1. INTRODUCTION:

In the past with the help of traditional marketing communication tools marketers used to push messages towards buyers or prospects by using one-way communication but as customers are exposed with too many communications and the messages from the communication interrupt customers inappropriately hence this type of traditional tools do not work anymore (Brown and Hayes, 2008).

Introduction of social media has changed the buyer behavior and hence the concept of marketing communication. Social media have significantly changed the relationship between buyers and marketers by allowing a two-way communication. A new marketing tool called ‘social media’ has been introduced in the marketing discipline. A shift has been occurred in the marketing, where in the past marketers had the power of influence to customers but now consumers/people have a greater influence on their peers (Jaffe, 2010) as they can share contents, ideas; write recommendations, reviews and opinions about firms and brands and influence consumer behavior thereby (Brown and Hayes, 2008).

Literature review suggests that buyers have access to different types of social media tools from everywhere. Word-of-mouth is effective to spread positive or negative experience about the firms or brands which influences customers and with the help of social media users can spread the experiences more quickly to unlimited audience/users (Brown and Hayes, 2008) and hence social media has significant influence on information sharing among consumer (Sago, 2010) which in turn influence buyer behavior.

2. RATIONALE:

Because of strong internet network coverage firms and their customers around the globe are more connected than the past and because of introduction of social media consumers/users can create personal connection with other individuals. This table shows the number of social media users worldwide from 2010 to 2016 with projections until 2021.The use of social media is increasing and 71 % of internet users are using social media (www.statista.com).The number of online US Adults who use social media sites has risen from 8% (2005) to 72% (2013) and number of Twitter users has gone up 125% in just 3 years (www.forbes.com).In terms of reach and scope, with a monthly active users of 1.86 billion Facebook is the market leader ((www.statista.com). Research findings showed that out of internet users of the USA, 79% (68% adults) use Facebook; 32% (28% adults) use Instagram’; 24% (21% adults) use Twitter; 29% (25% adults) use LinkedIn; and 31% (26% adults) use Pinterest (www.pewinternet.org).

Table-1: Number of Social Media Users in Billion

Year	Users	Year	Users
2010	0.97	2016	2.28
2011	1.22	2017	2.46
2012	1.4	2018	2.62
2013	1.59	2019	2.77
2014	1.91	2020	2.9
2015	2.14	2021	3.02

Source: www.statista.com

Throughout the world the penetration of social media network is increasing. Technological development plays an important role in this increase usage of social media by consumers. Now with the help of smartphones and other mobile devices consumers can interact and connect with each other from everywhere and the increased worldwide usage of these mobile devices have opened up the possibilities of mobile social networks. Social media help users to interact beyond their local or social boundaries and offers possibilities to share user generated content like photos and videos and features such as social games. Social advertising and social gaming are two major points of revenue for social networks.

Literature review suggests that consumer are using different social media tools and their buying decision processes are influenced by social media (Solis, 2010; MacInniss, 2010; Jaffe, 2010, Brown and Hayes, 2008; Foxall, 2005). Though the literature review reveals that use of social media tools influences consumer buying behavior but to the best of my knowledge no study has investigated about the exact role of social media on consumer buying behavior. Hence there exists a gap in academic literature about the role of uses of social media tools on consumer buying behavior more specifically in the context of Bangladesh market. Hence this study tries to fill in the gap by investing the role of social media on consumer buying behavior.

3. OBJECTIVES:

Despite the increasing use of social media in the marketing and critical role of use of social media on consumer buying behavior, the matter is insufficiently addressed in the academic literature. This study tries to fill in this gap in the context of Bangladesh market. The specific objective of this study is as follows:

· To find out the impact/role of use of social media at the various stages of buying decision process on effective buying decision process.

4. LITERATURE REVIEW:

Marketing landscape has changed a lot in the last decade. In the past firms and their buyers had no direct link with each other’s respective world (Jafee, 2010, p-160). But with the emergence of social media consumers now not only can interact with each other but also can communicate with the firms. Social media tools allow users to share contents, recommend products and can share about their positive or negative experience with firms and hence buyers, with their greater use of social media tools and by spreading their word-of mouth can influence more on their peers (Brown and Hayes, 2008). De Valek et. al. (2009, cited by Fotis et.al, 2012) suggests that virtual communities are becoming a key important information source to gather knowledge which in turn influences buyer behavior. Modern consumers are likely to be participating than watching i, e, a shift in the consumer behavior has been witnessed from ‘consumers as viewers’ to consumers as participants’ (Wertime and Ferwick, 2008) and this shift is the consequence of growing use of social media tools by the consumers.

