latent variables, especially when they associate with privacy-related research on Internet services (Lee, 2009). Empirical works on e-commerce consumers note that the importance of perceived security or risk(Khalilzadeh et al., 2017). It refers to the degree to which a customer believes that using a new procedure or technology will be secure in financial, privacy or other fields. Several studies declare the significant relationship between perceived security/ risk and behavioral intention (Zhang et al., 2016). Because the Internetbased services lack of tangible and concrete cues, it is even more subjective and uncertain than traditional offline service. Therefore, we suggest that users are likely to rely on risk perception and subjective satisfaction when evaluating self-service delivery service. Perceived risk is an aggregated construct, so the behavioral intention of self-service delivery service may not be produced in a completely direct way. The users judge risk based on his experience of the delivery service (Flavián et al., 2006). Because of this, perception satisfaction may partially mediate the relationship between perceived risk and behavioral intention. Therefore, this study presents the following hypothesis: H4a. Perceived risk has a negative influence on online consumers' behavioral intention to use self-service delivery service. H4b. Perceived risk has a negative impact on online consumers' perceived satisfaction to use self-service delivery service. H5a. Perceived satisfaction has a positive impact on online consumers' behavioral intention to use self-service delivery service.

H5b. Perceived satisfaction has positive influence on online consumers' usage behavior of self-service delivery service. H6a. : Facilitating conditions has positive influence on online consumers' perceived satisfaction to use self-service delivery service.

2.5. Perceived satisfaction, facilitating conditions and usage behavior Perceived satisfaction refers to the customer's perception of the fulfilled degree to which a product's or service's stated, commonly implied, or required performance, and it is a psychological experience (Flavián et al., 2006). The most important factor in determining the “user retention rate” is the customer's perceived satisfaction. What determines consumer satisfaction is not the experience of the product itself, but the difference between the “expectation” and “perceived experience” of the product or service (Venkatesh et al., 2012a). Research in e-commerce consumer's satisfaction with express delivery services is still in its early stages and scarce. Empirical research in the Chinese express industry shows that five factors are determinants of satisfaction, Tangibility, Reliability, Responsiveness, Assurance, Empathy (Zhuo et al., 2013). To evaluate the competitive advantages of international express delivery service providers from the perspective of customer value, results show that ‘time’ is the most important criterion affecting customer satisfaction. In addition, ‘land time between house and airport at both ends,’ ‘administrative processing time,’ ‘pick -up and haul time in warehouse,’ ‘level of accuracy,’ ‘level of safety,’ and ‘rapid turnover’ are also the top six key factors affecting customer perceived service value (Ding et al., 2016). Perceived satisfaction may also be linked to intangible issues such as happy feeling related to the service experience (Flavián et al., 2006). Although multidimensional representations of perceived satisfaction are useful, some researchers have performed a unidimensional, overall evaluation of services. Empirical studies have demonstrated that direct measurement of service satisfaction can serve as predictors of behavioral intention rather than multidimensional measures. Therefore, we observe and measure perceived satisfaction as an overall assessment of self-service delivery service. Emotional processes constitute a powerful source of human motivation and have a major influence on people's choices (Forgas, 1995). Satisfactory psychological perception is the dominant factor in making positive behavioral decisions. In the context of online business, consumers often use emotional states as cognitive information (Hume and Mort, 2010). According to this pattern of behavior, we assume that perceived satisfaction will influence online consumers' decision to use self-service delivery service. Regarding the facilitating conditions, it is

H6b. Facilitating conditions has positive influence on online consumers' usage behavior of self-service delivery service. H6c. Facilitating conditions has positive influence on online consumers' behavioral intention of self-service delivery service.H7: Behavioral intention has positive influence on online consumers' usage behavior of self-service delivery service.

3. Methodology 3.1. Questionnaire design The questionnaire contains two parts. Part A collects socio-demographic information of respondents and Part B surveys respondents’ view about using self-service delivery service. Part A focuses on four demographic issues: age, gender, education level, and monthly income. Part B consists of 8 constructs and 28 items. The constructs including Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Perceived Risk (PR), Perceived Satisfaction (PS), Behavioral Intention (BI) and Usage behavior (UB). Each construct is measured by 3–4 items which were adapted from previous literature. Variables pertaining to the seven constructs were scored on a 7-point Likert scale, ranging from 1 = “strongly disagree” to 7 = “strongly agree”. The draft measure was reviewed by two experts from express industry and three academic professors. Experts from express industry have more than 10 years of operational management experience in the city's delivery services, and the academic professors have nearly 15 years of professional research experience in behavioral economics. Take into account their suggestions, 3 items were removed as they are easily misunderstood by respondents and obscure technical terms are replaced by colloquial words. The questionnaire was further revised after the pilot test and the final revision of measures for each construct is showed in Table 1. From August to October 2017, a pilot survey was conducted at Changsha to test the viability of the questionnaire and 56 responses 4

