Are smartphones a smart marketing buy?
Almost two-thirds of 106 undergraduate students surveyed have used their smartphones for purchases, mostly purchasing apps, music and videos, and entertainment tickets. The dollar amount spent is low. Smartphones are seen as saving time and money when shopping, and almost all users use them while shopping mostly to compare prices, call/text someone about a product, forward product images to friends/family, or find other stores that have products. One area that is underutilized is QR codes. Being more extroverted and a spontaneous shopper increases the odds ratio of buying something while using your smartphone during shopping.
REVIEW OF LITERATURE
A smartphone is a handheld device that integrates mobile phone capabilities with the more common features of a handheld computer or PDA. Smartphones allow users to store information, e-mail, install programs, along with using a mobile phone in one device (Mobile Marketing Association, 2011). Consumer adoption is driven by perceived ease of use and usefulness (Lee et al., 2009). Smartphones help manage everyday life by providing relevant information and strengthen user's relationships (Liu, 2010). They are becoming ubiquitous (representing 30 percent of consumer electronic sales in 2012 along with tablets (Snider, 2012)), and they are expanding in function: countless entertainment applications, mobile payment devices--almost half of American smartphone users will purchase or have purchased something using their smartphone--and marketing opportunities (Nittala, 2011). ShopSavvy allows smartphone users to scan product barcodes and find the lowest price online or at brick-and-mortar retail outlets (Zmuda, 2010). Yowza, using GPS location, delivers coupons for nearby stores (Zmuda, 2010). Shopkick identifies the store you are in and offers reward points on future purchases (Milian, 2012a). Seventy-nine percent use their devices in retail outlets to compare prices, find product information, or locate another store (Levine, 2011). The potential of smartphone use for marketing is demonstrated through the relief effort for Haiti after its devastating earthquake. Over $30 million dollars was donated from mobile phones in 10 days, accounting for 14 percent of all donations (Choney, 2010).
The proliferation of smartphone use has created opportunities for savvy marketers who can target individuals with interactive ads (Bauer et al., 2005). Even small businesses are using e-mail marketing. A recent study shows 95 percent have used the medium (Stein & Devaney, 2012). Email (from businesses) opened on mobile devices are up 34 percent from April to September of 2012 (Soat, 2012). Within three years, it is estimated that mobile shopping will account for 12 percent of global ecommerce turnover (PIRQ, 2012). Almost three-quarters of smartphone users found it useful to download mobile coupons to their phone or receive coupons while shopping in a store (PIRQ, 2012). More than two-thirds of smartphone owners choose it over a tablet (McAtee, 2011). However, there are concerns.
Sixty-six percent of smartphone users in one study were annoyed at receiving a mobile phone advertisement, and almost a third were very concerned about how the business got their telephone number. More than half who reported receiving a mobile phone advertisement indicated they would be less likely to purchase from the sender (Hanley & Boostrom, 2011). This may be solvable. More than half of smartphone users indicated they would accept text messages notifying them of coupons or discounts. Sit-down restaurants were the highest-rated discount to receive, followed by fast-food restaurants and movies. Half of smartphone users downloaded or purchased ringtones. Smartphone users see benefit in their location being shared but are concerned about privacy issues (Jones & Grandhi, 2005).
In 2011 only 6 percent of mobile users scanned QR codes (Skeldon, 2011). (QR codes are two-dimensional barcodes accessed through cameras on smartphones.) The numbers are better for college students. Eight percent of all magazine advertisements in December 2011 included one-- double the previous year--but only five percent of Americans scanned one (Milian, 2012b). Among college students, nearly three quarters recognize QR codes, with 40 percent seeing them in newspaper or magazine advertisements, yet less than two in 10 have actually scanned them, because they did not know if their phone was AR code compatible, what QR codes do, or have an interest in receiving information (Sago, 2011). A quarter scanned them for additional information or to get a coupon, with one in six using them to interact with social media. Males were more likely to use QR codes than females.