4.1 Consumer Behavior and Buying Decision Process:

In marketing literature, different models explain consumer behaviors in their buying decision processes and these models portray the process of proceeding through a major purchase decision (Erasmus et. al., 2001). During consumption, consumers pass through different stages in the consumer’s purchasing decision-making process (Belch and Belch, 2003). Engel et. al. (1968) introduced EKB model which is considered as the most famous consumer decision making model. The Engel Kollat Blackwell Model of Consumer Behavior consists of four distinct stages, which are information input stage, information processing stage, decision process stage and variables influencing the decision process. Hoyer and MacInnis (2010) recognized five stages in the consumer buying decision process, these are: need recognition, information search, evaluation of alternatives, decision making and post-purchase evaluation. Need recognition, information search, evaluation of alternatives. Howard and Sheth (1969) in their consumers’ purchasing decision-making model incorporates/highlights 3 stages in consumer buying decision process. These are extensive problem solving; limited problem solving; and routinized response behavior. In first stage (Extensive problem solving), consumers usually do not have any prior experience about products they are looking for hence they usually have no/little information about brands and have not yet defined criteria that help them to choose products and undertakes information search about the brands. In the second stage (limited problem solving) consumers have gathered information and succeeded to define evaluation criteria but not yet decided about the brands which will suit them. In the final stage (routinized response behavior) consumers have strong predispositions toward the brand and are now ready to purchase a particular brand that they have chosen after having enough information and made an evaluation of alternatives. McKinsey and Company introduced the ‘Consumer Decision Journey’ model in 2009. As per this model, consumers pass through different stages during their decision making journey. Loyalty loop is integrated in this model and allows consumers who want to repurchase the same product not to go through all the stages they went through for the first purchase. Consumers can use the loyalty loop to go directly at the ‘moment of purchase’. There exist some differences between different theories and models for the consumers’ purchasing decision-making model in the academic literature but the most of them integrate the five stages in the consumers’ buying decision process. These are: need recognition, information search, evaluation of alternatives and post-purchase behavior. In their purchasing decision-making process, consumers are often influenced by both internal and external influences (Belch and Belch, 2003).

4.2 Social Media and Types of Social Media:

Social media is a term which is used to describe the type of media that is based on conversation and interaction between people online where the key difference between traditional media and social media is in social media content is not a corporate monologue, it is conversation where participants can upload content, discuss, and also rate content (Strauss and Frost, 2011). Social media is not a one-way broadcast channel (Solis, 2011). Social media have been defined as a group of internet based application that build on the ideological and technological foundations of Web 2.0 that allow the creation and exchange of user-generated content (Kaplan and Haenlein, 2010). Facebook, Google+, YouTube and twitter hold the largest market shares. LinkedIn and Pinterest have very low shares (Belch and Belch 2015). With the help of measurable vast network of customers with trustworthy data and with real-time feedback of customer experiences, social media firms have successfully transformed the attitudes and perceptions of consumers and in the end helped revolutionized many businesses (Khan and Siddique, 2013). Hoyer and MacInnis (2010) defined social media as a two-way communication where information from a personal source seems more effective than information from the mass media because when a person speaks it makes more real and persuasive towards the audience. Social media spreads the word about product and brand on the Web using tools and websites that allow a conversation to take place between marketers and their target customers (Parker, 2011).

Different types of social media are available to people/buyers to connect with each other and to form communities. These social media tools allow people to publish, to share, play, build network, buy and localize. People have access on social media from different devices which allows them to connect them from everywhere. Parker (2011) classified social media into different categories. These are blogging (e.g. TypePad, WordPress, Blogger), microblogging (e.g. Twitter, FriendFeed), social networking (e.g. Facebook, LinkedIn, Orkut, Plaxo, Ning, MySpace), social bookmarking (e.g. Digg, Stumble Upon, Delicious), multimedia sharing (e.g. You Tube, Flickr), reviews and opinions (e.g. Epinions, Trip Advisor, eHow), wikis (e.g. wikipedia), forums. Zarrella (2010) extended this list by adding Virtualworlds (e.g. SecondLife) as one social media tool. Solis and Thomas (2011) classified social media tools into a lot more categories like social bookmarks, comment and reputation, crowd sourced content, blog platforms, blogs/ conversations, blog communities, micromedia, lifestreams, twitters, SMS/voice, social networks, niche networks, customers service networks, location, video, video aggretation, documents, events, music, wiki, livecasting video and audio, pictures. This is not a complete list of different types of social media tools as in every year new social media tool getting introduced.

