Market Segmentation In Radio Listening Habits

Market Segmentation In Radio Listening HabitsMarket segmentation plays an important role in tuner listening habits due to terce important reasons. Firstly, it allows marketers to identify groups of listeners with similar needs and facilitate the abridgment of characteristics and listening behaviour of these groups (Soutar Clarke 1983 Kent 1994 Arjona et al. 1998). Secondly, segmentation succeeds marketers with critical information that argon necessary for designing merchandise mixes that are consistent with the characteristics and desires of one or to a greater extent segments (Arjona et al. 1998 Gatfield 2006). Thirdly, segmentation allows radio stations achieved its objectives while satisfying the needs and wants of its customers and listeners (Fitzgerald 2004 Gatfield 2006).According to past inquiry make on radio listening habits had indicated that modus vivendi segmentation is an appropriate and effective near adopted by marketing double-deckers to r individually its t arget audiences (Massy 1971 Soutar Clarke 1983 Evans, Lawson Todd 2006). Therefore, this quantitative write up provide be focusing on a key research question Do lifestyle predicts radio listening patterns for 6WF and 96FM Radio Station?Firstly, an remote market research company was engaged to conduct phone interviews within the Perth metropolitan area and respondents were asked to respond to 43 key sets of AIO (activity, interest and opinion statement). The AIO approach is one of the most common approach use by scholars to measure consumers lifestyle (Li 2004). Respondents lifestyle faeces be assess through a 1-7 likert scale of measurement measurement (where 1 stands for strongly disagree and 7 stands for strongly agree) (Cicia et al. 2010). Appendix 1 shows the AIO questionnaire. Responses were obtained from 400 household in metropolitan Western Australia. Secondly, factor analysis will be used to identify the latent construct of questionnaire as this is commonly use in bus iness research (Hair Jr et al. 2010). A Confirmatory fixings Analysis (CFA) will be used to verify all the questionnaire are categorized on a lower floor the latent dimensions as proposed by previous theories or literatures (Soutar Clarke 1983) because the research will be using the 43 seven-point Lickert scale degrees to gauge if lifestyle influence a respondents listening habits. Thirdly, according to Leung, Fund and Lee (2009) a bit-by-bit regression analysis is an appropriate method used to predict if lifestyle plays an important role in affecting radio listening habits.But in advance a factor analysis can be conducted the assumption of sample size, normality, bilinearity, outliers among cases, multicollinearity and singularity, factorability of the correction matrix and outliners among variables before analysis must be conducted.ResultsThe assumption on sample size was capable as the research is based on 400 responses which are higher than the rule of thumb of 100. Thus , the sampling size is adequate for a factor analysis. When testing for normality on the AIO items, most of the AIO items seem to be normal with the exception of AIO8, 10,11,14,25,31,32,38. These 8 cases also had a series of outliners. Therefore, a data transformation will be required to determine if these questions can be kept for the analysis. After a series of data transformation, normality could non be achieved. Even with the deletion of outliners, normality was not achievable. Therefore these questions were eliminated from the analysis. Thus a total of 35 AIO items will be used.AIO8 AIO10 AIO11AIO14 AIO25 AIO31AIO32 AIO38Reliability StatisticsCronbachs alphaCronbachs Alpha Based on Standardized Items.732.739The reliability statistic as indicated above has a result higher than Cronbachs alpha .70, which indicates an accep elude degree of internal consistency.Confirmatory cistron Analysis will be conducted to verify the hidden dimension of lifestyle towards listening habits as well as to determine the number of items categorized under separately