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Table 1 Measurement items. Constructs

Items

Source

Performance Expectancy (PE)

PE1 I think using SSDS will save my time. PE2 I think using SSDS will be more cost-effective. PE3 I think using SSDS will be more convenient. PE4 I think using SD will make me feel comfortable. EE1 I think it's easy for me to use SSDS. EE2 I think pick-up a package from SSDS is simple. EE3 I think mail a package by SSDS is simple. EE4 I think I can easily master all the operating procedures of SSDS. SI1 People who are important to me think I should use SSDS. SI2 People who affect my behavior think I should use SSDS. SI3 E-shopper who Using SSDS will be considered as a trendy person. FC1 I got adequate guidance to use SSDS. FC2 I got the necessary knowledge to use SSDS. FC3 SSDS is compatible with online shopping system. PR1 Financial risks of SSDS make me worried. PR2 Privacy risks of SSDS make me nervous. PR3 Performance risks of SSDS make me feel uncomfortable. PR4 Time risks of SSDS make me feel stressful. PS1 SSDS gives me more control over my daily lives. PS2 It is a pleasant experience to use SSDS. PS3 I feel perfectly satisfied with the operation way of SSDS. BI1 I am willing to adopt SSDS to pick-up or mail the packages. BI2 I intend to adopt SSDS in the future. BI3 I predict I would adopt SSDS. BI4 I think I will always try to adopt SSDS. UB1 I currently use SSDS. UB2 I will recommend SSDS to my friends or others. UB3 SSDS are my first choice When my online shopping needs a delivery.

(Magsamen-Conrad et al., 2015; Zhou et al., 2010)

Effort Expectancy (EE)

Social Influence (SI)

Facilitating Condition (FC)

Perceived Risk (PR)

Perceived Satisfaction (PS)

Behavioral intention (BI)

Usage behavior (UB)

(Dunn et al., 1998; Holden, 2011)

Magsamen-Conrad et al. (2015)

Wang et al. (2018)

Steinhilber et al. (2013)

Palmer et al. (2018)

Wang et al. (2009)

(Li et al., 2017; Wang et al., 2017)

SSDS: self-service delivery services.

3.2. Data description

were received. It pointed out the improvement measures of the questionnaire. For example, some elderly respondents cannot understand some privacy risks items in perceived risks construct. As a result, the research team modified these items to be more colloquial. After the questionnaire was revised, a formal street-intercept survey was conducted in seven cities in China from November 27, 2017 to April 20, 2018, including Beijing, Shanghai, Guangzhou, Shenzhen, Nanjing, Changsha and Wuhan. Beijing, Shanghai, Guangzhou and Shenzhen are the top four cities in GDP of China and the important target market for self-service delivery services. Nanjing, Changsha and Wuhan are active cities for technological innovation and application, and are cities most likely to promote self-service. Before the survey, the researchers first ensured that the respondent had at least one online shopping and selfservice delivery service experience. A total of 630 questionnaires were distributed by the seven research groups in these cities, and 525 valid responses were collected.

Table 2 shows the demographic characteristics of the sample. Around 60% of the participants were males. More than three quarters (77.0%) of participants were between the ages of 21–40, corresponding to the major age range of the online consumers(Chen et al., 2018). Most of the participants had bachelor or higher degree (59.6%), and more than 60% reported monthly income (before tax) between $ 1000 to $ 3000.

3.3. Statistical comparison Statistical comparisons were made with respect to the socioeconomic group of each participant. SPSS and AMOS for Windows were used as the analysis software to examine the data. To assess the validity of constructs, the confirmatory factor analysis (CFA), Cronbach's alpha and the average variance extracted (AVE) were adopted in the present study. The threshold values of the indicators are as follows: each standardized factor loading must be greater than 0.5, CR must be

Table 2 Sample demographics (N = 525). Variable

Options

Frequency

Valid Percent

Cumulative Percent

Mean

SD

Gender

Male (1) Female (2) Under 20 (1) 21–30 years old (2) 31–40 years old (3) Over 40 (4) Senior high school or lower (1) Vocational college (2) Bachelor degree or higher (3) Below $ 1000 (1) $ 1000 - $2000 (2) $ 2000 - $ 3000 (3) Over $ 3000 (4)