4.3 Social Media and Consumer Buying Behavior:

At the end of 2004 Facebook had 1 million users which became 100 million in 2008 (Facebook, 2008) and at the end of 2014 it had 1.44 billion users (The Associated Press). Twitter counted 50 million tweets per day in March 2010 which went up to 140 million tweets per day in March 2011. 800 million visitors visited YouTube each month and number of videos viewed everyday has doubled in between 2010 and 2012 to reach 4 billion in May 2012 (YouTube, 2012).

Firms started to identify the opportunities to exploit from the growth of social media users and their usage rate. 87% of Fortune Global 100 firms are using at least one social media tool which is 10% higher than the previous two years and average number of followers on firms’ Twitter accounts and Facebook pages has also extremely increased during that time (Burson and Marsteller, 2012).

In 2016, more than 81 percent of the United States population had a social media profile (www.statista.com). Number of active social media users passed 3 billion (3.028 billion) in August 2017 with no signs of slowing (www. nextweb.com). With over 1.86 billion monthly active users, social network Facebook is currently the market leader in terms of reach and scope (www.statista.com)

Consumers buying decision process consists of different stages which are influenced by both internal and external sources (Belch and Belch, 2003) and Hoyer and MacInnis (2010) recognized five stages which are need recognition, information search, evaluation of alternatives, decision making and post-purchase evaluation.

Social media has changed the consumer behavior. In the past consumers used to wait for firms to push messages towards them but now they are seeking information directly on social media. Online communities have more and more influence on consumer buying behavior. When people are in the research or education phase of the buying cycle, they can consult before taking any decision because digitally empowered shoppers have their access with different current, past, potential customers, peers and experts. Buyers can compare alternatives based on reviews, opinions and ratings of products and firms available on social media platforms. Moreover with the help of social media buyers can directly seek information from the firms. Consumers are exposed to excessive communication from the firms and hence they are no longer interested to listen and to be guided by their communication. Moreover with the help of social media tools, they tend to filter these communication (Jaffe, 2010)

Literature review (e.g. Solis, 2010) suggests that today’s consumers are well exposed to different types of social media tools and their buying decision processes are influenced by social media and online communities and this influence is reflected in different forms and in a different degrees. Hence it is hard to mention the exact influence of social media on consumer buyer behavior.

Social media tools are considered as non-marketing approach/source hence are more credible to the buyers as buyers do not believe that these sources have personal stakes/interests in the purchase and consumption products and buyers are aware of difference between traditional marketing communication and social media (Solis, 2010, MacInnis, 2010). As a non-marketing source social media can gather a lot of people who can easily through word of mouth share ideas and contents with other consumers (Jafee, 2010) as social media platforms allow consumers to interact together and form communities. Buyers who interact on social media tools have their willingness to listen to their peers, to trust them and hence media tools can influence buyer behavior. Nielson (2009) reported that 70% of people who interact through online trust consumers’ opinions posted online.

4.4 Social Media and Buying Decision Process:

Need recognition starts from the realization of customers that their existing needs are not satisfied (Hoyer and Macinnis, 2010, p-12). Needs can be triggered by any internal or external stimuli. Social media might trigger needs through advertisement displayed on a Facebook page for example or through a discussion with a friend that could make consumers recognize that they have an unmet need. When consumers like brand/product on Facebook, all their contacts are able to see this ‘like’. ‘Follow’ button in Twitter works in similar way. Fancy combines different social media tools (e,g. blog and social bookmarking) and is similar to Facebook and Twitter pages and displays pictures of trendy products to its users and allows them to click on the ‘fancy‘ button of products they would like to buy.

Need recognition stage is followed by information search stage (MacInnis, 2010). As mentioned above consumers can collect information from their friends in social media and brand/product page on Facebook. Though during information search stage consumers can undertake internal or external search but in case of external search consumers often use personal sources (Belch and Belch, 2003). Hence consumers are using social media tools in their information search stage. Whenever consumers buy a product for first time or when they buy an expensive/costly product they need to undertake more research and social media play influence more because social media provide information about product or brand and potential buyers can be engaged with the firms (McKinsey and Company, 2010). Jaffe (2010) pointed out that whenever social media allow to have a two way communication, then consumers trust other consumers more than the firms. Hence to the consumers, information from a social media source is more trusted to the buyers.

