Factors Influencing on Online Shopping Attitude and Intention of Mongolian Consumers

Transcription

1 Facors Influencing on Online Shopping Aiude and Inenion of Mongolian Consumers Shu-Hung Hsu, Assisan Professor, Deparmen of Business Adminisraion, Nanhua Universiy, Taiwan Ba-Erdene Bayarsaikhan, MBA, Deparmen of Business Adminisraion, Nanhua Universiy, Taiwan ABSTRACT The purpose of his sudy was o invesigae Mongolian consumer percepion of online shopping, as well as he facors influencing on heir aiude oward online shopping and heir effec on heir inenion oward online shopping. Sample of his sudy included online shopping consumers of Mongolian. The sudy used e-survey o collec daa wih 107 (10.7%) valid daa. The regression analysis was used o analysis he relaionship beween dependen and independen variables, and discovered mediaion. The resuls of his sudy found ha consumer innovaiveness, perceived benefis and perceived risk are imporan deermining facors influencing online shopping. Also he findings shown ha consumer innovaiveness, perceived benefis had posiive impac on consumer shopping aiude, and perceived risk had a negaive impac on consumer online shopping aiude. Moreover, consumer innovaiveness, perceived benefis, perceived risk had an indirec effec on he inenion of online shopping. The resuls of his sudy nearly suppored all hypoheses. Keywords: E-commerce, Consumer Innovaiveness, Perceived Benefis, Perceived Risk, Online Shopping Aiude, Online Shopping Inenion. INTRODUCTION E-commerce referred o he buying and selling of producs, services, ha hrough elecronic nework. Online shopping was one of he mos imporan aciviies of E-commerce. Online shopping aciviy was broadly defined, included finding online reailers and producs, searching for produc informaion, selecing paymen opions, communicaing wih oher consumers, and purchasing producs or services (Cai & Cude, 2008). Many online reail oules made a noiceable effor o reach Mongolian consumers, bu mos of hem have suffered loss and have deraced heir invesmen because of insufficien cusomer base. Therefore, he online shopping behavior of Mongolian consumers needed o reveal. The usage of he Inerne for purchasing and selling aciviy has changed he pah o he buyer-sellers relaionship. Prior sudies ha conduced sudy was focused on developed counries of heir consumers online shopping behavior and influencing facors. The online shopping behavior and inenion of developing counries consumers would be he ineresed issue o reveal. Mongolian consumer s online shopping behavior can conribue o exising lieraures of online shopping behavior. Purpose This sudy invesigaed wha was Mongolian consumers online shopping behavior and influencing facors. The main objecives were: 1) he sudy provided he behavioral model o beer undersand Mongolian consumer s online shopping behaviors, and 2) he sudy deermined he influencing facors impaced on he online shopping inenions of Mongolian consumers. The Journal of Inernaional Managemen Sudies, Volume 7 Number 2, Ocober,

2 LITERATURE REVIEW E-commerce E-commerce referred o a wide range of online business aciviies for producs and services (Rosen, 2000). E-commerce was he use of elecronic communicaions and digial informaion processing echnology doing business ransacions o creae, ransform, and redefine relaionships for value creaion beween or among organizaions (Andam, 2003). Elecronic Commerce (e-commerce) is he process of buying, selling ransferring, or exchanging producs, services and /or informaion via compuer neworks (Turban e al., 2008). Schneider (2008) menioned ha E-commerce includes many aciviies, such as business rading wih oher businesses and inernal processes. Companies use E-commerce o suppor heir buying, selling, hiring, planning, and oher aciviies (Schneider, 2008). There are several major ypes of e-commerce are business-o-business (B2B), business o consumer (B2C), governmen o business (G2B), consumer o consumer (C2C), and consumer o business (C2B) (Andam, 2003; Schneider, 2008; Turban e al., 2008). Consumer Innovaiveness The concep of consumer innovaiveness was conneced