The best mobile advertisements are unobtrusive, targeted, and unique (Khan, 2008). Fiftysix percent of teens and 37 percent of adults would view mobile ads if incentives were provided. In an Indian study, those willing to register for incentivized mobile ads are more male and want cash or free minutes (Nittala, 2011).
The survey to assess perceptions about smartphones and their uses from a marketer's perspective was developed through a literature review, multiple iterations among undergraduate students in a marketing research class, and a pre-test using a protocol analysis with 24 undergraduate students. It was administered at a southeastern university. Students completed the survey through paper and pencil.
The data were analyzed in SPSS version 20. Data were recorded by one person and reviewed for mistakes by another. Frequencies were then examined to ensure no data were outside the range of feasible answers. Individual questions were tested against the scale midpoint, for example, of four in a one-sample t-test (seven-point scale). Hypotheses were tested at the .05 and .10 level. An independent-samples t-test was used when comparing across groups, again at the .05 and .10 level. Pairwise deletion was used (i.e., deleted by individual by question). Outliers were identified through Mahalanobis' Distance where the dependent variable was respondent number and the critical value is chi-squared with the number of degrees of freedom equal to the number of independent variables.
Three scales were created by summing the items within. The first scale, shopping spontaneity, measured respondent's spontaneity for shopping. It is a seven-item scale where each question is bounded by strongly agree to strongly disagree (seven points) (Rook & Fisher, 1995). The second scale is smartphone shopping (Weun, Jones, & Beatty, 1997). It is a fouritem scale bounded by strongly agree to strongly disagree (seven points). It examines shopping behavior when a smartphone is present. The final scale measures personality, from introversion to extroversion, and is an eight-item scale bounded by strongly agree to strongly disagree (five points) (John & Srivastava, 1999).
Additional Statistical Tools
An Exploratory Factor Analysis is conducted to assess uni-dimensionality on the three scales with a Varimax rotation. Kaiser-Meyer-Olkin Measure of Sampling Adequacy (above .6), Bartlett's Test of Sphericity (significant at the .05 level), and the Anti-image Matrix (small offdiagonal values) are examined to determine whether the data is appropriate for factor analysis. Eigenvalues greater than one and a Scree plot is used to determine the number of factors. The rotated component matrix is examined to determine whether the variables and factors match theory (i.e., correlation between variables and factors and mathematical signs). Internal consistency is measured for all three through Coefficient Alpha. Coefficient Alpha values of .70, an indicator of reliability, is adequate in the early stages of predictive or construct validation research (Nunnally & Bernstein, 1994). If uni-dimensional (factor analysis) and internally consistent (Coefficient Alpha), the items are summed to create a scale.
Discriminant Analysis and Logistic Regression are used to determine differences among those who use their smartphone for purchases and those who do not and to classify groups. Multicollinearity is estimated through regression by examining the tolerance and the variance inflation factor, where the dependent variable is respondent number and the three scales are the predictors. Tolerance is "the percentage of the variance in a given predictor that cannot be explained by the other predictors" (IBM SPSS, 2011).
For Discriminant Analysis, the Box's M Test is examined and used to test significant differences in the covariance matrices across groups (Tabachnick & Fidell, 2011). The canonical correlation is the correlation between the discriminant scores and the dependent variable levels. Squaring it provides an effect size. Wilks' Lambda and chi-squared values are examined to determine whether the function discriminates well based on dependent variable levels. Since the dependent variable has only two levels, only one discriminant function will be estimated. Standardized coefficients will be used to determine the contribution of each independent variable. The standardized and the structure matrix coefficients are examined to determine the relationship between the independent and dependent variables. The latter are similar to factor loadings or correlations between the function and predictors. The hit rate or correct classifications for the discriminant function is also examined.