To evaluate alternatives, consumers compare products and brands to make the choice(s) that meet their needs the most. Social media offer consumers a wide range of reviews and opinions which help them to compare alternatives (Parker, 2011). Messages delivered by trusted friends who share their own experience influence more (Brown and Hayes (2008). Valuable information can be obtained from the forums and they allow discussions with current/ former customers and communication of experience from peers has a strong influence on evaluation of alternative stage (Jaffe, 2010).

At the end of evaluation of alternative stage consumers decide whether they will buy a product or not. This purchase decision depends not only on the motivation of consumers but also on the outcomes of previous stage (s).

In the post-purchase behavior stage, consumer evaluate the outcome of their purchase decision (Hoyer and MacInnis, 2010) and is very important for customers as it influences future purchase pattern of consumers (Foxall, 2015), as well as future purchase patterns of their peers (Jafee, 2010). In the past consumers who wanted to share their consumption experience were able to share it to a limited number of people but now with the help of social media users can share their good or bad experience to unlimited people or potential customers.

Hence the academic literature suggests that there exists increasing influence of social media on consumer buying decision process however it is not very clear about the exact influences of social media on consumer buying behavior because of unavailability of relevant academic articles in this regard more specifically in the context of Bangladesh market.

5.0 METHODOLOGY:

This research can be classified in terms of research design it is conclusive; in terms of purpose of the research it is causal; in terms of the process of this research it is quantitative; in terms of logic of the research it is deductive; and In terms of outcome of the research this is a basic research. Interval scale was used to measure buyers’ attitude attitudes towards different statements on use of social media and their impact on effective buying decision processes. Questionnaire has been designed in a way so that it provides meaningful data. Response format of the questionnaire was closed. Multi-item measure and five points Likert-style rating scale anchored by ‘strongly disagree’/most unimportant (i.e., value 1) and ‘strongly agree’ /most important (i.e., value 5) have been chosen to operationalise all variables. Buyers of different areas of Dhaka city constitute populations. Dhaka city is large with a huge population and hence it provides sampling frame of adequate size but the sampling frame is not standardized as it is not very accurate and not easily accessible. Sampling units of this study were individuals who usually use social media. The survey for this study has been conducted at different areas of Dhaka city. Convenience type non-probability sampling technique has been considered to select the respondents from the sampling frame where primary data is collected by surveying a structured questionnaire by using personal interview technique. Primary data have been collected from 207 valid respondents in order to investigate the impact/role of use of social media at the various stages of buying decision process on buying behavior. Both Bivariate correlation analysis and multiple regression analysis have been used to examine causal relationship among variables.

6.0 RESULTS AND DISCUSSIONS:

6.1 Correlation between Use of Social media at Different Stages of Buying Decision Process and Effective Buying Decision Process:

The value of Pearson’s r were considered to assess the strength of the relationship between variables and significance level (sig value) were taken into consideration to measure the level of statistical significance/confidence As suggested by Cohen (1988, cited by Pallant, 2007) correlation is small if r = 0.10 to 0.29; medium if r = 0.30 to 0.49; and large if r = 0.50 to 1.0. Table-1 highlights the correlation between use of social media at different stages of buying decision process and effective buying decision process. Use of social media at all stages has weak association with effective buying decision process. In this case use of social media in the problem recognition stage (r= 0.228, sig value= 0.001); use of social media in the purchase decision stage (0.138, sig value=0.047); and use of social media in the post-purchase decision (r= 0.223, sig value= 0.001) have weak but statistically significant association with effective (easy and simple) buying decision process. Uses of social media during information search stage and evaluation of alternative stage have no statistically significant associations with effective buying decision process. Overall, use of social media has weak but statistically significant association (r= 0.241, sig value= 0.000) with effective buying decision (see; table-2).