o he new produc or new service adopion process ha received considerable aenion (Hirschman, 1980; Midgley & Dowling, 1978; Rogers & Shoemaker, 1971). Rogers and Shoemaker (1971) defined consumer innovaiveness was he degree which an individual adoped an innovaion before oher members of his or her social sysem. Hirschman (1980) defined consumer innovaiveness is personaliy rai ha relae o individual s desire o seek new simuli. Consumer innovaiveness was he degree o which an individual is relaively earlier in adoping an innovaion han oher members of sysem (Rogers, 2003). Early adopers conribued o a new produc or service's iniial sales, and provided imporan word of mouh communicaion abou he new produc o laer adopers (Cirin e al., 2000). Perceived Benefis Wu (2003) defined ha perceived benefis was he consumers needs or wans he sum of online shopping advanages or saisfacions. Perceived benefis of shopping online were he consumer s subjecive percepion of gain from shopping online (Forsyhe e al., 2006). Also Kim e al. (2008) defined perceived benefi is as a consumer's belief abou he exen o which he or she will become beer off from he online ransacion wih a cerain online shopping. Bagdoniene and Zemblye (2009) found he main reasons ha Lihuanian consumers shop o online are he convenience, produc variey, purchase surrounding, informaion, and brand. Forsyhe e al. (2006) idenified ha he four dimensions of perceived benefis of online shopping were shopping convenience, produc selecion, ease/comfor of shopping, and hedonic/enjoymen. Shopping convenience was an imporan dimension of perceived benefis, paricularly in he online shopping conex. Produc selecion was defined as he availabiliy of a wide range of producs and produc informaion o suppor consumer decision making wih an imporan benefi of online shopping (Forsyhe e al., 2006). Ease/comfor of shopping was hough of as avoiding he physical and emoional hassles of shopping in oher channels (Forsyhe e al., 2006). Hedonic/enjoymen was defined as o do wih he fun and exciemen experience by rying new experiences, cusom designing producs, ec. (Forsyhe e al., 2006). Perceived Risk Perceived risk was consumer s percepions of he uncerainy, and advised consequences of buying a produc or service (Dowling & Saelin, 1994). Forsyhe and Shi (2003) defined perceived risk in online shopping as he subjecively deermined expecaion of loss by an Inerne shopper. Pavlou (2003) defined perceived risk as consumers subjecive fear of suffering a loss in pursui of a desired oucome. Perceived risk is a consumer's belief abou he poenial uncerain negaive oucomes from he online ransacion (Kim e al., 2008). Perceived risk considered as he main barrier o online shopping (Bhanagar e al., 2000; Kau e al., 2003; Forshye & Shi, 2003; Kim e al., 2008). Bhanagar e al. (2000) found ha percepion of risk significanly decreases he likelihood ha an individual will purchase goods or services online. 168 The Journal of Inernaional Managemen Sudies, Volume 7, Number 2, Ocober, 2012

3 Researchers classified various ypes of perceived risk, such as he risk dimensions ypically consideredd were economic risk, privacy risk, personal risk, and performance risk (Jarvenpaa & Todd, 1996). Bhanagar e al. (2000) classified wo ypes of risk ha were produc caegory risk and financial risk. Vijayasarahy (2003) menioned wo dimension of perceived risk including privacy risk and securiy risk. Consumer personal informaionn and heir browsing and shopping habis can be capured online, and he poenial opporuniy for misusing informaion may elevae he degree of privacy risks o unaccepable levels (Vijayasarahy, 2003). Vijayasarahyy (2003) defined securiy was he exen o whichh a consumer believes ha making paymens online is secure. Online Shopping Aiude Aiude was a predisposiion o behave in a consisenly favorable or unfavorable way o produc, service, or mehod of conducing commerce (Schiffman & Kanuk, 2000). Vijayasarahy (2003) defined aiude as he exen which a consumer likes online shopping, and considered i o be a good idea. Undersanding consumer aiudes oward online shopping