Logistic Regression does not require assumptions about the independent variables (i.e., normality, linearly related, or equal variances with groups). Multicollinearity is a potential problem (also with Discriminant Analysis). Outliers are detected through examination of the standardized residuals (values greater than three). The -2 Log Likelihood (perfect model equals zero, where each case's predicted and actual probabilities are compared and summed) and Goodness of Fit indicates model fit (low values are better). Chi-squared is also used to assess the overall model by comparing the estimated with an intercept only models. Cox & Snell and Nagelkerke R (bounded by zero and one) indicate the proportion of variability in the dependent variable accounted for by the equation's predictor variables. The Hosmer and Lemeshow Test with a p-value greater than .05 indicates good fit (Hilbe, 2009). Each predictor's significance is tested through a Wald statistic. For each predictor, we will examine the unstandardized regression coefficient (B), Wald statistic, and odds ratio (Exp - B).
The survey instrument was completed by 111 undergraduate students. Five surveys were unusable because of incomplete data, and no question had more than six omitted responses. Table 1 indicates respondents were mostly males (51%) who majored in business as undergraduates (52%). Almost half live in suburban areas (44%) with the remainder almost equally distributed between urban (28%) and rural areas (26%). Almost two-thirds live on-campus during the academic year (64%). Sixty-eight percent are either juniors (30%) or seniors (38%). Slightly more than 10 percent are student athletes (13%). They work an average of 15 hours weekly and study an additional 10 hours. Thirty-one percent do not work at a paid or unpaid job. Thirty-six percent do not belong to a student organization, and the average is 1.8 (including non-participants), with a maximum of 17. Eighty-six percent pay for a portion of their college expenses (including tuition, room and board, books, health care, fees, loans and excluding grants and scholarships). The average percent is 38, including the 14 percent who pay nothing. The average GPA is 2.99.
Table 2 shows that among those using their smartphone for purchases, respondents spend an average of $37.25 per month on purchases with their smartphone; however, 39 percent have never purchased anything with a smartphone. Respondents used their smartphone 10 times since January to purchase something or slightly more than twice a month. Here is what they purchase: apps (66%); music or videos (55%); entertainment tickets (39%); clothing, shoes, or accessories (28%); electronic goods (22%); and travel tickets (14%).
Table 2 also shows that respondents believe that having a smartphone when shopping saves time and money. When shopping in a retail outlet, 95 percent use their smartphones for the following: compare product prices (57%); call/text someone about a product (52%); forward product images to friends/family (49%); find other stores that have the product (45%); snap product pictures (36%); find coupons or deals (31%); scan QR codes (23%); read online product reviews (19%); peruse products through websites (14%); and pay at the register (2%). Almost a quarter use QR codes. They used them to: access additional information (29%); get a coupon (25%); interact with social media (13%); sign up for additional information (9%); enter a sweepstake (9%); access video (6%); and make a purchase (5%).
Table 3 suggests that having your smartphone when shopping does not lead to unplanned purchases or the disregard of consequences when purchasing.
Table 4 presents the outcome of the conducted Exploratory Factor Analysis. The model is factorable since the Kaiser-Meyer-Olkin measure is .792, Bartlett's Test of Sphericity is significant at the .000 level, and only one value in the Anti-image Matrix is above 4. The correlation matrix has many large bivariate correlations. An Exploratory Factor Analysis was conducted and four eigenvalues were greater than one, although the final was 1.1. The first three eigenvalues account for 62 percent of the variation in the original variables.
The fourth factor accounts for only 6 percent of the variation and appears to be a subfactor of the personality scale. As such, the model was rerun with only three factors. The rotated component matrix indicates that the original variables load (high value and correct sign) on the three factors according to theory: smartphone shopping, shopping spontaneity, and personality. Three dimensions are extracted accounting for 63 percent of the variability. The Scree Plot confirms a three-dimension solution. Uni-dimensionally is established.
