Understanding Consumer Online Shopping Behaviour From The Perspective .

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UNDERSTANDING CONSUMER ONLINE SHOPPING BEHAVIOUR FROMTHE PERSPECTIVE OF TRANSACTION COSTSBy Lingling Gao, B.A. Computer Science (SCNU), M.I.B (UTAS)Submitted in fulfilment of the requirements for the degree ofDoctor of PhilosophyUniversity of Tasmania(April, 2015)i

DECLARATIONI declare that this thesis contains no material which has been accepted for a degree ordiploma by the University or any other institution, except by way of backgroundinformation and duly acknowledged in the thesis, and to the best of my knowledgeand belief no material previously published or written by another person except wheredue acknowledgement is made in the text of the thesis, nor does the thesis contain anymaterial that infringes copyright.Date 02 / 04 / 2015ii

AUTHORITY OF ACCESSThis thesis may be made available for loan. Copying and communication of any partof this thesis is prohibited for two years from the date this statement was signed; afterthat time limited copying and communication is permitted in accordance with theCopyright Act 1968.Date 0 2 / 04 / 2015iii

STATEMENT OF ETHICAL CONDUCTThe research associated with this thesis abides by the international and Australiancodes on human and animal experimentation, the guidelines by the AustralianGovernment's Office of the Gene Technology Regulator and the rulings of the Safety,Ethics and Institutional Biosafety Committees of the University.Date 02 / 04 / 2015iv

ABSTRACTMost prior empirical online shopping research studied consumer purchase behaviourand post-purchase behaviour from the perceived benefit/value perspective. However,few efforts have attempted to employ the cost concept to analyse consumers’ onlinebehaviour. Researchers in psychology, marketing, and organizational behaviour haveapplied the transaction cost (TC) construct to study how the TCs influence themanagers’ decision-making process at the organizational level. At the individualconsumer level, TC consideration has become increasingly important in affecting theway consumers choose shopping channel and vendors in their daily lives.Nevertheless, little research attention has been devoted to understanding how theindividual consumers’ online purchase and post-purchase behaviours are affected bytheir perceived TCs. This study therefore represents a point of departure in that itbrings in TCs to explain online behaviour at the individual online shopper level. Byextending TCs from traditional shopping to online shopping, this study develops anintegrative model of consumer TCs associated with shopping at an online store, basedon which hypotheses regarding the salient antecedents and consequences of consumerTCs were developed.The research was undertaken in China, in which the economy, particularly the onlineshopping industry, has been increasing rapidly. China has also a unique cultural andinstitutional setting when compared to other countries although existing researchbased on China is limited. This research is therefore expected to shed light onconsumer TCs of online shopping within the Chinese context. Data for the study wascollected using an on-street survey conducted on a face-to-face basis in oneeconomically developed city and one economically less-developed city randomlyv

selected from the pool of coastal cities and inland cities of China, respectively.Hypotheses were tested using structural equation modelling (AMOS 20.0) andmultiple group analysis.Results of the study indicate that consumer TCs consisting of pre-, contemporaneous-,and post-TCs are derived from three major aspects, namely consumer-relatedcharacteristics, online store- and product-related characteristics, and online channelrelated characteristics. The consumer-related characteristics, including Internet accessavailability, perceived Internet expertise, and online buying frequency, are found tonegatively affect consumer TCs. The online store- and product- related characteristics,consisting of e-service quality and reputation of online store, can significantly lowerconsumer TCs. In the last category of online channel-related characteristics, theresults confirm that privacy and security concerns increase consumer TCs whereasperceived convenience largely reduces consumer TCs. As for the consequences ofconsumer TCs, online purchase behaviour and customer loyalty are found to bedirectly affected by TCs.Further, the results reveal that though TCs have direct and negative effects oncustomer loyalty, part of their effects is conditional on their ability to reduce customersatisfaction. That is, though lower TCs in online purchasing activities could help gaincustomer loyalty, such relationship is subject to the mediating effects of customersatisfaction in online shopping. Additionally, results of the study imply that asconsumers’ inherent attributes, consumer’s risk-bearing propensity confounds theeffects of TCs on customer loyalty, and perceived enjoyment of online shoppingmoderates the effect of TCs on online purchase behaviour. Finally, the results suggestvi