56.0 44.0 8.4 36.4 40.6 14.7 6.5 33.9 59.6 16.6 31.0 32.0 20.4

56.0 100.0 8.4 44.8 85.3 100.0 6.5 40.4 100.0 16.6 47.6 79.6 100.0

56.0 44.0 8.4 36.4 40.6 14.7 6.5 33.9 59.6 16.6 31.0 32.0 20.4

1.44

0.497

2.62

0.836

2.53

0.616

2.56

0.994

Age

Education

Monthly income before tax

5

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greater than 0.7, the Cronbach's alpha must be greater than 0.7, and AVE must be greater than 0.5 (Fornell and Larcker, 1981; MacKinnon et al., 2004). The discriminant of model validity is obtained by comparing the square root of the AVE and the cross-loading matrix. The square root of the AVE of each construct should be greater than the corresponding cross loading (McDonald and Ho, 2002). The following indicators were used to test the overall fitness of the measurement model: CHI /DF, NFI, IFI, RFI, P, TLI, CFI, GFI, AGFI, RMSEA, and the maximum likelihood method was used to calculate the parameters. The recommended values for these indicators are as follows: CHI/DF < 3; NFI > 0.9; IFI > 0.9; RFI > 0.9; TLI > 0.9; CFI > 0.9; GFI > 0.9; AGFI > 0.9; P-value < 0.05; RMSEA < 0.08 (Bollen and Lennox, 1991; MacKinnon et al., 2004). Structural equation modeling (SEM) and Bootstrapping methods were used to examine the hypotheses. The statistical tests were twotailed ones with 95% confidence intervals (CI), and the hypotheses were examined by factor analysis and path analysis. The moderating effects were tested by a regression analysis based on non-standardized coefficients between any two comparison groups. With the bootstrap estimation technique, confidence intervals were obtained through the theoretical model (Fornell and Larcker, 1981; Little et al., 2002; MacKinnon et al., 2004). To better understand the influence mechanism among variables in the theoretical model, we investigated the mediating role of facilitating conditions by using bias-corrected bootstrapping analysis. When assessing the effectiveness of the mediation model, it is necessary to consider the influence of the mediator variable on the main variables. With the bootstrap estimation technique, confidence intervals were obtained through the theoretical model (Fornell and Larcker, 1981; Little et al., 2002; MacKinnon et al., 2004). These estimates were used to calculate the exponential loadings and error variances for interactions and secondary latent variables. By the principle proposed by Baron and Kenny (1986a,b) and improved by Hayes (2009), the mediation effect analysis was carried out as the following steps (Baron and Kenny, 1986a; Bollen and Lennox, 1991; Hayes, 2009; Sobel, 1986). First, the total effect between independent variables and dependent variables was tested; then the indirect effects between them were tested; and the direct effect between them was examined in the final step. It is a necessary prerequisite for the mediating effect that the total effect and indirect effect are significant. If the direct effect is also significant, then the mediator is a “Partial Mediator”; otherwise it is a “Complete Mediator”. It is assumed that the data in empirical studies are homogeneous and that it is often unrealistic to represent a single population in social and behavioral sciences, such as information systems, management, and marketing (Rust and Verhoef, 2005). Unobserved heterogeneity can result in differences between measurement model weights and loadings across groups. The finite mixture models have been developed to uncover unobserved heterogeneity that extend the structural equation model based on multigroup of covariances (Dolan and van der Maas, 1998; Jedidi et al., 1997). In the current study, six factors affecting Behavioral intention (BI) were included in the test model, Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Condition (FC), Perceived Risk (PR) and Perceived Satisfaction (PS), and a mixed model (Jedidi et al., 1997) was adopted to uncover the unobserved heterogeneity.

Table 3 Confirmatory factor analysis (N = 525). Constructs

Variables

t-value

λ

C.R

AVE

Cronbach's Alpha

PE

PE1 PE2 PE3 PE4 EE1 EE2 EE3 EE4 SI1 SI2 SI3 FC1 FC2 FC3 PR1 PR2 PR3 PR4 PS1 PS2 PS3 BI1 BI2 BI3 BI4 UB1 UB2 UB3

– 19.279 20.877 20.081 – 20.826 21.645 22.276 – 19.622 18.785 – 21.36 21.078 – 19.645 22.417 24.342 – 18.869 18.584 – 22.044 22.342 22.668 – 26.671 26.288

0.804 0.789 0.845 0.816 0.88 0.774 0.795 0.811 0.781 0.903 0.798 0.846 0.847 0.831 0.888 0.734 0.801 0.847 0.796 0.845 0.815 0.797 0.858 0.867 0.877 0.865 0.897 0.886

0.887

0.662

0.886

0.888

0.666

0.887

0.868

0.687

0.866

0.879

0.708

0.878

0.891

0.672

0.889

0.859

0.671

0.858

0.912

0.723

0.9111

0.914

0.779

0.913

EE SI

FC

PR

PS BI

UB

λ = Standardized factor loading; CR = Composite Reliability; AVE: Average Variance Extracted. Table 4 Constructs' correlations and square roots of AVE.