can help markeing managers predic he online shopping rae and evaluae he fuure growh of online shopping (Wu, 2003). Davis (1989) proposed ha he echnology accepance model (TAM) explain he poenial user s adop or use of new informaion sysem (IS) or new informaion echnology (IT). TAM was based on he heory of reasoned acion (TRA) (Fishbein e al., 1975, 1980). I assumed ha user accepance of echnology can be explained wo primary beliefs, including perceived ease of use and perceived usefulness, as deerminans of aiude owards using and inenions o use, shown as Figure 1. Figure 1: Technology Accepance Model Noe. From Perceived usefulness, perceived ease of use, and user accepance of informaion echnology, by F. D. Davis (1989), MIS Quarerly, 13 (3), p The TAM had shown ha a consumer s aiude owards using an informaion echnology impacs he acual usage of he sysem. Aiude referred o he degree which a person has a favorable or unfavorable evaluaion or appraisal of he behavior (Ajzen, 1991). Jarvenpaa and Todd (1997) found ha online shopping benefis were posiively associaed wih aiude and inenions oward online shopping. Online shopping Inenion Sopping inenion does no equae o acual purchase behavior, i has been demonsraed ha measures of purchase inenion do possess predicive usefulness (Jamieson & Bass, 1989). Fishbein and Ajzen (1975, 1980) developed he heory of reasoned acion (TRA), shown as Figure 2. Theory of reasoned acion proposed ha behavioral inenion was a funcion of aiude oward behavior and subjecive norms. The Theory of Reasoned Acion (TRA) proposes ha people form inenions o adop a behavior or echnology based on heir beliefs abou he consequences of adopion. The Journal of Inernaional Managemen Sudies, Volume 7 Number 2, Ocober,

4 Figure 2: Theory of Reasoned Acion Noe. From Belief, aiude, inenion, and behavior: An inroducion o heory and research, by M. Fishbein and I. Ajzen (1975), Addison-Wesley, USA. The Theory of Planned Behavior (TPB) (Ajzen 1985, 1991) exended he Theory of Reasoned Acion (Fishbein & Ajzen 1975) o posi ha aiude oward a behavior, subjecive norm, and perceived behavioral conrols are he prior of inenion o perform a behavior, shown as Figure 3. Figure 3: Theory of Planned Behavior Noe. From The heory of planned behavior, by I.Ajzen (1991), Organizaional Behaviour and Human Decision Process, 50, p Ajzen and Fishbein (1980) demonsraed ha behavior can be prediced by inenions, and ha inenions were deermined by aiude and subjecive norms. Inenions represen he srengh of an individual s plans o perform a specific behavior. Also he main facor in he heory of planned behavior was he individual s inenion. Inenions were assumed o capure he moivaional facors ha influence a behavior. Consumers were indicaions of how hard people were willing o ry, of how much of an effor hey were planning o exer, in order o perform he behavior (Ajzen, 1991). Online shopping aiude and Inenion The researchers (Teo, 2001; Wu, 2003; Chiu e al., 2005; Vijayasarahy, 2003; Chang e al., 2008; Laohapesang, 2009) had exensively adoped or based on he heory of planned behavior (TRP) (Ajzen, 1985, 1991) and he echnology accepance model (TAM) (Davis, 1989) o explain or predic consumer online shopping aiude, and online shopping inenion. Chang e al. (2005) observed six sudies of aiude oward online shopping and all sudies showed aiude oward online shopping significan posiive impac on online shopping inenion and behavior. Vijayasarahy (2003) conduced a sudy o examine consumer shopping inenions and augmen echnology accepance model. The sudy resuls indicaed ha posiive associaed wih he consumers online shopping aenion and online shopping aiude. Donhu and Garcia (1999) found ha consumer innovaiveness posiively influenced online shopping behaviors and online shopping inenion, he direc effecs being mediaed by aiude. Goldsmih (2002) sudy indicaed ha consumer innovaiveness posiively influenced on he online shopping aiude. 170 The Journal of Inernaional Managemen Sudies, Volume 7, Number 2, Ocober, 2012