Table-1: Correlation between Use of Social media at Different Stages of Buying Decision Process and Effective Buying Decision Process

		Effective (Easy and Simple) Buying Decision Process
Problem/need Recognition	Pearson Correlation	.228^^
	Sig. (2-tailed)	.001
	N	207
Information search/gather	Pearson Correlation	.075
	Sig. (2-tailed)	.282
	N	207
Evaluation of Alternatives	Pearson Correlation	.103
	Sig. (2-tailed)	.139
	N	207
Purchase decision-influence	Pearson Correlation	.138^*
	Sig. (2-tailed)	.047
	N	207
Post Purchase Decision-word-of-mouth	Pearson Correlation	.223^**
	Sig. (2-tailed)	.001
	N	207
Easy and Simple Buying Decision Process	Pearson Correlation	1
	Sig. (2-tailed)
	N	207

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Table-2: Correlation between Overall Use of Social media and Effective Buying Decision Process

		Social Media Use	Effective (Easy and Simple) Buying Decision Process
Social Media Use	Pearson Correlation	1	.241^**
	Sig. (2-tailed)		.000
	N	207	207
Easy and Simple Buying Decision Process	Pearson Correlation	.241^**	1
	Sig. (2-tailed)	.000
	N	207	207

**. Correlation is significant at the 0.01 level (2-tailed).

6.2 Output from Multiple Regression Analysis

Table-3: Model Summary

Model Summary^b
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.292^a	.085	.062	.934

a. Predictors: (Constant), Post Purchase Decision-word-of-mouth, Information search- gather, Evaluation of Alternatives-gather, Purchase decision-influence, Problem Recognition-recognize needs

b. Dependent Variable: Effective (Easy and Simple) Buying Decision process

Table-4: ANOVA

Model		Sum of Squares	Df	Mean Square	F	Sig.
1	Regression	16.328	5	3.266	3.744	.003^a
	Residual	175.325	201	.872
	Total	191.652	206

a. Predictors: (Constant), Post Purchase Decision-word-of-mouth, Information search- gather, Evaluation of Alternatives-gather, Purchase decision-influence, Problem Recognition-recognize needs

b. Dependent Variable: Effective (Easy and Simple) Buying Decision Process

Table-5: Coefficients

Model		Unstandard ized Coefficients		Standardized Coefficients	T	Sig.	95.0% Confidence Interval for B		Correlations			Collinearity Statistics
Model		B	Std. Error	Beta	T	Sig.	Lower Bound	Upper Bound	Zero-order	Partial	Part	Tolerance	VIF
1	(Constant)	2.480	.376		6.600	.000	1.739	3.220
	Problem Recognition-recognize needs	.230	.094	.206	2.438	.016	.044	.415	.228	.170	.165	.640	1.563
	Information search/gather	-.067	.074	-.073	-.912	.363	-.212	.078	.075	-.064	-.062	.717	1.395
	Evaluation of Alternatives	.051	.064	.056	.798	.426	-.075	.176	.103	.056	.054	.924	1.083
	Purchase decision-influence	.016	.065	.018	.244	.808	-.113	.144	.138	.017	.016	.804	1.244
	Post Purchase Decision-word-of-mouth	.146	.067	.159	2.172	.031	.013	.279	.223	.151	.147	.852	1.173

a. Dependent Variable: Effective (Easy and Simple) Buying Decision Process

Table-6: Casewise Diagnostics

Case Number	Std. Residual	Easy and Simple Buying Decision Process	Predicted Value	Residual
17	-3.158	1	3.95	-2.949
19	-3.175	1	3.97	-2.965

a. Dependent Variable: Effective (Easy and Simple) Buying Decision Process

Table-7: Residual Statistics

	Minimum	Maximum	Mean	Std. Deviation	N
Predicted Value	2.60	4.37	3.86	.282	207
Std. Predicted Value	-4.450	1.840	.000	1.000	207
Standard Error of Predicted Value	.076	.357	.150	.052	207
Adjusted Predicted Value	2.71	4.43	3.86	.283	207
Residual	-2.965	1.668	.000	.923	207
Std. Residual	-3.175	1.786	.000	.988	207
Stud. Residual	-3.192	1.839	.000	1.004	207
Deleted Residual	-3.014	1.769	.000	.953	207
Stud. Deleted Residual	-3.268	1.850	-.002	1.011	207
Mahal. Distance	.352	29.159	4.976	4.517	207
Cook's Distance	.000	.092	.005	.012	207
Centered Leverage Value	.002	.142	.024	.022	207

a. Dependent Variable: Effective (Easy and Simple) Buying Decision Process

Figure-1: Normal P-P Plot of Regression Standardized Residual

Figure-2: Scatterplot

Tolerance is an indicator of how much of the variability of the specified independent is not explained by other independent variables. If this value is very small (less than 0.10), it indicates that the multiple correlations with other variables is high, suggesting the possibility of multicollinearity. The VIF value is just the inverse of the tolerance value. VIF (variance inflation factor) value above 10 indicates multicollineariy. In this study tolerance values are above 0.10 and VIF values are less than 10 (see: table-5). Hence no multicollinearity exists. In the normal P-P plot (see: fig-1) points lied in a reasonably straight diagonal line from left to top right. It suggests some deviations from normality. In the scatterplot of standardized residuals (see: fig-2) residuals are roughly rectangularly distributed with the scores not concentrated in the center. It suggested some violation of assumptions.

