臺灣博碩士論文加值系統

Social Media it is inevitable. Nowadays everything it is related towards those platforms. The aim of this study it is to examine on how Social Media has disrupted the traditional five steps of purchasing decision process (Problem/Need recognition, Information search, Evaluation of alternatives, Purchase decision and Post-purchase behavior) and has created a new model based on peer to peer comparison and influence through the environment and channels of Social Media. This paper will prove how online platforms make the consumer buy products easier, faster and skipping the first three steps of the traditional purchase intention.

Thanks to the proliferation of online platforms, internet and communication consumers are more demanding and well informed (having 24/7 access to all kind of information) and its most valuable ressource it is time. Hence, consumer behavior is not longer under push strategies but companies use a pull strategy in order to save ressources and time for both businesses and consumers making the shopper the one that decides what will buy. Those choices will be made upon what their friends, relatives, peers and social media influencers the user follows on the online world; creating a very impulsive and easily manipulated shopper. Today, online socialization it is a phenomenon that companies will take advantage of in order to know better the consumer and sell more.

For this research the best way to measure the impact of social media platforms in the consumer behavior towards online buying, it will be conducted an experimental design. We use hierarchical linear modeling (HLM) and two-way MANOVA to test all proposed hypotheses. The empirical results show that (1)Consumer inertia has a direct effect on Purchase intention. Higher consumer inertia has higher purchase intention of the brand. (2)Product involvement has no effect between consumer inertia and purchase intention. (3)Source of online advertising has no effect between consumer.

TABLE OF CONTENTS III
CHAPTER ONE INTRODUCTION 1
1.1 MOTIVATION AND PURPOSES 1
1.2 IMPORTANCE OF THE RESEARCH 2
1.3 CONCEPTUAL FRAMEWORK 3
CHAPTER TWO LITTERATURE REVIEW 5
2.1 PURCHASING DECISION PROCESS 5
2.1.1 The Definition of Purchasing Decision Process 5
2.1.2 Theory and Related Studies 6
2.2 SOCIAL MEDIA MARKETING 7
2.2.1 Social Media Marketing Definition 7
2.2.2 Theory and Related Studies 7
2.3 ONLINE CONSUMER BEHAVIOR AND PURCHASING PROCESS 9
2.3.1 Theory and Related Studies 9
2.4 ONLINE INFLUENCE 11
2.4.1 Theory and Related Studies 11
2.5 PRODUCT INVOLVEMENT 12
2.5.1 Definition of product involvement 12
2.5.2 Related Studies of product involvement 12
CHAPTER THREE RESEARCH METHOD 14
3.1 CONCEPTUAL FRAMEWORK AND RESEARCH HYPOTHESES 14
3.2 RESEARCH DESIGN 15
3.3 HIGH INVOLVEMENT PRODUCT SCENARIOS 16
3.4 LOW INVOLVEMENT PRODUCT SCENARIOS 18
3.5 MEASUREMENT OF VARIABLES 20
3.5.1 Measuring Purchase Intention 21
3.5.2 Measuring Inertia 21
3.6 QUESTIONNAIRE 21
3.7 DATA COLLECTION 22
CHAPTER FOUR DATA ANALYSIS 23
4.1 DEMOGRAPHIC ANALYSIS 23
4.1.1 High Involvement / Company 23
4.1.2 High Involvement / Person 25
4.1.3 Low Involvement / Company 26
4.1.4 Low Involvement / Person 28
4.2 RELIABILITY ANALYSIS AND FACTOR ANALYSIS 30
4.3 MANOVA TEST 31
CHAPTER 5 CONCLUSIONS 32
5.1 FINDINGS 32
5.2 MANAGERIAL IMPLICATIONS 33
5.3 LIMITATIONS 34
REFERENCE 35
APPENDIX QUESTIONNAIRE 38