Coefficient Alpha for the four-item scale smartphone shopping scale indicates internal consistency among the items (CA=.901). A summed scale was created bounded by four (smartphone use leads to unplanned purchases) to 28 (it does not), with a neutral point of 16. Coefficient Alpha for the seven-item shopping spontaneity scale indicates internal consistency (CA=.888). Coefficient Alpha for the eight-item personality scale indicates internal consistency among the items (CA=.834). Four items were reverse coded. A summed scale was created bounded by eight (introversion) to 40 (extroversion), with a neutral point of 24. Respondents scoring below 22 were categorized as introverts and above 26 as extroverts. There are 15 introverts and 59 extroverts.
Table 5 presents the outcome of the conducted Discriminant Analysis. Multicollinearity is not a problem with tolerance (i.e., predictor variance unexplained by other predictors) measures all above .85 and the variance inflation factors (one divided by tolerance) below 1.2 (above two is problematic). The Box's M test (Box's M = 9.66; df1=6; df2=30908.29; p<.160) is not statistically significant; thus, covariance homogeneity is assumed. The canonical correlation (r =.39) is moderately related to the dependent variable levels, with only 15 percent of the variation in the discriminant function accounted for by the dependent variable. Wilk's Lamba is statistically significant (Wilk's Lamba=.85; Chi-squared (3)=15.05; p<.002) indicating the discriminant function derived from the three scales significantly differentiates between those who use their smartphone to make purchases and those who do not. The correlations with the function (Structure Matrix) are in order of magnitude: smartphone shopping (-.80), shopping spontaneity (.55), and personality scale (.48). The hit rate is 69 percent.
The standardized canonical discriminant function coefficients show the smartphone shopping scale (low values mean smartphones lead to unplanned purchases and high values mean it does not) has the highest coefficient (-.68), followed by the personality scale (low values mean introvert and high values mean extrovert) (.53), and finally, the shopping spontaneity scale (low values mean spontaneous and high values mean careful) (.38). The unstandardized coefficients are smartphone (-.12), personality (.08), and shopping spontaneity scale (.04). Using a smartphone during shopping and its effect on unplanned purchases has a positive impact on whether you will purchase something with your smartphone. Extroverts are more likely to use their smartphones for purchases than introverts. Those prone to spontaneous purchasing are more likely to use their smartphone for purchases. Seventy-seven percent of those who do not purchase using their smartphone were correctly identified, while only 65 percent of those who do not were identified.
The -2 Log Likelihood value is 107.05 and the model is significantly different from the constant-only model (chi-squared (3)=15.9777; p<.001). Twenty-one percent of the variation in the model accounted for by the predictor (Nagelkerke R =.214). The Hosmer and Lemeshow Test is not statistically significant (11.71 (8), p<.166). The hit rate is 70 percent, with 85 percent of respondents who purchase with their smartphone and 44 percent of those who do not correctly identified. The Wald statistic is significant for the personality and smartphone shopping scales (p<.044 and p<.02, respectively). A one-unit increase on the personality scale results in an 8 percent increase in the odds ratio of buying something with a smartphone, a one-unit increase on the spontaneity scale increases the odds ratio by 4 percent, and an increase of one unit for the smartphone shopping scale decreases the odds ratio of purchasing something with a smartphone by 10 percent, as presented in Table 4. Being more extroverted and a spontaneous shopper increases the odds ratio of buying something while using a smartphone during shopping. (Scale was reverse coded.)
LIMITATIONS OF THIS STUDY
Every research effort has limitations, and this paper is based on a sample size of 106 college students who are assumed to have low discretionary income. Students were not asked how long they had their smartphones, although anecdotal information indicates they learn the functions of new phones quickly. Results were not tracked longitudinally.
RECOMMENDATIONS FOR FUTURE RESEARCH
Future research should compare college graduates, who have higher levels of discretionary income, with college students. Are results curtailed merely by income (i.e., college students have low discretionary income)? Almost 100 percent of millennials (13 to 34 years of age) own a cell phone, which includes those with smartphone, while less than half of those 75 and older own one (Zickuhr, 2011). Results could be compared by generation.