that the different product categories affect TCs itself as well as the effects of theantecedents on TCs. Product categories further influence the relationships betweenTCs and subsequent online behaviour.This study advances the consumer behaviour literature by taking a new perspective ofTC mechanisms in online consumers’ decision-making. It offers deeper theoreticaland empirical insights into online purchase and post-purchase behaviour byexplicating the role of TCs at the individual consumer level and exploring acomprehensive set of antecedents of TCs. This study also has important practicalimplications. From the consumer’s perspective, this research brings benefits toindividual consumers by informing them about the advantages of online shoppingwhich can reduce their time and cognitive effort expended on shopping andconsequently lower their TCs of online shopping. In addition, the research findingsprovide online vendors with a deeper understanding on the allocation of resources andcapabilities in achieving minimum consumer TCs and inducing favourablebehavioural outcomes.vii

ACKNOWLEDGEMENTI would like to express my heartfelt gratitude to my supervisors, Dr Fan Liang, DrRob Hecker and Dr Tommy Wang, for their guidance, encouragement, enthusiasm,and support throughout all the stages of completing this dissertation. Theirencouragement and invaluable wisdom made it for me possible to the zenith of mydoctoral accomplishment.I would like to thank the faculty members, colleagues, and friends from theTasmanian School of Business and Economics at UTAS for their help, assistance, andsupport throughout the data collection and the data analysis processes. I extend myprofound thanks to Dr Debra Grace who generously gave of her time to answer myendless questions about complex structural equation modelling techniques. I also wantto give great thanks to three of my colleagues who helped with translation and backtranslation.Special thanks go to many Chinese professors who provided significant help for thedata collection process in China. Especially, I wish to express my sincerest thanks toDr. Hongwei Wang and Dr. Jing Fu who provided full support to the survey carriedout in China.Last, but not least, I want to express my love and appreciation to my beloved husband,Xuesong Bai and my lovely daughter, Emma. Without their support, encouragementand sacrifice, my graduate work and this thesis would not have been possible. Specialthanks also go to my parents who raised and taught me and always give meencouragement. Words cannot describe my immense gratitude for their endlessviii

support, patience and understanding during the pursuit of my educational goals. Thisthesis is especially dedicated to them.ix

PEER-REVIEWED PUBLICATIONS OF THE AUTHORJournal publications:Gao. L, Bai, X. & Parker, A. 2015. Understanding sustained participation in virtualtravel communities from the perspectives of IS success model and flow theory,Journal of Hospitality and Tourism Research (Forthcoming).Gao. L & Bai, X. 2014. Online consumer behaviour and its relationship to websiteatmospheric induced flow: Insights into online travel agencies in China. Journal ofRetailing and Consumer Services, 21 (4): 653-665.Gao, L. & Bai, X.2014. An empirical study on continuance intention of mobile socialnetworking services: Integrating the IS success model, network externalities and flowtheory. Asia Pacific Journal of Marketing and Logistics, 26 (2): 168-189.Gao, L. & Bai, X. 2014. A unified perspective on the factors influencing acceptanceof internet of things technology. Asia Pacific Journal of Marketing and Logistics, 26(2): 211-231.Conference publications and presentations:Gao, L. & Bai, X. 2013. Understanding website atmospherics-induced flow of onlineshopping for tourism products and services. Proceedings of the 2013 Australian andNew Zealand Academy of Management Conference, Hobart, Australia.x

Gao, L. & Bai, X. 2013.Understanding emotional and rational routes influencingrepurchase intention in online settings. Proceedings of the 2013 Australian and NewZealand Academy of Marketing Conference, Auckland, New ZealandGao, L. & Bai, X. 2012. Exploring factors affecting consumers' intention to use IoTtechnologies in China. Proceedings of the 2012 Australian and New ZealandAcademy of Management Conference, Perth, Australia.Liang, F. & Gao, L. 2011. An institution-and culture-induced dualistic approachtowards Guanxi management: a case of Chinese migrant entrepreneurs in Australia.Proceedings of the Academy of International Business Southeast Asia RegionalConference, Taipei, Taiwan.xi