PE EE SI FC PR PS BI UB

PE

EE

SI

FC

PR

PS

BI

UB

0.814 0.261 0.295 0.319 −0.257 0.362 0.557 0.376

0.816 0.299 0.325 −0.274 0.305 0.503 0.329

0.829 0.206 −0.284 0.332 0.510 0.346

0.841 −0.279 0.411 0.564 0.532

0.820 −0.586 −0.549 −0.203

0.819 0.603 0.607

0.850 0.629

0.883

latent variable used in this study is higher than 0.80, and AVE is higher than 0.50, indicating that the measurement model has strong convergence validity (MacKinnon et al., 2004). As shown in Table 4, each diagonal element is the square root of the AVE on the corresponding construct, and the entries in the corresponding columns and rows list the cross-loadings of the associated constructs. The results show that all the structures meet the requirements and demonstrate that the discriminant validity of the data is acceptable. The proposed structural model fits well with the observed data (CHI/df = 1.750 < 3, NFI = 0.943 > 0.9, IFI = 0.975 > 0.9, RFI = 0.935 > 0.9, TLI = 0.971 > 0.9, CFI = 0.975 > 0.9, GFI = 0.929 > 0.9, AGFI = 0.913 > 0.9, P = 0.00 < 0.05, RMSEA = 0.038 < 0.05). The results of the finite mixture analysis suggest that the data are homogeneous. All the reliabilities in the three-group solution have significant loadings, suggesting a good fit. The mean levels for the behavioral intention constructs across groups are given in the first panel of Table 5. The results show that Segment 3 (33.8% of the sample) has the highest mean behavioral intention levels across all constructs. Segment 2 (28.9% of the sample) has the lowest mean behavioral intention levels for all constructs. However, the mean differences for all

4. Results and discussion 4.1. Reliability and validity of the measurement model Table 3 summarizes the standardized loading of the items of each construct, average variance extracted (AVE), composite reliability (CR) and Cronbach alpha values. Cronbach's alpha values are exceeded criteria (0.80), which indicates all constructs are reliable (Bollen and Lennox, 1991; Hair et al., 2012). The composite reliability of each 6

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Table 5 Parameter estimates for the aggregate and three-segment solutions. Factor mean scores

Structural Parameters Finite Mixture

Factor

Segment 2

η Behavioral intention ζ1 Performance Expectancy ζ2 Effort Expectancy ζ3 Social Influence ζ4 Facilitating Condition ζ5 Perceived Satisfaction ζ6 Perceived Risk Mixing Proportions

3.241 4.344 4.397 4.256 3.427 4.743 3.906 –

a

(0.615) (0.822) (0.915) (0.587) (0.898) (0.915)

Segment 3 5.045 5.216 4.994 4.978 3.900 3.996 4.840 –

b

(0.867) (0.731) (1.273) (0.524) (1.105) (1.132)

Aggregate Solution – 0.302*** (0.043) 0.225*** (0.043) 0.235*** (0.039) 0.291*** (0.044) 0.17*** (0.043) −0.171*** (0.036) –

Segment 1

Segment 2

Segment 3

– 0.373*** (0.059) 0.204*** (0.058) 0.224*** (0.047) 0.273*** (0.057) 0.141** (0.059) −0.158*** (0.047) 0.337

– 0.258*** (0.06) 0.211*** (0.055) 0.209*** (0.07) 0.284*** (0.065) 0.134** (0.061) −0.16*** (0.053) 0.339

– 0.319*** (0.062) 0.233*** (0.065) 0.159*** (0.047) 0.312*** (0.06) 0.124* (0.064) −0.191*** (0.052) 0.324

*** Denotes significant at the 0.001 level; ** Denotes significant at the 0.01 level; * Denotes significant at the 0.05 level. a Segment 1 is the reference group (i.e., full sample). b Standard errors are in parentheses.

Behavioral intention (BI) and Usage behavior (UB). R square that points to each latent variable represents the residual variance not explained by the latent variables (Fig. 2). The model estimated that 31% of the variance of behavioral intention is explained by performance expectancy, effort expectancy, social influence, facilitating condition, perceived satisfaction and perceived risk, and 59% of the variance of perceived satisfaction is explained by facilitating condition and perceived risk. Model indices demonstrate adequate fit and 67% of the variance in usage behavior is explained by facilitating condition, perceived satisfaction and behavioral intention.

latent variables are insignificant across all groups. Hence, the manager might consider steps to improve the behavioral intentions of all groups, not a certain segment.

4.2. Hypothesis testing Hypothesis testing results are shown in Table 6. Performance expectancy (β = 0.255, p < 0.001), effort expectancy (β = 0.186, p < 0.001) and social influence (β = 0.220, p < 0.001) are shown to positively affect online consumers' behavioral intention to use selfservice delivery service, so H1, H2 and H3 are supported. The results of H4a and H4b indicated that perceived risk has a significantly negative effect on online consumers' behavioral intention (β = −0.198, p < 0.001) and perceived satisfaction (β = −0.499, p < 0.001), and thus H4a and H4b are supported. Moreover, perceived satisfaction has a positive impact on both online consumers' behavioral intention (β = 0.173, p < 0.001) and usage behavior (β = 0.298, p < 0.001), so H5a and H5b are supported. Results of H6a and H6b indicated that facilitating conditions positive effect on perceived satisfaction (β = 0.286, p < 0.001), online consumers' usage behavior (β = 0.240, p < 0.001), and behavioral intention (β = 0.257, p < 0.001) thus these two hypotheses are supported. Furthermore, behavioral intention has a positive impact on online consumers’ usage behavior of self-service delivery service (β = 0.312, p < 0.001), and H7 is supported. In general, all hypotheses are supported by data, indicating that the fit of the structural model and data is reasonable (Fig. 2). These results support the predictive validity of the theoretical model for users’ intention to use self-service parcel delivery service. By looking at the R2 values, the general factors, Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Condition (FC) and Perceived Risk (PR), are able to predict Perceived Satisfaction (PS),