hidden dimension. The factorability of a correlation matrix can be detected via the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy. In relation to this, the measure of sampling enough for each item (as shown on the diagonal of the anti-image correlation matrix) is larger than .5.Anti-image MatricesExtraction Method caput Component Analysis.The Communalities table shows the communalities assess for each of the 35 items in the AIO questionnaire. Note that aio39 (I listen to the radio for a specific announcer or DJ) shows the highest communalities (.832), whereas aio5 (I have traditional ideas about most things) shows the lowest communalities (.413). The entertain of the initial communalities represents the persona of mutation in each item that can be explained by all possible factors. Hence, the value of initial communalities of (1.000) means that 100% of the variance in an item can be explained by all possible factors. On the other hand, the value of extracted communalities represents the fate of variance in each item that can be explained by the extracted factors via the Principal Component (PC) Analysis. Hence the value of the extracted communalities is smaller as compared to the value of initial communalities.Based on the gibe Variance Explained table, we can decide that only 13 factors (with eigen value of more than 1) have been extracted via Principal Component (PC) Analysis. The first extracted factor can explain 11.448% in the items the second factor can explain 10.115% of the variance in the items, all the way to the 11th extractor factor which explained 3.015% of the variance in the items. These 11 extracted factors can explain 65.518% of the variances in the AIO questionnaire items. entireness Variance ExplainedExtraction Method Principal Component Analysis.The Rotation nubs of straightforwardlyd Loadings column reports information on the extracted factors with th eir respective eigen values, the percentage of variance and the cumulative percentage of variance explained by the extracted factor after the Varimax rotation. Only 11 factors (with eigen value of 1.055 to 4.007 respectively have been extracted. The first extracted factor can explain 7.744% of the variance in the items whereas the 11th factor can explain 3.727% of the variance in the items. As a whole, these 11 factors can explain 65.518% of the variance in questionnaire items. The Varimax rotation has changed the percentage of variance explained by the 11 factors (for example Factor 1 from 11.448% to 7.744%).The scree plot displays the eigen value for each of the factor. The plotted eigen value is based on the eigen value reported in Extraction Sum of neatd Loadings column. From the scree plot, observation can be made that there are two dominant factors with an eigen value of greater than 3.540.The Varimax rotation with Kaiser Normalization is conducted to better categorize the l uck matrix. Each item of the questionnaire that best suited a particular component will be categorized together and highlighted in different colors. For example, component 1 consists of items with a factor loading of 0.831 to 0.874. Appendix 2 provides the series of AIO questions associated to each component. Examination of the items classify in each component allows the representation of a conceptually distinct aspect of lifestyle to radio listening as indicated in Appendix 2. The turn Component Matrix provide a form of content validity as it provide an assessment of the correspondence of the variables to be included in each component and its conceptual definition. This form of validity, also known as face validity, subjectively assesses the correspondence between the individual items and the concept through ratings (Hair Jr et al. 2010). For example, items in component 1 can be classified under a main concept or variables TV Addicts. The mean score of items associated to each c omponent will be computed through the Transform Compute Variables function, for example, mean(aio27,aio20,ai04) of each respondent to form a new variables call