TABLE OF CONTENTSDECLARATION . iiAUTHORITY OF ACCESS . iiiSTATEMENT OF ETHICAL CONDUCT . ivABSTRACT . viiiACKNOWLEDGEMENT . viiiPEER-REVIEWED PUBLICATIONS OF THE AUTHOR . xTABLE OF CONTENTS . xiiiiLIST OF TABLES . xviLIST OF FIGURES . xixxCHAPTER1: INTRODUCTION. 11.1 BACKGROUND . 11.2 RESEARCH GAPS . 81.3 PURPOSE OF THE STUDY . Error! Bookmark not defined.1.4 SIGNIFICANCE OF THE STUDY . Error! Bookmark not defined.1.5 STRUCTURE OF THE THESIS . 14CHAPTER 2: LITERATURE REVIEW . 162.1 CHAPTER OVERVIEW . 162.2 THEORIES EXPLAINING ONLINE CONSUMER BEHAVIOUR. 162.2.1 Introduction . 162.2.2 The Technology Acceptance Model (TAM) . 192.2.3 The Theory of Planned Behaviour (TPB). Error! Bookmark not defined.2.2.4 Comparison between TAM and TPB . 272.2.5 Conclusion . 292.3 EMPIRICAL EVIDENCE OF ONLINE SHOPPING BEHAVIOURError! Bookmark not def2.3.1 Introduction. Error! Bookmark not defined.2.3.2 Consumer Characteristics . Error! Bookmark not defined.xii

2.3.3 Online Vendor/Store and Product Characteristics . 442.3.4 Perceived Channel Characteristics . Error! Bookmark not defined.2.3.5 Conclusion . Error! Bookmark not defined.2.4 APPLICATION OF TCS IN CONSUMER ONLINE BEHAVIOUR . 632.4.1 Introduction . 632.4.2 Major Research on TCs of Online Shopping . 652.4.3 Limitations of the Research on TCs . Error! Bookmark not defined.2.5 CHPATER SUMMARY AND RESEARCH QUESTIONS . 84CHAPTER 3: THEORETICAL BACKGROUND AND RESEARCHFRAMEWORK. 883.1 CHAPTER OVERVIEW . Error! Bookmark not defined.3.2 TCT . 893.2.1 The Assumptions of TCT . Error! Bookmark not defined.3.2.2 The Dimensions of the Transaction. Error! Bookmark not defined.3.2.3 The Basic Propositions of TCT. Error! Bookmark not defined.3.3 APPLYING TCT IN ONLINE SHOPPING . Error! Bookmark not defined.3.3.1 The Explanation of TCT on Online Consumer Behaviour. 943.3.2 TCT as an Integrative Theory . 993.3.3 The Conceptualization of Consumer TCs of Online ShoppingError! Bookmark not d3.4 IDENTIFYING ANTECEDENTS OF CONSUMER TCS OF ONLINESHOPPING . Error! Bookmark not defined.3.4.1 Dimensions of the Online Transaction. Error! Bookmark not defined.3.4.2 Antecedents of Consumer TCs . Error! Bookmark not defined.3.5 CONCEPTUAL MODEL OF CONSUMER TCS OF ONLINESHOPPING . Error! Bookmark not defined.3.6 HYPOTHESES DEVELOPMENT . Error! Bookmark not defined.3.6.1 Antecedents of Consumer TCs . Error! Bookmark not defined.3.6.2 Consequences of Consumer TCs . Error! Bookmark not defined.3.6.3 Customer Satisfaction as a Mediating VariableError! Bookmark not defined.73.6.4 Risk-Bearing Propensity and Perceived Enjoyment asModerating Variables . Error! Bookmark not defined.xiii