5 METHODOLOGY Theoreical Framework Based on he lieraure, his hesis proposed he research framework, as shown in Figure 4. This heoreical framework included six componens: 1) consumer innovaiveness (McKnigh e al., 2002), 2) consumer demographics (Joines e al., 2003), 3) perceived benefis (Forsyhe e al., 2006), 4) perceived risk (Vijayasarahy, 2003), 5) online shopping aiude (Vijayasarahy, 2003), and 6) online shopping inenion (Vijayasarahy, 2003). In his sudy, consumer s innovaiveness was measured using five consumer innovaiveness quesions. (McKnigh e al., 2002). Perceived benefis was focused on four benefis, namely, 1) shopping convenience 2) produc selecion 3) ease/comfor of shopping 4) hedonic/enjoymen. (Forsyhe e al., 2006). Perceived risk was focused on wo risk, namely, 1) privacy risk 2) securiy risk (Vijayasarahy,2003). Aiude oward online shopping and online purchase inenion developed by Vijayasarahy ( 2003). Consumer Innovaiveness Perceived Benefis Perceived Risk Online Shopping Aiude Figure 4: Theoreical Framework Online Shopping Inenion Sampling Plan The sample was limied regisered consumers of online sores in Mongolian. Sudy randomly seleced consumers of online sore in Mongolian. Insrumenaion According o heoreical framework, he sudy developed 29 iem quesionnaires. The quesionnaire comprised from iems relaed o consumer innovaiveness, perceived benefis, perceived risk, online shopping aiude, and online shopping inenion. All he measuremen iems were adoped from prior sudies (McKnigh e al., 2002: Vijayasarahy, 2003; Forsyhe e al., 2006). The five-par, self-repor survey was used o collec daa. Par 1 was measured consumer innovaiveness wih five iem quesions, and developed by McKnigh e al. (2002). Pa 2 was measured perceived benefis wih eigh iem quesions, and developed by Forsyhe e al. (2006). Par 2 wih four dimensions of perceived benefis were developed by Forsyhe e al. (2006), including 1) shopping convenience, 2) produc selecion, 3) ease/comfor shopping, and 4) hedonic/enjoymen. Par 3 wih wo dimensions of perceived risk were developed by Vijayasarahy (2003), including 1) privacy risk, 2) securiy risk. Par 4 measured online shopping aiude and developed by Vijayasarahy (2003). Par 5 measured online shopping inenion was developed by Vijayasarahy (2003), shown in he Table 1. In he Table 1 The Cranach s alpha was greaer han 0.7, he reliabiliy was accepable. Table 1: Reliabiliy of he Sudy Dimension Number of iems Cronbach s alph Consumer innovaiveness (McKnigh e al.2002) Perceived benefis (Forsyhe e al.2006) Convenience Selecion Ease/Comfor of Shopping Enjoymen/Hedonic Perceived risk (Vijayasarahy 2004) Privacy risk Securiy risk Aiude (Vijayasarahy 2003) Inenion (Vijayasarahy 2003) The Journal of Inernaional Managemen Sudies, Volume 7 Number 2, Ocober,

6 Daa Analysis The sudy used a web-based survey o collec daa. The daa were analyzed using he SPSS 16.0 saisical package. The sudy used regression analysis o es daa. RESULTS The sudy received 178 surveys responds. There were 71 invalid daa and removed, and ended up 107 (10.7 percen rae) valid daa, as shown in Table 2. Table 2: Saisics Frequencies of Samples Frequency Percenage N Invalid Sample % Valid Sample % Toal % H1: Consumer innovaiveness has a posiive impac on consumer online shopping aiude. Regression analysis was used o measure he influence of consumer innovaiveness on aiude oward online shopping. As shown in Table 3, he regression analyzed he relaionship beween consumer innovaiveness and online shopping aiude was significan (p<0.01). The adjused R² indicaed ha consumer innovaiveness as a whole explained 6.7% (.067) of he variance in aiude oward online shopping. To analyze he individual predicors -saisics (=2.942, p<0.01) and β coefficien (0.276) found o be significan. The resul found ha wake posiive relaionship exiss beween aiude oward online shopping and consumer innovaiveness. Therefore, H1 was suppored. Table 3: Regression Analysis for Consumer Innovaiveness Model R² Adjused R² Unsandardized coefficien Sand.coeff B Sd.error Bea Consumer innovaiveness ** Noes: *p<0.05; **p<0.01 H2: Effec of ype of perceived benefis (shopping convenience) on online shopping