Alternatives could be created in a tradeoff analysis (e.g., conjoint analysis or discrete choice models) to determine consumer acceptance of mobile advertisements, including incentives. For example, would consumers accept advertisements (type of advertisement could also be tested) in exchange for discount coupons for restaurants?
A cross-cultural study (United Kingdom and Germany) of mobile location-based services found the same core values: anytime/anywhere, always prepared; enabling information; increased opportunities and independence; and simplicity and happiness. UK respondents view their phones as a friend or guide with whom they are proactively engaged, while Germans view their smartphones as a servant and are reactive to it (Wagner, 2011). In general, smartphones are a mobile resource allowing greater connections with family and friends and one's environment (e.g., who lives or lived in the area), and staying in control of information shared. Do results remain constant across cultures?
In a world where cell phones are ubiquitous and smartphones are becoming dominant, marketers have a plethora of opportunities ranging from method of payment to acceptance of advertisements (Snider, 2012). Smartphone purchases are primarily by the young and affluent. Almost two-thirds have used their smartphones for purchases, with most purchasing apps, music and videos, and entertainment tickets. The dollar amount spent is low. Smartphones are seen as saving time and money when shopping, and almost all users use them while shopping, mostly to compare prices, call/text someone about a product, forward product images to friends/family, or find other stores that have the products. The problem is that these activities may drive retail prices down as consumers compare outlets and purchase at the cheapest one. One underutilized area is QR codes, confirming other studies (Sago, 2011). Marketers have to convey the importance or benefit of these. A positive for the industry is that 40 percent of Facebook and Twitter users clicked on five or more QR codes in 2011 (Wadhwani, 2011). The convergence of greater purchases with a smartphone and hackers trying to steal personal information may curtail shopping usage (Acohido, 2012; Acohido, 2011).
Being an extrovert and a spontaneous shopper increases the likelihood of using a smartphone for purchases. Marketers can promote spontaneous shopping, but changing personalities is beyond even their capabilities. What marketers can do is stress the benefits of having a smartphone on when shopping, and given consumers' desire for connectivity, which a recent study found 89 percent of smartphone users valued, may be achievable (Think Insights, 2011). What marketers should stress is how to use a smartphone effectively while shopping, especially the use of QR codes.
Currently, shopping with a smartphone is not seen as generating unplanned purchases. Location-based advertising would assist here (Xu, Oh, & Teo, 2009). Marketers could identify a consumer's location and proximity to retail outlets. Then, it could target that consumer with advertisements for those retail outlets. In an eight-country study, only respondents from India indicated that location-based advertisements affected the way they live their lives (Gibbs, 2012). The types of advertisements will also affect results. Marketers are banking on video ads, which should be about 25 percent of all mobile advertisements by 2014 (Sheth, 2010). Phone manufacturers are improving filters for unsolicited telephone calls, e-mails, and texts (Swartz, 2012). Retailers must create websites easily visible on a smartphone's small display.
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Stephen L. Baglione
Saint Leo University
Stephen L. Baglione is Professor of Marketing and Quantitative Methods at Saint Leo University in Florida. He received his doctorate in Marketing from the University of South Carolina. Dr. Baglione has authored or co-authored almost 80 refereed journal and proceedings articles for publications such as the British Food Journal, Chinese Management Studies, Electronic Markets, Journal of Excellence in College Teaching, Journal of Promotion Management, Quarterly Review of Distance Education, and The Journal of Applied Management and Entrepreneurship. He was a Fulbright Scholar at the University of Ljubljana in Slovenia.