3.6.5 Differences Cross Product Categories . Error! Bookmark not defined.3.7 CHAPTER SUMMARY . Error! Bookmark not defined.CHATER 4: METHODOGLY . Error! Bookmark not defined.4.1 CHAPTER OVERVIEW . Error! Bookmark not defined.4.2 RESEARCH METHODS . Error! Bookmark not defined.4.3 SAMPLE . Error! Bookmark not defined.4.4 DATA COLLECTION . Error! Bookmark not defined.4.5 QUESTIONNAIRE DEVELOPMENT . Error! Bookmark not defined.4.6 MEASURES . Error! Bookmark not defined.4.7 INSTRUMENT VALIDITY AND RELIABILITY . Error! Bookmark not defined.4.8 COMMON METHOD VARIANCE . Error! Bookmark not defined.4.9 DATA ANALYSIS TECHNIQUES: STRUCTURAL EQUATIONMODELLING . Error! Bookmark not defined.4.10 CHAPTER SUMMARY . Error! Bookmark not defined.CHAPTER 5: DATA ANALYSIS . Error! Bookmark not defined.5.1 CHAPTER OVERVIEW . Error! Bookmark not defined.5.2 DATA CLEANING AND SCREENING . Error! Bookmark not defined.5.3 PROFILE OF THE SAMPLE . Error! Bookmark not defined.5.4 PRELIMINARY METHOD OF DATA ANALYSISError! Bookmark not defined.5.5 PRELIMINARY RESULTS. Error! Bookmark not defined.5.6 OVERALL MEASUREMENT MODEL . Error! Bookmark not defined.45.7 STRUCTURAL MODEL . Error! Bookmark not defined.5.8 TESTING FOR MEDIATION . Error! Bookmark not defined.5.9 MULTIPLE GROUP ANALYSES: TEST OF MODERATORSError! Bookmark not defined5.10 TESTING FOR MODEL DIFFERENCES ACROSS PRDOCUTCATEGORIES . Error! Bookmark not defined.5.11 CHAPTER SUMMARY . Error! Bookmark not defined.xiv

CHAPTER 6: DISCUSSION AND CONCLUSION . Error! Bookmark not defined.6.1 CHAPTER OVERVIEW . Error! Bookmark not defined.6.2 DISCUSSION OF RESULTS . 2906.2.1 Antecedents of Consumer TCs . 2906.2.2 Consequences of Consumer TCs . Error! Bookmark not defined.6.2.3 The Mediating Role of Customer SatisfactionError! Bookmark not defined.6.2.4 The Moderating Roles of Risk-Bearing Propensity and PerceivedEnjoyment . Error! Bookmark not defined.6.2.5 Product Category Comparison . Error! Bookmark not defined.6.3 IMPLICATIONS. Error! Bookmark not defined.6.3.1 Theoretical Implications . Error! Bookmark not defined.6.3.2 Practical Implications . Error! Bookmark not defined.6.4 LIMITATIONS . Error! Bookmark not defined.6.5 RECOMMENDATIONS FOR FUTURE STUDY . Error! Bookmark not defined.6.6 OVERALL CONCLUSIONS . Error! Bookmark not defined.REFERENCES . Error! Bookmark not defined.APPENDICES . 395APPENDIX A Summary of the Findings of the Consumer Characteristics onOnline Shopping Adoption . 416APPENDIX B Summary of the Findings of the Online Vendor and ProductCharacteristics on Online Shopping AdoptionError! Bookmark not defined.APPENDIX C Summary of the Findings of the Perceived Online ChannelCharacteristics on Online Shopping AdoptionError! Bookmark not defined.APPENDIX D The Mean, Stand Deviations, Skewness and Kurtosis of AllVariables . Error! Bookmark not defined.APPENDIX E Survey Instrument: English Version . Error! Bookmark not defined.APPENDIX F Survey Instrument: Chinese Version . Error! Bookmark not defined.xv

LIST OF TABLESTable 2.1 Summary of the Findings of the Effect of Online TCs onBehaviour-Related Consequences77Table 2.2 Summary of the Findings of the Antecedents of Online TCs79Table 3.1 Summary of Hypotheses162Table 4.1 Pre-TCs Measurement Items178Table 4.2 Contemporaneous TCs Measurement Items178Table 4.3 Post-TCs Measurement Items179Table 4.4 Internet Access Availability Measurement Items179Table 4.5 Perceived Internet Expertise Measurement Items180Table 4.6 Online Buying Frequency Measurement Items181Table 4.7 Product Quality Concern Measurement Items181Table 4.8 Site Design Measurement Items182Table 4.9 E-Service Quality Measurement Items184Table 4.10 Reputation of online store Measurement Items185Table 4.11 Perceived Convenience Measurement Items185Table 4.12 Privacy and Security Concerns Measurement Items187Table 4.13 Environmental Uncertainty Measurement Items188Table 4.14 Online Purchase Behaviour Measurement Items189Table 4.15 Customer satisfaction Measurement Items189Table 4.16 Consumer Loyalty Measurement Items190Table 4.17 Consumer’s Risk-Bearing Propensity Measurement Items191Table 4.18 Perceived Enjoyment Measurement Items191Table 5.1 Demographic Profile of the Respondents (N 962)208xvi