Lifestyle_TV_Addicts.Rotated Component MatrixaExtraction Method Principal Component Analysis.Rotation Method Varimax with Kaiser Normalization.The computed data had resulted in 11 newly created variables that can be used to perform a multiple regression and provide information if lifestyle influence radio listening behaviour. The list of newly created variables can be found in Appendix 2. A stepwise linear regression will be conducted to address the below mentioned research questionWhat contributions do the 11 variables make to the prediction of radio listening preference of each radio station?Null Hypothesis Lifestyle influence radio listening preferenceAlternate Hypothesis Lifestyle does not influence radio listening preferenceRadio Station 6WFVariables Entered/Removeda stickVariables EnteredVariables Removeddimension01Life style_Cultural_Classical.2Lifestyle_Fashion.3Lifestyle_Outdoor.a. Dependent Variable q3wfThe table above shows the order in which the variables were entered and removed from the model. 3 variables were added and none were removed. In addition, an examination of the Mahalanobis distance MAH_1 values had indicated that there are no variable outliers among the independent variables as there are no values that are greater than or equal to the critical chi square value of 13.8 at an alpha level of .001 (Coakes, Steed Ong 2010).Model SummarydModelRR SquareAdjusted R SquareStd. Error of the Estimate exchange StatisticsR Square neuterF Changedf1df2dimension01.279a.078.0751.991.07833.57613982.332b.111.1061.958.03314.60313973.375c.141.1341.927.03013.9681396a. Predictors (Constant), Lifestyle_Cultural_Classicalb. Predictors (Constant), Lifestyle_Cultural_Classical, Lifestyle_Fashionc. Predictors (Constant), Lifestyle_Cultural_Classical, Lifestyle_Fashion, Lifestyle_Outdoord. Dependent Varia ble q3wfThe above model summary indicated that model 1, which included only Lifestyle_Cultural_Classical accounted for 7.5% of the variance (adjusted R Square = 0.075). The inclusion of Lifestyle_Fashion in model 2 resulted in an additional 3.1% of the variance explained. The inclusion of Lifestyle_Outdoor into model 3 resulted in an additional 2.8% of the variance explained (R Square change = 0.03). The whole model accounted for 13.4% of the variance in radio listening preference, which is highly significant, as indicated by the F-value of 21.635 in the ANOVA table below.ANOVAdModelSum of SquaresdfMean SquareFSig.1 backsliding133.1051133.10533.576.000aResidual1577.7923983.964 essential1710.8973992Regression189.082294.54124.663.000bResidual1521.8153973.833Total1710.8973993Regression240.931380.31021.635.000cResidual1469.9673963.712Total1710.897399a. Predictors (Constant), Lifestyle_Cultural_Classicalb. Predictors (Constant), Lifestyle_Cultural_Classical, Lifestyle_Fashionc. Predictor s (Constant), Lifestyle_Cultural_Classical, Lifestyle_Fashion, Lifestyle_Outdoord. Dependent Variable q3wfThe ANOVA assess the boilersuit significance of the model. As pCoefficientsaModelUnstandardized CoefficientsStandardized CoefficientstSig.BStd. ErrorBeta1(Constant)1.120.267Lifestyle_Cultural_Classical.384.0662(Constant)2.016.3525.732Lifestyle_Cultural_Classical.444.067.3236.625Lifestyle_Fashion-.266.070-.186-3.8213(Constant)1.280.3983.213Lifestyle_Cultural_Classical.452.066.3296.856Lifestyle_Fashion-.306.069-.214-4.413Lifestyle_Outdoor.221.059.1763.737Dependent Variable q3wfTherefore the overall strength of the model in predicting lifestyle influence on radio listening preference for 6WF is as followAdjusted R square = .134 F3,396 = 21.635, p Predictor Variable Beta pLifestyle_Cultural_Classical .329 pLifestyle_Fashion -.214 pLifestyle_Outdoor .176 pRadio Station 96FMVariables Entered/RemovedaModelVariables EnteredVariables Removeddimension01Lifestyle_Radio_Addicts.2Lifestyle_T radition.3Lifestyle_Cultural_Classical.4Lifestyle_Conservative.5Lifestyle_Follower.6Lifestyle_Fashion.7Lifestyle_TV_Addicts.a. Dependent Variable q396fmThe table above shows us the order in which the variables were entered and removed from the model. Seven