Table 1 Category Respondent Demographics (n=106) Gender Major Male 51% (1) Business 52% Female 49% Non-business 39% Both 0% Undecided 9% Residence Residence (permanent) (academic year) Urban 28% On-campus 64% Suburban 44% Off-campus 16% (no family) Rural 26% Off-campus 20% (family) College Class Freshman 9% Sophomore 20% Junior 30% Senior 38% (1) Because of rounding error may not sum to 100 [beta] Table 2 Smartphone Usage Category Purchase with Smartphone Apps 66% Entertainment tickets 39% Electronic goods 22% Smartphone Usage in Retail Outlets Compare product prices 57% Forward product images to friends/family 49% Find coupons or deals 31% Scan QR codes 23% Peruse products through websites 14% QR Code Usage Access additional information 29% Interact with social media 13% Enter a sweepstakes 9% Make a purchase 5% Music or videos 55% Clothing, shoes, or accessories 28% Travel tickets 14% Call/text someone about a product 52% Snap product pictures 36% Find other stores that have the product 45% Read online product reviews 19% Pay at the register 2% Get a coupon 25% Sign up for additional information 9% Access video 6% (1) Because respondents could check all that apply, values will not sum to 100. Table 3 One-Sample T-test Statements t-stat. value (mean) 1) Shopping using my smartphone saves me time. (1) 2.40 .018 (4.47) 2) Shopping using my smartphone saves me money. 4.35 .000 (4.74) 3) When I shop using my smartphone, I buy things 7.66 .000 that I had not intended to purchase. (2) (5.24) 8.47 .000 4) When I shop using my smartphone, I make (5.37) unplanned purchases. 5) When I shop using my smartphone, it is fun to 6.70 .000 buy spontaneously. (5.17) 6) When I shop using my smartphone, I buy without 8.10 .000 considering the consequences. (5.35) (1) Strongly Agree (1) to Strongly Disagree (7) (2) Last four statements are the smartphone shopping scale. Table 4 Factor Solution (loadings) Rotated Components Matrix Shopping Personality Smartphone Spontaneity Shopping I often buy things .83 spontaneously. (1) "Just do it" describes .82 the way I buy things. I often buy things .82 without thinking. "Buy now, think about it .84 later" describes me. Sometimes I feel like .80 buying things on the spur of the moment. I carefully plan most of .66 my purchases (reverse coded). I buy things according to .63 how I feel at the moment. I see myself as someone who: (2) Is talkative .77 Is full of energy .77 Tends to be quiet (reverse .83 coded) Is reserved (reverse coded) .72 Is sometimes shy, inhibited .57 (reverse coded) Generates a lot of .78 enthusiasm Has an assertive .64 personality Is outgoing, sociable. .68 When I shop using my .87 smartphone, I buy things I intended to purchase. (3) When I shop using my .88 smartphone, I make unplanned purchases. When I shop using my .85 smartphone, it is fun to buy spontaneously. When I shop using my .84 smartphone, I buy without considering the consequences. (1) Strongly Disagree (1) to Strongly Agree (7) (2) Disagree Strongly (1) to Agree Strongly (5) (3) Strongly Agree (1) to Strongly Disagree (7) Table 5 Discriminant Function (dependent variable is purchasing with a smartphone or not) Questions Unstandardized Structure Standardized Coefficients Matrix Coefficients Constant -0.80 Shopping Scale -0.11 -0.80 -0.68 Personality Scale 0.08 0.48 0.53 Spontaneity Scale 0.04 0.55 0.38 Wilk's Lamba=.847; Chi-squared (3)=15.045; p<.002 Table 6 Logistic Regression (dependent variable is purchasing with a smartphone or not) Questions Unstandardized Exp(B) Wald-Statistic Beta (sig) Constant -0.03 0.99 Shopping Scale 0.04 1.04 1.96 (.162) Personality Scale .072 1.08 4.08 (.044) Smartphone Scale -0.11 0.90 5.73 (.017) Chi-squared=15.98, significant (.0010); Cox & Snell [R.sup.2]=.16; Negalkerke [R.sup.2]=.214
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|Author:||Baglione, Stephen L.|
|Publication:||International Journal of Business, Marketing, and Decision Sciences (IJBMDS)|
|Date:||Jun 22, 2014|
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