Table 5.2 Preliminary Data Analysis- Internet Access Availability217Table 5.3 Preliminary Data Analysis- Perceived Internet Expertise218Table 5.4 Preliminary Data Analysis- Product Quality Concern220Table 5.5 Preliminary Data Analysis- Site Design222Table 5.6 Preliminary Data Analysis- E- Service Quality225Table 5.7 Preliminary Data Analysis- Reputation of Online Store227Table 5.8 Preliminary Data Analysis- Perceived Convenience229Table 5.9 Preliminary Data Analysis- Privacy and Security Concerns231Table 5.10 Preliminary Data Analysis- Environmental Uncertainty233Table 5.11 Preliminary Data Analysis- Perceived Consumer TCs236Table 5.12 Preliminary Data Analysis- Customer Satisfaction239Table 5.13 Preliminary Data Analysis- Customer Loyalty240Table 5.14 Preliminary Data Analysis- Consumer’s Risk-BearingPropensity242Table 5.15 Preliminary Data Analysis- Perceived Enjoyment244Table 5.16 Results of the Measurement Model of Latent Variables245Table 5.17 Results of Discriminant Analysis249Table 5.18 Results of the New Measurement Model of Latent Variables252Table 5.19 Discriminant Validity (N 962)254Table 5.20 Results of Harman’s Single Factor Test256Table 5.21 Results of Structural Model When Controlling for Age, Gender,Income and Education Level261Table 5.22 Direct, Indirect and Total Effects266Table 5.23 Results of Hypotheses Testing267Table 5.24 Mediation Analysis Results269xvii

Table 5.25 Multigroup SEM Results of Testing the Effect of Risk-bearingPropensity on Perceived TCs Online Purchase Behaviour270Table 5.26 Multigroup SEM Results of Testing the Effect of Risk-bearingPropensity on Perceived TCs Consumer Loyalty271Table 5.27 Multigroup SEM Results of Testing the Effect of PerceivedEnjoyment on Perceived TCs Online Purchase Behaviour273Table 5.28 Multigroup SEM Results of Testing the Effect of PerceivedEnjoyment on Perceived TCs Customer Loyalty274Table 5.29 Results of the Structural Model – Search Products277Table 5.30 Results of the Structural Model – Experience Products281Table 5.31 Comparison of Search Products and Experience Products Model283Table 5.32 Comparison of Path Coefficients via T-tests286xviii

LIST OF FIGURESFigure 3.1 An Integrative Model of Consumer TCs of online Shopping114Figure 3.2 The Antecedents of Consumer TCs of online Shopping116Figure 3.3 The Consequences of Consumer TCs of online Shopping117Figure 3.4 The Partially Mediating Role of Customer Satisfaction118Figure 3.5 The Moderating Role of Consumer Risk-Bearing Propensity119Figure 3.6 The Moderating Role of Perceived Enjoyment of online Shopping119Figure 5.1 Two-Step Preliminary Analyses211Figure 5.2 The Structural Model (Used for Testing of H1a-5)258Figure 5.3 Structural Model with Control Variables Showing Results ofAnalysis262Figure 5.4 Model Results – Search Products278Figure 5.5 Model Results – Experience Products282xix

CHAPTER1: INTRODUCTION1.1 BACKGROUNDE-commerce is business transactions undertaken through the Internet for goods or services,including online shopping, online banking, online payment, online stocks, and online travelreservation (Reddy and Iyer 2002). It has grown rapidly since the mid-1990s (Reddy and Iyer2002) and is an important means of conducting business (Tian and Stewart 2007). Accordingto an estimation by Online Retail Forecast in 2012, online shoppers in the United States willspend 327 billion in 2016, an growth of 45 per cent over 2012 and 62 per cent growth over2011 (Mulpuru 2011). There are many reasons given for the growth of e-commerce overrecent times, including for example, its size as a source of information, increasinglybecoming much more user friendly and more accessible and less expensive (Bonn et al.1999). Today, e-commerce has penetrated into the national economy, social services, all areasof people's lives, from the basic necessities of daily life to all industry.Online shopping has increased rapidly over the past several decades not only in the world butalso in China in particular (Clemes et al. 2013). In the past few years, the Internet hasemerged as a vibrant marketplace for consumers and sellers of various goods and services inChina (Gao and Bai 2014). According to a report published by China Electronic CommerceResearch Centre (CECRC) (2014), the total online retail market (B2C and C2C) in Chinareached 225 billion by June of 2014 and is expecting to reach 368.2 billion at the end of2014, almost doubling over 2013. Business-to-Customer (B2C) online retail sales were 12.67 billion in 2010, 29.46 billion in 2011, 63.65 billion in 2012, and 122.57 billion in1