4.3. Mediating and moderating effects The results of the four mediation effects are listed in Table 7. As a partial mediator variable, perceived satisfaction affects three paths: BI < — PR, UB < − FC, and BI < − FC. Results of Sobel Z test (−6.508, 6.271, and 6.163) show that the total effects of these paths are significant, and the confidence interval from Bootstrap results does not include zero, and this further confirming that the indirect effects are significant. The mediating effect ratios of perceived satisfaction among three paths of are 45.57%, 36.08% and 32.71%. The behavioral intention also acts as a partial mediator in path of UB < − FC, Sobel Z test is 7.325 and the mediating effect ratio is 51.06%. The method of Ping was used to test for moderating effects (Ping, 1996). To assess significance of the moderating effects, the incidence of non-significant coefficients and lack of fit produced by ML estimates was observed, as well as two convenient less distributed dependent estimators and the asymptotic distribution free (ADF) estimators. By constructing the moderating variable Mo and putting it into the theoretical model to test its significance, the regression results show that perceived satisfaction is a significant moderator (λ = 0.636, S.E. = 0.054, C.R. = 18.118, p < 0.001) in the path of UB < − BI.

Table 6 Hypothesis testing. Hypotheses

Paths

H1 H2 H3 H4a H4b H5a H5b H6a H6b H6c H7

BI BI BI BI PS BI UB PS UB BI UB

5. Discussion

<— <— <— <— <— <— <— <— <— <— <—

PE EE SI PR PR PS PS FC FC FC BI

t-value

β

p

Comments

6.977 5.224 6.089 −4.716 −10.574 3.915 6.142 6.333 4.905 6.539 5.899

0.255 0.186 0.220 −0.198 −0.499 0.173 0.298 0.286 0.240 0.257 0.312

*** *** *** *** *** *** *** *** *** *** ***

Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported

The main purpose of the present study is to investigate how multiple psychological factors influence online consumers' perceptions and behaviors to self-service delivery services. Perceived risk and perceived satisfaction were incorporated into the extended UTAUT theoretical model as the two main factors affecting behavioral intentions and usage behavior. Empirical results demonstrate that performance expectancy, effort expectancy, social influence and facilitating conditions are positive determinants on online consumers’ usage behavior to self-service delivery service, while perceived risk was negatively factor to behavioral intention. Study results also provide evidence for the predictive validity of self-service delivery services. The 67% of explained variance in usage behavior is similar to or higher than that in other studies (Ege

***p < 0.001. 7

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Fig. 2. Results of structural equation modeling.

on online shopping. The comments from online assessment system will obviously affect the online shopper's psychological expectations of products or services, thus affecting their decision-making (Lee et al., 2017). For self-service delivery service, the positive experience shared by friends or others will significantly enhance the online consumers' willingness to adopt it. Previous experience is an important factor in determining consumer behavior in many fields, such as online purchasing (Cowley, 2007), rural hospitality industry (Pena et al., 2013), restaurant (Velazquez et al., 2010), and purchase intention in emerging countries (Diallo and Siqueira, 2017). The current manuscript also assumes it may significantly affect online shoppers' behavior for selfservice express services, then UB2 and UB3 are set in construct of Usage behavior (UB). Empirical results show that previous experiences are significant variables that influence consumer behavior, which is consistent with previous research in other fields. Perceived risk is a significant negative psychological factor on perceived satisfaction and behavioral intention, as the structural analysis results. It is consistent with previous consumer behavior research findings. In the context of online shopping, the consumer's perceived risk had a negative relationship with satisfaction, but the website functionality positively associated with satisfaction (Tandon et al., 2018). Perceived risk act as functional and psychological barriers that may arise negative consequences and reduce consumers' action intentions (Martins et al., 2014). A integrated model examines the determinants of the technology acceptance of near-field communication