aiude. Regression analysis was used o measure he effec ype of perceived benefis (shopping convenience) on online shopping aiude. As shown in Table 4, he regression analyzing he aiude oward online shopping was significan (p<0.01). The adjused R² indicaed ha effec of ype of perceived benefis (shopping convenience) as a whole explained 35.8% (.358) of he variance in online shopping aiude. To analyze he individual predicors, -saisics (=7.653, p<0.01) and β coefficien (0.598) found o be significan. The resul found ha ype of perceived benefis (shopping convenience) posiive effec on online shopping aiude. Therefore, H2 was suppored. Table 4: Regression Analysis for Perceived Benefis (Shopping Convenience) Models R² Adjused R² Unsandardized coefficien Sand.coeff B Sd.error Bea Convenience ** Noes: *p<0.05; **p<0.01 H3: Effec of ype of perceived benefis (produc selecion) on online shopping aiude. Regression analysis was employed o examine he effec of ypes of perceived benefis (shopping selecion) on online shopping aiude. The regression analyzing was significan (p<0.01). The adjused R² indicaed ha effec of ype of perceived benefis (produc selecion) as a whole explained 61.7% (.617) of he variance in online shopping aiude. To analyze he individual predicors, -saisics (=13.10, p<0.01) and β coefficien (0.788) found o be significan. The resul found ha ype of perceived benefis (shopping selecion) posiive effec on online shopping aiude, as shown in Table 5. Consequenly, H3 was suppored. 172 The Journal of Inernaional Managemen Sudies, Volume 7, Number 2, Ocober, 2012

7 Table 5: Regression Analysis for Perceived Benefis (Produc Selecion) Models R² Adjused R² Unsandardized coefficien Sand.coeff B Sd.error Bea Selecion ** Noes: *p<0.05; **p<0.01 H4: Effec of ype of perceived benefis (ease/comfor) on online shopping aiude. Regression analysis was used o measure he effec of ype of perceived benefis (ease/comfor shopping) on online shopping aiude. In Table 6, he F value (109.9) for he regression analyzing was significan (p<0.01). The adjused R² indicaed ha effec of ype of perceived benefis (ease/comfor) as a whole explained 50.7% (.507) of he variance in aiude oward online shopping. To analyze he individual predicors, -saisics (=10.488, p<0.01) and β coefficien (0.715) found o be significan. The resul found ha ype of perceived benefis (ease/comfor shopping) posiive effec on aiude oward online purchasing. Therefore, H4 was suppored. Table 6: Regression Analysis for Perceived Benefis(Ease/Comfor) Models R² Adjused R² Unsandardized coefficien Sand.coeff B Sd.error Bea Ease/comfor ** Noes: *p<0.05; **p<0.01 H5: Effec of ype of perceived benefis (hedonic/enjoymen) on online shopping aiude. Regression analysis was employed o examine he effec of ype of perceived benefis (hedonic/enjoymen) on online shopping aiude. As shown in Table 7, he regression analyzing was significan (p<0.01). The adjused R² indicaed ha effec of ype of perceived benefis (hedonic/enjoymen) as a whole explained 59.7% (.597) of he variance in aiude oward online shopping. To analyze he individual predicors, -saisics (= , p<0.01) and β coefficien (0.755) found o be significan. The resul found ha ype of perceived benefis (hedonic/enjoymen) posiive effec on online shopping aiude. Therefore, H5 was suppored. Table 7: Regression Analysis for Perceived Benefis (Hedonic/Enjoymen) Models R² Adjused R² Unsandardized coefficien Sand.coeff B Sd.error Bea Enjoymne/Hedonic ** Noes: *p<0.05; **p<0.01 H6: The perceived risk (privacy risk) has a negaive impac on online shopping aiude. Regression analysis was employed o examine he privacy risk on online shopping aiude. As shown in Table 8, he regression analyzing was significan (p<0.01). The adjused R² indicaed ha privacy risk as a whole explained 17.5% (.175) of he variance in aiude oward online shopping. To analyze he individual predicors, -saisics (= -4.86, p<0.01) and β coefficien (-0.427) found o be significan. In summary, he resul found ha privacy risk negaive impac on online shopping aiude. Therefore, H6 was suppored. Table 8: Regression Analysis for Privacy Risk Models R² Adjused R² Unsandardized coefficien Sand.coeff B Sd.error Bea Privacy risk ** Noes: *p<0.05; **p<0.01 H7: The perceived risk (securiy risk) has a negaive impac on online shopping aiude. Regression analysis was used o measure he securiy risk on online shopping aiude. As shown in Table 9, he regression analyzing was significan (p<0.01). The adjused R² indicaed ha securiy risk as a whole explained 33% (.33) of he variance in aiude oward online shopping. To analyze he individual predicors, -saisics (= , The Journal of Inernaional Managemen Sudies, Volume 7 Number 2, Ocober,

