60
61 interesting to use those firms’ private information to create more customer engagement
variables and study the effect of the different (moderating) factors used in this paper on those variables and see how it could lead to higher online customer engagement. Scholars could work together with firms to get excess to their online (social media) data and then examine firms in the same and different industries with different digital content strategies to see how they affect their online performance in terms of customer engagement. Furthermore, future studies could use ‘click-through rate’ as an extra variable when cooperating with firms, as this variable is an important measurement for (digital) marketing managers, especially for those managers who aim to increase traffic on their webpage(s).
Secondly, this study relied on aggregate numbers, limiting the conclusions to some extent.
When investigated on an individual level, that is, on a user-by-user basis, the research may yield more intriguing results because such data allows for data validation and updating.
Furthermore, individual-level data allows for in-depth examination and customization of consumer characteristics. As a result, it gives researchers exploratory power. However, the resources, time, and cooperation necessary for such studies will continue to limit their use in many crucial areas were properly executed, aggregated data analysis can provide meaningful and cost-effective solutions (Lyman & Kuderer, 2005).
Moreover, this study didn’t use the post format as a variable, meaning whether the content of the post was communicated through a photo or a video format. Previous research has looked into this matter and has found that richer media content, i.e. videos, are more effective in social media communication when compared to less rich media content, i.e. photos. However, Shahbaznezhad et al. (2021) found that posts in the form of videos have a more substantial effect on active customer engagement in the form of comments. In contrast, posts in the form of photos have a stronger effect on passive customer engagement, such as liking.
62 Nevertheless, it can be concluded that multiple researchers found strong evidence for the significant impact of the format of the content on different types of customer engagement.
Because of the current study’s time and resource constraints, less substantial effects for the proposed framework could be observed. In addition to that, just one international firm per category could be researched, limiting the conclusions’ generalizability and validity. As a result, it would be interesting for future scholars to add more organizations to the dataset and see what effect those changes in data have on customer engagement outcomes. Besides, more or different industries could be used to research how those changes in variables would affect the moderating role they have on the direct effect between the type of content and customer engagement.
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69 Appendices
Appendix 1 – Multicollinearity Detection
Ratio Likes/Followers
Unstandardized Coefficients Standardized 95.0% Confidence Interval for B Collinearity Statistics
B Std. Error Coefficients
Beta
t Sig. Lower Bound Upper Bound Tolerance VIF
(Constant) -11.726 10.067 -1.165 .245 -31.561 8.108
Platform_Dummy Instagram = 1 (Twitter = Basis = 0)
39.746 5.215 .442 7.622 .000 29.472 50.020 .936 1.069
Informative 3.820 6.616 .042 .577 .564 -9.216 16.855 .584 1.713
Persuasive -6.016 6.532 -.063 -.921 .358 -18.885 6.853 .663 1.509
Entertaining .888 6.777 .010 .131 .896 -12.465 14.240 .596 1.677
Product_Type_Dummy Hedonic = 1 (Utilitarian = Basis = 0)
9.824 6.921 .096 1.419 .157 -3.812 23.460 .686 1.458
Industry_Dummy Fashion
= 1 (Electronics & all other = Basis = 0)
1.806 7.475 .019 .242 .809 -12.922 16.535 .512 1.953
Industry_Dummy Travel
= 1 (Electronics & all others = Basis = 0)
24.387 6.823 .256 3.574 .000 10.944 37.830 .615 1.627
Dependent Variable: Ratio Likes X 10,000
Ratio Comments/Followers