Outliers can be checked by inspecting the Mahalalanobis distances that are produced by the multiple regression programs. Critical chi-square value for evaluating Mahalalanobis distance is 22.46 if number of independent variable is 6 (Pallant, p-157). Residual statistics table (see: table-7) maximum value in this data file is 29.159 which exceeds the critical value. To find out which case has this value Case wise diagnostics table is used. This presents information about cases that have standardized residual values above 3.0 or below -3.0. In a normally distributed sample, we would expect only 1% of cases to fall outside this range. In the sample of this study only 2 cases (see: table-6) out of 207 cases fall outside this range. To check whether these outliers have any undue influence on the results for this model as a whole the value of Cook’s Distance is checked. According to Tabachnick and Fidell (2007; cited by Pallant, 2007 p-158) cases with values larger than 1 are a potential problem. In this study, the maximum value of Cook’s Distance is 0.092 (see: table-7) which is lower than 1 suggesting no major problems. Model Summary table indicates that the value of R-Square is 0.085 (see: table-3), this means that this model explained 8.5% of variance in consumer buying decision process. This model in this study reached statistical significance at significant at the 0.01 level (Sig. = 0.003; this really means p < 0.010; see: table-4).

In this case, the largest beta coefficient +0.206, which is use of social media during problem recognition stage this means that use of social media during problem recognition made the strongest unique contribution to explain the dependent variable effective consumer buying decision process, when the variance explained by all other variables in the model is controlled for. The Beta value for use of social media during post-purchase decision is 0.159, indicating use of social media at this stage made second largest contribution to explain effective consumer buying decision process. Sig values indicate use of social media at these two stages is making statistically significant contribution in the equation (see; table-5).

7. CONCLUSIONS, MANAGERIAL IMPLICATIONS AND FURTHER RESEARCH:

The primary contribution of this study was to verify the role of use of social media during stages of consumer buying decision process on effective consumer buying decision process in the context of Bangladesh market. Based on the findings the following conclusions are drawn:

· The associations of use of social media in the problem recognition stage (r= 0.228, sig value= 0.001); use of social media in the purchase decision stage (0.138, sig value=0.047); and use of social media in the post-purchase decision (r= 0.223, sig value= 0.001) with effective (easy and simple) buying decision process are weak but statistically significant.

· Overall, use of social media has weak but statistically significant association (r= 0.241, sig value= 0.000) with effective buying decision (see; table-2).

· Use of social media during problem recognition stage (Beta= +0.206) made the strongest unique contribution to explain the dependent variable effective consumer buying decision process and is statistically significant (sig= +0.016). Use of social media during post-purchase decision (Beta= -0.159) made second largest contribution to explain dependent variable and is statistically significant (sig= +0.031).

This research reveals that use of social media during problem recognition stage and post-purchase decision stage has significant associations with effective buying decision process and managers in the Bangladesh market should use social media tools to generate demand of their product offerings. Marketers should also offer superior value products to provide customer satisfaction so that they spread positive word-of-mouth because negative word-of-mouth can be spread to millions of customers with in a moment which will in turn negative outcomes for the firm. This could be used as a guideline for the marketers for developing marketing strategies to influence buying decision processes of the consumers which will in turn develop customer satisfaction.

The findings of this study might not be directly generalised as the study considered only limited number respondents and the respondents were selected from different organizations (banks/other corporate offices) and from different households with the help of convenience sampling. At present, to the best of my knowledge there are no studies investigating the impact of use of social media on effective buying decision process. Therefore more comprehensive research is required in other settings and sample populations in order to verify and generalise the findings of this study. In conclusion, this study could lead to further research on consumer buying behaviour in toiletries sector.

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Received on 29.09.2018 Modified on 31.10.2018

Asian Journal of Management. 2019; 10(1): 53-60.

DOI: 10.5958/2321-5763.2019.00010.6