Oruç and Tatar, 2017; Khalilzadeh et al., 2017). The 33% of residual on usage behavior could be potentially influenced by user variance (e.g., gender, age, education, monthly income before tax), or other factors not included in the theoretical model. Performance expectancy, effort expectancy and social influence have a positive impact on behavioral intention, and these findings are consistent with previous studies, then confirmed that the UTAUT model has a strong explanatory power in the behavioral intent of new technology applications. In the context of technology applications such as Automated Road Transport Systems (ARTS), telemedicine, and NFC, performance expectancy is the strongest influencing factor for behavioral intention (Khalilzadeh et al., 2017; Madigan et al., 2016). The following variables are important factors to influence consumers' choice of last mile delivery service: time savings (Kim et al., 2017), cost or price (Li and Jain, 2016; Peine et al., 2009), delivery efficiency (Wang and Lan, 2015; Xu et al., 2014). In most empirical studies based on UTAUT model, effort expectancy is a significant but weak variable that influences behavioral intentions (Bhatiasevi, 2016; Jewer, 2018; Kijsanayotin et al., 2009). The results of the current study partially support previous studies but have new findings. Effort expectancy is a significant positive psychological factor to influence behavioral intentions. The self-service delivery services involve new technologies, new business models and new privacy protections, and the online consumers feel it's hard to use. Social influence also has a positive impact on behavioral intentions, and this finding supported by behavioral research Table 7 Mediating effects. Mediator and path

Sobel Z

PS BI < — PR −6.508 PS UB < − FC 6.271 PS BI < — FC 6.163 BI UB < — FC 7.325

Effects

Total Effects Direct Effects Indirect Effects Total Effects Direct Effects Indirect Effects Total Effects Direct Effects Indirect Effects Total Effects Direct Effects Indirect Effects

Estimate

−0.474 −0.258 −0.216 0.668 0.427 0.241 0.584 0.393 0.191 0.613 0.300 0.313

Product of Coefficients

Bias-Corrected 95% CI

Percentile 95% CI

S.E.

Z

lower

upper

lower

upper

0.038 0.047 0.037 0.053 0.059 0.038 0.050 0.049 0.031 0.048 0.064 0.046

−12.474 −5.489 −5.838 12.604 7.237 6.342 11.680 8.020 6.161 12.771 4.688 6.804

−0.550 −0.346 −0.298 0.576 0.317 0.174 0.495 0.302 0.136 0.522 0.176 0.227

−0.402 −0.163 −0.152 0.780 0.545 0.323 0.685 0.490 0.258 0.717 0.422 0.406

−0.549 −0.345 −0.296 0.574 0.314 0.175 0.494 0.299 0.136 0.520 0.167 0.231

−0.402 −0.159 −0.152 0.778 0.543 0.324 0.684 0.488 0.258 0.714 0.418 0.410

Note: 2000 bootstrap samples. 8

Comments

Partial Mediation 45.57%

Partial Mediation 36.08%

Partial Mediation 32.71%

Partial Mediation 51.06%

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6. Implications and future research directions

(NFC) based mobile payment (MP) in the restaurant industry, and the results of data collected from 412 restaurant customers show that risk, security and trust have the significant impact on customers' behavioral intentions to use this technology; and the study further demonstrate these factors are also important determinants, with direct and indirect effects, of other key structures, such as effort expectancy, hedonic and utilitarian performance expectancy, attitude, and behavioral intention (Khalilzadeh et al., 2017). In addition, the proposed model in current manuscript was tested via structural equation modeling. The potential disclosure risk of customer privacy data, possible financial risk from online payment systems, performance risk and time risk caused by system operations are key factors for self-service delivery service to be successfully accepted by consumers. Previous studies have made contributions to extend the theoretical framework of UTAUT. The extensions are helpful to expand the expand the scope and generalizability of model by adding or modifying some constructs, such as Perceived Credibility, Perceived Financial Cost, Perceived Convenience, Security, Trust, Attitude, Price Value (Bhatiasevi, 2016; Cimperman et al., 2016; Khalilzadeh et al., 2017). Current manuscripts add new knowledge to the UTAUT theoretical framework, and perceived risk can serve as a new construct that affects consumer behavior. Facilitating conditions has positive influences on online consumers' usage behavior of self-service delivery service. A new technology application will change the past consumption behavior patterns and habits, which will bring uneasy psychological perception to consumers (Davis and Venkatesh, 1996). Good facilitating conditions are effective measures to reduce this psychological unease feeling. For example, a convenient APP application will help consumers to use online banking, and the health information sharing technology can transfer the family health monitoring data to the doctor's diagnosis and treatment system in real time, which will promote elderly people to use mHealth systems (Hoque and Sorwar, 2017). The positive and significant effects of facilitating conditions on usage behavior was confirmed as in the previous literature. The results from mediation analysis and moderating analysis show that perceived satisfaction is a pivotal psychological variable and should not be ignored. In the consumer behavior research of e-commerce, there are four dimensions related to customer satisfaction of eretail, including timeliness, availability, condition, and billing accuracy, while customer satisfaction predicts two key consequences: purchase intention and word-of-mouth (Koufteros et al., 2014). There are some interesting new discoveries in the current research: (1) Perceived satisfaction has a significant and direct impact on usage behavior. Previous studies on consumer behavior have affirmed the positive effects of satisfaction on promoting purchasing intention, such as increasing consuming interests and possible favorable comments, but few documents have shown that it directly and positively influence the consuming behavior (Flavián et al., 2006). (2) Perceived satisfaction is a mediating variable that significant affects two paths: behavioral intention < — perceived risk and usage behavior < — facilitating conditions. Perceived risk negatively affects perceived satisfaction, thereby changing the online shopper's psychological expectations and enhancing the impact on behavioral intentions. Good facilitating conditions improves perceived satisfaction and ultimately increases the possibility of adopt behavior. This finding confirms the positive effects of satisfaction on E-customers’ trust and loyalty, and ultimately affect consumer behavior (Flavián et al., 2006; Koufteros et al., 2014). (3) Perceived satisfaction affects behavioral intention and usage behavior, moreover, it significantly enhances the positive impact of behavioral intention on usage behavior. The moderating effect of perceived satisfaction explains the need for positive stimulation derived from this psychological factor, and then realize the leap from intention to behavior (Baron and Kenny, 1986b).