Unstandardized Coefficients Standardized 95.0% Confidence Interval for B Collinearity Statistics
B Std. Error Coefficients
Beta
t Sig. Lower Bound Upper Bound Tolerance VIF
(Constant) -.009 .382 -.023 .981 -.761 .743
Platform_Dummy Instagram = 1 (Twitter = Basis = 0)
.922 .198 .293 4.663 .000 .532 1.311 .936 1.069
Informative .127 .251 .040 .507 .613 -.367 .621 .584 1.713
Persuasive .018 .248 .005 .073 .942 -.470 .506 .663 1.509
Entertaining -.409 .257 -.125 -1.594 .112 -.916 .097 .596 1.677
Product_Type_Dummy Hedonic = 1 (Utilitarian = Basis = 0)
.455 .262 .127 1.733 .084 -.062 .971 .686 1.458
Industry_Dummy Fashion
= 1 (Electronics & all other = Basis = 0)
-.645 .283 -.193 -2.276 .024 -1.203 -.087 .512 1.953
Industry_Dummy Travel
= 1 (Electronics & all others = Basis = 0)
.413 .259 .124 1.598 .111 -.096 .923 .615 1.627
Dependent Variable: Ratio Comments X 10,000
70 Engagement Rate (%)
Unstandardized Coefficients Standardized 95.0% Confidence Interval for B Collinearity Statistics
B Std. Error Coefficients
Beta
t Sig. Lower Bound Upper Bound Tolerance VIF
(Constant) -11173.513 1018.704 -1.152 .251 -3180.607 833.581
Platform_Dummy Instagram = 1 (Twitter = Basis = 0)
4066.763 527.679 .445 7.707 .000 3027.108 5106.419 .936 1.069
Informative 394.667 669.521 .043 .589 .556 -924.450 1713.785 .584 1.713
Persuasive -599.782 660.971 -.062 -.907 .365 -1902.056 702.492 .663 1.509
Entertaining 47.829 685.787 .005 .070 .944 -1303.338 1398.996 .596 1.677
Product_Type_Dummy Hedonic = 1 (Utilitarian = Basis = 0)
1027.873 700.346 .099 1.468 .144 -351.977 2407.724 .686 1.458
Industry_Dummy Fashion
= 1 (Electronics & all other = Basis = 0)
116.138 756.463 .012 .154 .878 -1374.277 1606.553 .512 1.953
Industry_Dummy Travel
= 1 (Electronics & all others = Basis = 0)
2480.039 690.449 .256 3.592 .000 1119.688 3840.390 .615 1.627
Dependent Variable: Engagement Rate X 10,000
Sentiment Score
Unstandardized Coefficients Standardized 95.0% Confidence Interval for B Collinearity Statistics
B Std. Error Coefficients
Beta
t Sig. Lower Bound Upper Bound Tolerance VIF
(Constant) .147 .065 2.243 .026 .018 .276
Platform_Dummy Instagram = 1 (Twitter = Basis = 0)
.195 .034 .321 5.760 .000 .128 .262 .936 1.069
Informative -.014 .043 -.022 -.316 .752 -.098 .071 .584 1.713
Persuasive -.058 .042 -.091 -1.376 .170 -.142 .025 .663 1.509
Entertaining .061 .044 .097 1.384 .168 -.026 .148 .596 1.677
Product_Type_Dummy Hedonic = 1 (Utilitarian = Basis = 0)
-.020 .045 -.029 -.446 .656 -.109 .069 .686 1.458
Industry_Dummy Fashion
= 1 (Electronics & all other = Basis = 0)
.364 .049 .564 7.486 .000 .268 .459 .512 1.953
Industry_Dummy Travel
= 1 (Electronics & all others = Basis = 0)
.137 .044 .213 3.094 .002 .050 .224 .615 1.627
Dependent Variable: Sentiment Score
71 Appendix 2 - Correlation Matrix
72 Appendix 3 – Main Effects Models
Model 1: All predictor variables and their main effects on the outcome variable Ratio Likes/Followers
Unstandardized Coefficients
Standardized
Coefficients 95.0% Confidence Interval for B
B Std. Error Beta t Sig. Lower Bound Upper Bound
(Constant) -11.726 10.067 -1.165 .245 -31.561 8.108
Platform_Dummy Instagram = 1 (Twitter = Basis = 0)
39.746 5.215 .442 7.622 .000 29.472 50.020
Informative 3.820 6.616 .042 .577 .564 -9.216 16.855
Persuasive -6.016 6.532 -.063 -.921 .358 -18.885 6.853
Entertaining .888 6.777 .010 .131 .896 -12.465 14.240
Product_Type_Dummy Hedonic = 1 (Utilitarian = Basis = 0)
9.824 6.921 .096 1.419 .157 -3.812 23.460
Industry_Dummy Fashion = 1 (Electronics & all other = Basis = 0)
1.806 7.475 .019 .242 .809 -12.922 16.535
Industry_Dummy Travel = 1 (Electronics & all other = Basis = 0)
24.387 6.823 .256 3.574 .000 10.944 37.830
Dependent Variable: Ratio Likes X 10,000
Note. R2 = .271 and F = 12.327***. Significance level: ***p < 0.001
Model 2: All predictor variables and their main effects on the outcome variable Ratio Comments/Followers
Unstandardized Coefficients
Standardized
Coefficients 95.0% Confidence Interval for B
B Std. Error Beta t Sig. Lower Bound Upper Bound
(Constant) -.009 .382 -.023 .981 -.761 .743