Aldridge, A., Forcht, K., & Pierson, J. (1997). Get linked or get lost: marketing strategy for the Internet. Internet research, 7(3), 161-169.
Annamalai Manickam Sakkthivel, M. B. A. (2013). Do online companies control the behavior of online consumers?: Exploratory study on the impacting variables from Middle East. Journal of Internet Banking and Commerce, 18(3).
Aral, S. (2010). Identifying social influence: a comment on opinion leadership andsocial contagion in new product diffusion. Marketing Science, 30(2), 217-223.
Bonchi, F., Castillo, C., Gionis, A., & Jaimes, A. (2011). Social network analysis and mining for business applications. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 22.
Broach, A., & Tellis, G. J. (2016). Halo (Spillover) effects in social media: Do product recalls of one brand hurt or help rival brands? Journal of Marketing Research, 53(2), 143-160.
Constantinides, E. (2004). Influencing the online consumer's behavior: the web experience. Internet research, 14(2), 111-126.
Dube JP, Hitsch GJ, Rossi P E (2010) State dependence and alternative explanations for consumer inertia. The RAND Journal of Economics, 41, 417-445
Engel, J., Kegerreis, R., & Blackwell, R. (1969). Word-of-mouth communication by the innovator. Journal of Marketing, 33(3), 15-19.
Eastlick, M. A., & Lotz, S. (1999). Profiling potential adopters and non-adopters of an interactive electronic shopping medium. International Journal of Retail & Distribution Management, 27(6), 209-223.
Katz, J., & Aspden, P. (1997). Motivations for and barriers to Internet usage: Results of a national public opinion survey. Internet Research, 7(3), 170-188.
Ken Sundheim, KAS Placement Staffing (2011). Where they started, the beginning of Facebook and Twitter: A brief history of social media
Kollat, D., Engel, J., & Blackwell, R. (1970). Current problems in consumer behavior Research. Journal of Marketing Research, 7(3), 327-332.
Kotler, P., & Zaltman, G. (1971). Social marketing: an approach to planned social change. The Journal of Marketing, 3-12.
Kotler, P. (2009). Marketing management: A South Asian perspective. Pearson Education India.
Lithium (2014) “9 Big Reasons for Serious WOMM” https://aci.info/wpcontent/uploads/2014/02/lithium-wom-marketing-infographic.png
Masood, O., & Javaria, K. (2016). Inertia and online trust leading factors affecting consumer repeat-purchase intention in e-commerce: moderating role of alternative attraction. Journal of Internet Banking and Commerce, 21(S5).
Mohammadian, M., Alipoor, H., & Daryani, S. M. (2013). Surveying the effects of cooperation companies perception of brand personality in buying decision of ardabil citizens.
Narayanam, R., & Nanavati, A. A. (2012). Viral marketing for product cross-sell through social networks. In joint European conference on machine learning and knowledge discovery in databases (pp. 581-596). Springer Berlin Heidelberg.
Nielsen, (2012), “State of the Media: The social media report 2012”
Olshavsky, R. W., & Granbois, D. H. (1979). Consumer decision making—fact or fiction?. Journal of Consumer Research, 6(2), 93-100.
Park, C. H., & Kim, Y. G. (2003). Identifying key factors affecting consumer purchase behavior in an online shopping context. International Journal of Retail & Distribution Management, 31(1), 16-29.
Rook, D. W. (1987). The buying impulse. Journal of consumer research, 14(2), 189-199.
Shabbir, K., I. and K. (2009). COO and brand name’s affect on consumer behavior and purchase intention in Pakistan. Interdisciplinary Journal of Contemporary Research in Business, 1(3),84-95.
Shaema, A. K. (2016). Factors influencing youngsters behavior towards online shopping in Vellore district of Tmilnadu, India. The Journal of Internet Banking and Commerce, 2016.
Zhu, Z., Wang, J., Wang, X., & Wan, X. (2016). Exploring factors of user’s peer-influence behavior in social media on purchase intention: Evidence from QQ. Computers in Human Behavior, 63, 980-987.