2013 (iResearch 2013). The data indicate that China’s online shopping is growing and willcontinue to grow. Therefore, the research conducted in this area would have the potential tocontribute to a better understanding of this rapidly increasing market and to online shoppingin general.Despite increased Internet usage and rapid growth of online shopping (China InternetNetwork Information Centre 2012), transactions via online channel still constitute a verysmall percentage of total China retail sales (Jin 2012). The CECRC (2014) reported thatonline retail sales represent only 6.8 per cent of total retail sales of commodities in 2013,which implies that even though online shopping continues to break new records every year,the popularity of offline shopping far overshadows that of online shopping (Kim et al. 2012a).The reasons underlying the relatively small percentage of online retail sales when comparedwith the offline retail sales created by the traditional shopping are not clearly understood(Kim et al. 2012a), thereby still requiring investigation by practitioners in the marketing field.Furthermore, within all the e-commerce network applications, the increase rate of onlineshopping for physical goods usage ranks at the bottom in China (CECRC 2012). For example,in 2013, the annual growth rate of online payment ranks first, followed by travel reservation,and then followed by online stocks and online banking. The last one is online shopping. Thelow annual increase rate of online shopping may be caused by an insufficient grasp by onlinevendors of the complexity and dynamics of e-business, obstacles to web access and onlinebanking, inadequate supply and delivery systems, and privacy and security concerns of onlineconsumers, which have been already explored (Soopramanien and Robertson 2007, Hong andThong 2013). Nevertheless, there might be other potential reasons from the different2

perspectives that can offer a better explanation, which have not yet been fully investigated inChina (Wu et al. 2014).From the standpoint of online vendors, selling goods and services via the Internet that iscapable of accommodating various kinds of products and services is argued to have enormouspotential. However, the fact reported by online vendors is that people browse the Internetmore for information than for buying online (Hoffman et al. 1999, Wu et al. 2014, Meskaranet al. 2013). Although online shopping is becoming an accepted way to purchase many kindsof products and services (Soopramanien and Robertson 2007), most online consumers arestill “window shoppers” in that they use information gathered online to make purchases offline (Forsythe and Shi 2003, Riquelme and Román 2014). Several authors (Ahuja et al. 2003,Tsai et al. 2011, Taddei and Contena 2013) have attributed consumers’ reluctance topurchase online to apparent barriers, i.e. credit card issues, privacy issues, service frustrations.Furnell et al. (2008) and Hong and Thong (2013) also highlighted that the reason more peoplehave yet to shop online is due to a fundamental lack of faith in online privacy and securityprotection. It is therefore imperative for online vendors to change the current situation andimprove consumers’ incentive for purchasing goods online.In mature and highly competitive markets, the profitability of firms largely depends oncustomer loyalty (Chiu et al. 2009b). However, online vendors reveal that the Internet seemsto make customer loyalty irrelevant and acknowledge that the Internet has had a detrimentalimpact on building and maintaining customer relationships (Chen 2007). Gupta and Kim(2007) point out that only 1 per cent of the online shoppers eventually return and makepurchases from the online store where they have purchased before. Two reasons are oftencited to explain why it is hard to build long term loyalty in online shopping. Firstly, online3

shoppers could easily cover the globe in search of the best price. The Internet dramaticallyreduces consumers’ search costs (Vatanasombut et al. 2004). Consumer choice is no longerbound by the constraints of place or access to information (Urb

customer loyalty, such relationship is subject to the mediating effects of customer satisfaction in online shopping. Additionally, results of the study imply that as consumers' inherent attributes, consumer's risk-bearing propensity confounds the effects of TCs on customer loyalty, and perceived enjoyment of online shopping

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