6.1. Theoretical implications To the best of our knowledge, this study for the first time explored the mediating role of perceived risk and perceived satisfaction on a relationship between psychological factors and behavioral intention of online shoppers to use self-service delivery services. This aspect further enhances the explanatory power of UTAUT theoretical model in the context of self-service delivery services thus extending the literature and stock of knowledge on the subject. Perceived risk and perceived satisfaction as decisive psychological factors were included in theoretical model, the empirical results show that the perceived risk and perceived satisfaction affect the adoption behavior. Previous studies have primarily focused on optimization at the level of technology (information technology and delivery technology), such as analysis on optimization of deliver paths and deliver time, reducing operating costs and improving delivery efficiency in the last-mile problem (Boyer et al., 2009; Wang et al., 2014). Although these optimization models and empirical analysis are very valuable, they cannot change the online consumers’ psychological expectations and perceived satisfaction of delivery services (Lee and Whang, 2001; Wang et al., 2016). Online retailers can only provide limited knowledge and customer interaction, and the service is often considered unsatisfactory due to technical complexity and low user awareness (Chen et al., 2018; Kokkinou and Cranage, 2015). As a significant negative variable, perceived risk increases the consumers' opportunity cost of adoption new technologies and affects psychological expectations. Perceived satisfaction is not only the results of independent variables, such as facility conditions and perceived risk, but also determinant of the consumers' behavior. The empirical results also confirm that perceived satisfaction plays a multiple role of mediator and moderator, and as a pioneer variable for online consumers to adopt self-service delivery services. An extended theoretical model was conducted to better understand the online shoppers' behavioral intentions of self-service delivery services. The extended UTAUT model emphasizes the importance of user perception, the subjective feelings of customers, which are influenced by external environment and internal factors, including emotional variables and cognitive factors. Empirical studies confirm the UTAUT model's explanatory power for consumers' intention to accept new technologies. 6.2. Managerial implications The study provides some important implications for online retailers and express delivery companies who plan to conduct self-service courier services. Since perceived risk is a significant negative determinant of behavioral intention, marketers need to pay attention to consumers' psychological perception of risk. Consumers believe it will be riskier to use self-service delivery services rather than home delivery when shopping online, including the financial risk, security/privacy risk, performance risk and time risk. E-retailers and shippers should strengthen risk management by optimizing service process management, courier management, and secure information technology, then eliminate their misunderstandings about it. Perceived satisfaction acts as a partial mediator that regulates the relationship between perceived risk and behavioral intention, facilitating conditions and behavioral intention, facilitating conditions and usage behavior. Online retailers need to understand the formation of perceived satisfaction and explore appropriate communication, then to enhance online shoppers' knowledge of self-service delivery services, smart express cabinets, and improve their shopping experience. The pertinent issues like increasing labor costs and urban traffic pressures are threatening the “last mile” delivery service with pervasive impact on the development of online retail. Hence, more and more consumers are expected to shift towards 9

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efficient self-service delivery services, and this influence is more dominant in countries with developed online retail. At this juncture, it is important for retailers to incorporate the services of self-service delivery into their marketing strategies, and increase the perceived satisfaction of customers' online shopping by avoiding the consumption burden caused by the adoption of new technologies. We believe that current research will help them in understanding and adjusting some strategies of online retails, such as factors that may affect consumer behavioral intentions. Retailers may have to move away from the traditional delivery ways and develop new service models for online consumers.