variables were added and none were removed. In addition, an examination of the Mahalanobis distance MAH_2 values had indicated that there are no multivariate outliers among the independent variables as there are no values that are greater than or equal to the critical chi square value of 13.8 at an alpha level of .001 (Coakes, Steed Ong 2010).Model SummaryhModelRR SquareAdjusted R SquareStd. Error of the EstimateChange StatisticsR Square ChangeF Changedf1df2dimension01.273a.074.0722.448.07431.96113982.365b.133.1292.371.05927.05113973.417c.174.1682.318.04119.60713964.449d.201.1932.282.02713.35813955.468e.219.2092.260.0178.79513946.479f.229.2182.248.0115.38813937.488g.239.2252.237.0094.7881392a. Predictors (Constant), Lifestyle_Rad io_Addictsb. Predictors (Constant), Lifestyle_Radio_Addicts, Lifestyle_Traditionc. Predictors (Constant), Lifestyle_Radio_Addicts, Lifestyle_Tradition, Lifestyle_Cultural_Classicald. Predictors (Constant), Lifestyle_Radio_Addicts, Lifestyle_Tradition, Lifestyle_Cultural_Classical, Lifestyle_Conservativee. Predictors (Constant), Lifestyle_Radio_Addicts, Lifestyle_Tradition, Lifestyle_Cultural_Classical, Lifestyle_Conservative, Lifestyle_Followerf. Predictors (Constant), Lifestyle_Radio_Addicts, Lifestyle_Tradition, Lifestyle_Cultural_Classical, Lifestyle_Conservative, Lifestyle_Follower, Lifestyle_Fashiong. Predictors (Constant), Lifestyle_Radio_Addicts, Lifestyle_Tradition, Lifestyle_Cultural_Classical, Lifestyle_Conservative, Lifestyle_Follower, Lifestyle_Fashion, Lifestyle_TV_Addictsh. Dependent Variable q396fmModel 1, which included Lifestyle_Radio_Addicts accounted for 7.2% of the variance (Adjusted R Square 0.072). The inclusion of Lifestyle_Tradition into model 2 resulted in a n additional 6% of the variance being explained (R Square Change = .059). The inclusion of Lifestyle_Cultural_Classical in model 3 resulted in an additional 4% of the variance being explained (R Square Change = .041). The inclusion of Lifestyle_Conservative in model 4 resulted in an additional 3% of the variance being explained (R Square Change = .027). The inclusion of Lifestyle_Follower in model 5 resulted in an additional 2% of variance explained (R Square Change = .017). The inclusion of Lifestyle_Fashion into model 6 resulted in an additional 1% of the variance explained (R Square Change = .011). Lastly, the inclusion of Lifestyle_TV_Addicts into model 7 resulted in an additional 1% of the variance explained (R Square Change = .009). The whole model accounted for 22.5% of the variance, which is highly significant as indicated by the F-value of 17.547.ANOVAhModelSum of SquaresdfMean SquareF1Regression191.4811191.48131.961Residual2384.4793985.991Total2575.9603992Regression343.593 2171.79730.552Residual2232.3673975.623Total2575.9603993Regression448.9083149.63627.858Residual2127.0523965.371Total2575.9603994Regression518.4864129.62224.885Residual2057.4743955.209Total2575.9603995Regression563.4135112.68322.060Residual2012.5473945.108Total2575.9603996Regression590.630698.43819.486Residual1985.3303935.052Total2575.9603997Regression614.586787.79817.547Residual1961.3743925.004Total2575.960399a. Predictors (Constant), Lifestyle_Radio_Addictsb. Predictors (Constant), Lifestyle_Radio_Addicts, Lifestyle_Traditionc. Predictors (Constant), Lifestyle_Radio_Addicts, Lifestyle_Tradition, Lifestyle_Cultural_Classicald. Predictors (Constant), Lifestyle_Radio_Addicts, Lifestyle_Tradition, Lifestyle_Cultural_Classical, Lifestyle_Conservativee. Predictors (Constant), Lifestyle_Radio_Addicts, Lifestyle_Tradition, Lifestyle_Cultural_Classical, Lifestyle_Conservative, Lifestyle_Followerf. Predictors (Constant), Lifestyle_Radio_Addicts, Lifestyle_Tradition, Lifestyle_Cultural_Classic al, Lifestyle_Conservative, Lifestyle_Follower, Lifestyle_Fashiong. Predictors (Constant), Lifestyle_Radio_Addicts, Lifestyle_Tradition, Lifestyle_Cultural_Classical, Lifestyle_Conservative, Lifestyle_Follower, Lifestyle_Fashion, Lifestyle_TV_Addictsh. Dependent Variable q396fmThe ANOVA assess the overall significance of the model. As