8 p<0.01) and β coefficien (-0.580) found o be significan. The resul found ha securiy risk negaive impac on aiude oward online purchasing. Therefore, H7 was suppored. Table 9: Regression Analysis for Securiy Risk Models R² Adjused R² Unsandardized coefficien Sand.coeff B Sd.error Bea Securiy risk ** Noes: *p<0.05; **p<0.01 H8: Consumer aiudes have a posiive direc impac on online shopping inenions. Regression analysis was used o measure he influence of consumer aiude on online purchase inenion. As shown in Table 10, he regression analyzing he relaionship beween online shopping aiude and inenion was significan (p<0.01). The adjused R² indicaed ha consumer aiude as a whole explained 69.9% (.699) of he variance in online shopping inenion. To analyze he individual predicors -saisics (=15.604, p<0.01) and β coefficien (0.836) found o be significan. The resul found ha consumer aiude posiive direc influence on online purchase inenion. Consequenly, H8 was suppored. Table 10: Regression Analysis for Online Shopping Aiude and Online Shopping Inenion Model R² Adjused R² Unsandardized coefficien Sand.coeff B Sd.error Bea Aiude ** Noes: *p<0.05; **p<0.01 DISCUSSION The general purpose of his sudy was o examine Mongolian consumers online shopping behavior and inenion, and deermined influencing facors. The sudy was examined ha consumer percepions of online shopping and he facors influenced on online shopping aiude in Mongolian. The resuls of his sudy suppored nearly all hypoheses. The sudy findings ha consumer innovaiveness, perceived benefis, and perceived risk are imporan deermining facors influencing on consumer online shopping aiude and online shopping inenion. This sudy found ha consumer innovaiveness had a posiive influenced on online shopping aiude. In urn, online consumers were innovaors in Mongolian. The sudy found ha perceived benefis posiively influenced on online shopping aiude. Online shopping consumers of Mongolian agreed ha online shopping was more convenien. Perceived benefis were he major moivaion facor for online consumers in Mongolia. The findings sugges ha online sores need o provide more convenience, wider selecion of producs and a more easily navigable websie in order o arac online consumers and moivae hem o make purchases. The sudy found perceived risk negaively influenced on aiudes oward online shopping. Privacy risk was associaed wih personal informaion and personal informaion includes heir saemen daa. The sudy resuls confirmed earlier sudies revealed ha perceived risk was a major barrier o shopping online. Mongolian consumers misrus online sores abiliy for proecing heir personal informaion. This sudy found ha consumer innovaiveness had a posiive influenced on online shopping aiude, and Mongolian online shoppers are an innovaor. The perceived benefis posiively influenced on aiude oward online shopping, and he main reasons ha Mongolian consumers shop online was perceived benefis. In urn, Mongolian consumers acceped ha online shopping was more convenience, produc selecion, ease/comfor, and hedonic/enjoymen compared o oher channel shopping. This sudy found ha perceived risk negaive influenced on aiude oward online shopping. Perceived risk was ha he mos majoriy barrier for consumers didn shop online in Mongolia. This sudy found ha online shopping aiude direcly posiive influenced on online shopping inenion. 174 The Journal of Inernaional Managemen Sudies, Volume 7, Number 2, Ocober, 2012

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