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6.3. Limitations and future research directions Despite the interesting findings of the study, several limitations of the study are acknowledged. The first one is the sample size. Larger sample sizes from different geographic regions may be more conducive to better understanding the interaction mechanisms of psychological factors. In the context of the rapid spreading of self-service delivery service, future research should investigate enough data in broader range of region to test the generalizability of the current findings. Second, e-commerce provided new business models for consumers, such as mobile-online shopping, wireless payments and communication. These models are favorited by young people who are willing to try new things and it has become the mainstream form of e-commerce in recent years. Due to the difficulty of the investigation, the current research does not specifically survey the behavioral intentions of m-commerce consumers using self-service delivery services, so the control effects of this variable were not analyzed. Finally, there are two limitations on theoretical models and analytical methods. One is the psychological variable about “behavior habit or experience”, we were not test the impact of this variable to affect the user acceptance of the new technology. In the previous literature of UTAUT-2, “habit” is one of construct that affects the behavioral intention (Alalwan et al., 2018; Herrero et al., 2017). Since self-service delivery services are new technologies in recent years, most consumers have not yet formed relevant habits. From the future perspective, “behavior habit or experience” should be incorporated into the theoretical model, when consumers have become accustomed to self-service delivery services. The other is about the Importance-Performance Map Analysis (IPMA). IPMA contrasts the total effects, representing the predecessor constructs’ importance in shaping a certain target construct, with their average latent variable scores indicating their performance (Fornell et al., 1996). It provides guidance for the prioritization of managerial activities of high importance but require performance improvements (Henseler et al., 2016). In future research, IPMA should be included in research methods to extend findings, conclusions, and managerial recommendations. Declarations of interest None. Funding This work was supported by the National Natural Science Foundation of China (NSFC) [grant number 71601043, 71704052]; the Social Science Fund of Hunan Province, China [grant number 15YBA238]; and 2017 Youth Innovation Driven Project in Hunan University of Technology and Business, China [grant number 17QD06]. References Alalwan, A.A., Dwivedi, Y.K., Rana, N.P., Algharabat, R., 2018. Examining factors influencing Jordanian customers' intentions and adoption of internet banking: extending UTAUT2 with risk. J. Retail. Consum. Serv. 40, 125–138. https://doi.org/10. 1016/j.jretconser.2017.08.026.

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Min Zhou. He is an associate professor in Hunan University of Technology and Business. He was also worked as a postdoctoral at Southeast University and Purdue University. He received the Ph.D. in system engineering from Central South University in 2013. His research interests concern health service behavior and decision making. He has published over 20 papers peer-reviewed journal articles on international journals and conference proceedings. His research has been or currently being funded by the National Natural Science Foundation of China, China Development and Reform Commission, and China Health and Health Commission. Lindu Zhao. He is a professor in School of Economics and Management, Southeast University. He chairs the Economics and Management since 2013. He received his Ph.D. in 1997 from the Southeast University. He is an associate editor of International Journal of Biomedical Soft Computing and Human Sciences (2008-), International Journal of

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Journal of Retailing and Consumer Services 52 (2020) 101911

M. Zhou, et al. Innovative Computing & Information Control (2009-), Innovative Computing Information and Control–Express Letters (2007-), Asia Pacific Journal of Finance and Banking Research (2013-). His research interests concern the supply chain and logistics management.

Ge Xu. She is an associate professor in Hunan University of Technology and Business. She received her Ph.D. in Business Administration from Central South University in 2018. Her research interests include ecological civilization and green development. She has published over 10 peer-reviewed journal articles.

Nan Kong. He is an associate professor in the Weldon School of Biomedical Engineering at Purdue University. He received his PhD in industrial engineering from the University of Pittsburgh in 2006. His research interest includes healthcare operations management, in particular healthcare network design, provider staff scheduling, and within-network patient flow control. Recently, he has expanded his research to machine learning for biomedicine. He has published over 50 peer-reviewed journal articles. He is an Associate Editor for the IIE Transactions on Healthcare Systems Engineering and for the International Conference on Automation Science and Engineering. His research has been or currently being funded by the National Science Foundation, National Cancer Institute, Agency for Health Research and Quality, Centers for Medicare and Medicaid Services, and Air Force Office of Scientific Research. He is currently President of the Public Sector OR Section in INFORMS and Committee Chair for the INFORMS Undergraduate Operations Research Prize.

Guiju Zhu. She is an assistant professor in Hunan University of Technology and Business. She received her Ph.D. in Business Administration from Central South University in 2016. Her research interests include healthcare operations management and consumer behavior. She has published over 10 peer-reviewed journal articles. Xianye Cao. She is an assistant professor in Hunan University of Technology and Business. She received her Ph.D. in Business Administration from Central South University in 2019. Her research interests include operations management and consumer behavior. She has published 5 peer-reviewed journal articles. Song Wang. He is an associate professor in Hunan University of Technology and Business. He received the Ph.D. in industrial economics from Chinese Academy of Social Sciences in 2011. His research interests concern the industrial economics and behavioral operations research. He has published over 10 papers in international journals and conference proceedings.

Kathryn S. Campy. He is a professor in University of Pennsylvania. He received his Ph.D. in 1990 from Stanford University. He has over 45 years of professional experience in management. From 1985 to 2015, he served as professor at the University of Pennsylvania, published more than 40 academic papers and books.

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