pCoefficientsaModelUnstandardized CoefficientsStandardized CoefficientstSig.BStd. ErrorBeta1(Constant).534.537Lifestyle_Radio_Addicts.599.1062(Constant)1.858.5793.208Lifestyle_Radio_Addicts.566.103.2585.505Lifestyle_Tradition-.346.067-.243-5.2013(Constant)3.021.6244.841Lifestyle_Radio_Addicts.624.101.2846.152Lifestyle_Tradition-.390.066-.274-5.921Lifestyle_Cultural_Classical-.348.079-.206-4.4284(Constant)4.330.7116.088Lifestyle_Radio_Addicts.651.100.2966.503Lifestyle_Tradition-.353.066-.248-5.371Lifestyle_Cultural_Classical-.292.079-.173-3.696Lifestyle_Conservative-.363.099-.170-3.6555(Constant)4.539.7086.413Lifestyle_Radio_Addicts.723.102.3297.084Life style_Tradition-.330.065-.232-5.042Lifestyle_Cultural_Classical-.312.079-.185-3.975Lifestyle_Conservative-.351.098-.164-3.568Lifestyle_Follower-.195.066-.137-2.9666(Constant)4.187.7205.815Lifestyle_Radio_Addicts.679.103.3096.567Lifestyle_Tradition-.325.065-.228-4.984Lifestyle_Cultural_Classical-.351.080-.208-4.394Lifestyle_Conservative-.359.098-.168-3.663Lifestyle_Follower-.220.066-.155-3.316Lifestyle_Fashion.193.083.1102.3217(Constant)4.560.7376.191Lifestyle_Radio_Addicts.666.103.3036.463Lifestyle_Tradition-.297.066-.208-4.490Lifestyle_Cultural_Classical-.360.080-.213-4.525Lifestyle_Conservative-.333.098-.156-3.395Lifestyle_Follower-.203.067-.143-3.053Lifestyle_Fashion.210.083.1202.526Lifestyle_TV_Addicts-.162.074-.101-2.188a. Dependent Variable q396fmTherefore the overall strength of the model in predicting lifestyle influence on radio listening preference for 96FM is as followAdjusted R Square = .225, F7,392 = 17.547, p Predictor Variable Beta pLifestyle_Radio_Addicts .303 pLifes tyle_Tradition -.208 pLifestyle_Cultural_Classical -.213 pLifestyle_Conservative -.156 p = 0.001Lifestyle_Follower -.143 p = 0.002Lifestyle_Fashion .120 p = 0.012Lifestyle_TV_Addicts -.101 p = 0.029The models representing both radio stations, 6WF and 96FM, are not a good model as they only explained 13.4% and 22.5% of the variance (R Square) in radio listening preference. Therefore, lifestyle can be seen as an insignificant predictor of radio listening preference. Therefore, the H0 will be rejected and H1 will be accepted and conclude that lifestyle does not influence radio listening preference.ConclusionIt is apparent that, the two radio stations, 6WF and 96FM, did not have distinct audiences with different lifestyle. This is a direct seam to the previous research conducted by Soutar and Clarke (1983) that concluded lifestyle plays a role in influencing radio listening patterns. Therefore, the respective radio stations program manager need not have distinct radio programming polic y to attract a different group of audiences. However, the research has indicated that lifestyle plays a more important role in predicting the radio listening preference for 96FM than 6WF because the model represented in the regression analysis managed to explained 22.5% of the variance, which is 9.1% more than the 6WFs model (13.4% of the variance explained).BibliographyArjona, LD, Shah, R, Tinivelli, A Weiss, A 1998, Marketing to the Hispanic Consumer, The McKinsey Quarterly, vol. 1, no. 3.Cicia, G, Corduas, M, Del Giudice, T Piccolo, D 2010, Valuing Consumer Preferences with the CUB Model A CaseStudy of Fair Trade Coffee, International Journal on nutrient System Dynamics, vol. 1, pp. 82-93.Coakes, SJ, Steed, L Ong, C 2010, SPSS Version 17.0 for Windows Analysis without anguish, Wiley, Milton, Qld.Evans, S, Lawson, R Todd, S 2006, New Zealand in the 21st century A consumer lifestyles study, NZ Post, Loyalty NZ, University of OtagoFitzgerald, J 2004, Evaluating pass on Investm ent of Multimedia Advertising with a Single-Source Panel A Retail Case Study, Journal of Advertising Research, vol. 44, no. 3, pp. 262-270. 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D Aaker, Wads worth, California.Soutar, JN Clarke, YM 1983, LIFE STYLE AND RADIO LISTENING PATTERNS IN PERTH, western sandwich AUSTRALIA, Australian Journal of Management, vol. 8, no. 1, p. 71. Available from buh.Appendix 1AIO Questionnaire