Platform_Dummy Instagram = 1 (Twitter = Basis = 0)
.127 .251 .040 .507 .613 -.367 .621
Informative .018 .248 .005 .073 .942 -.470 .506
Persuasive -.409 .257 -.125 -1.594 .112 -.916 .097
Entertaining .922 .198 .293 4.663 .000 .532 1.311
Product_Type_Dummy Hedonic = 1 (Utilitarian = Basis = 0)
-.645 .283 -.193 -2.276 .024 -1.203 -.087
Industry_Dummy Fashion = 1 (Electronics & all other = Basis = 0)
.413 .259 .124 1.598 .111 -.096 .923
Industry_Dummy Travel = 1 (Electronics & all other = Basis = 0)
.455 .262 .127 1.733 .084 -.062 .971
Dependent Variable: Ratio Comments X 10,000
Note. R2 = .144 and F = 5.581***. Significance level: ***p < 0.001
73 Model 3: All predictor variables and their main effects on the outcome variable Engagement Rate (%)
Unstandardized Coefficients
Standardized
Coefficients 95.0% Confidence Interval for B
B Std. Error Beta t Sig. Lower Bound Upper Bound
(Constant) -11173.513 1018.704 -1.152 .251 -3180.607 833.581
Platform_Dummy Instagram = 1 (Twitter = Basis = 0)
4066.763 527.679 .445 7.707 .000 3027.108 5106.419
Informative 394.667 669.521 .043 .589 .556 -924.450 1713.785
Persuasive -599.782 660.971 -.062 -.907 .365 -1902.056 702.492
Entertaining 47.829 685.787 .005 .070 .944 -1303.338 1398.996
Product_Type_Dummy Hedonic = 1 (Utilitarian = Basis = 0)
1027.873 700.346 .099 1.468 .144 -351.977 2407.724
Industry_Dummy Fashion = 1 (Electronics & all other = Basis = 0)
116.138 756.463 .012 .154 .878 -1374.277 1606.553
Industry_Dummy Travel = 1 (Electronics & all other = Basis = 0)
2480.039 690.449 .256 3.592 .000 1119.688 3840.390
Dependent Variable: Engagement Rate X 10,000
Note. R2 = .275 and F = 12.578***. Significance level: ***p < 0.001
Model 4: All predictor variables and their main effects on the outcome variable Sentiment Score
Unstandardized Coefficients
Standardized
Coefficients 95.0% Confidence Interval for B
B Std. Error Beta t Sig. Lower Bound Upper Bound
(Constant) .147 .065 2.243 .026 .01 .276
Platform_Dummy Instagram = 1 (Twitter = Basis = 0)
.195 .034 .321 5.760 .000 .128 .262
Informative -.014 .043 -.022 -.316 .752 -.098 .071
Persuasive -.058 .042 -.091 -1.376 .170 -.142 .025
Entertaining .061 .044 .097 1.384 .168 -.026 .148
Product_Type_Dummy Hedonic = 1 (Utilitarian = Basis = 0)
-.020 .045 -.029 -.446 .656 -.109 .069
Industry_Dummy Fashion = 1 (Electronics & all other = Basis = 0)
.364 .049 .564 7.486 .000 .268 .459
Industry_Dummy Travel = 1 (Electronics & all other = Basis = 0)
.137 .044 .213 3.094 .002 .050 .224
Dependent Variable: Sentiment Score
Note. R2 = .325 and F = 15.973***. Significance level: ***p < 0.001
74 Appendix 4 – Main Effects + Interaction Effects Models
Model 1: All predictor variables and interaction variables and their effects on the outcome variable Ratio Likes/Followers
Unstandardized Coefficients
Standardized
Coefficients 95.0% Confidence Interval for B
Model 1 B Std. Error Beta t Sig. Lower Bound Upper Bound
(Constant) -11.726 10.067 -1.165 .245 -31.561 8.108
Platform_Dummy Instagram = 1 (Twitter = Basis = 0)
39.746 5.215 .442 7.622 .000 29.472 50.020
Informative 3.820 6.616 .042 .577 .564 -9.216 16.855
Persuasive -6.016 6.532 -.063 -.921 .358 -18.885 6.853
Entertaining .888 6.777 .010 .131 .896 -12.465 14.240
Product_Type_Dummy Hedonic = 1 (Utilitarian = Basis = 0)
9.824 6.921 .096 1.419 .157 -3.812 23.460
Industry_Dummy Fashion = 1 (Electronics & all other = Basis = 0)
1.806 7.475 .019 .242 .809 -12.922 16.535
Industry_Dummy Travel = 1 (Electronics & all other = Basis = 0)
24.387 6.823 .256 3.574 .000 10.944 37.830
Model 2
(Constant) 4.095 16.524 .248 .805 -28.466 36.656
Platform_Dummy Instagram = 1 (Twitter = Basis = 0)
38.605 8.722 .429 4.426 .000 21.417 55.793
Informative 11.981 7.421 .133 1.615 .108 -2.642 26.604
Persuasive -8.906 7.520 -.094 -1.184 .238 -23.726 5.913
Entertaining -15.660 12.158 -.168 -1.288 .199 -39.617 8.298
Product_Type Dummy Hedonic = 1 (Utilitarian = Basis = 0)
-19.166 18.796 -.187 -1.020 .309 -56.204 17.872
Industry_Dummy Fashion = 1 (Electronics & all other = Basis = 0)
9.125 11.961 .096 .763 .446 -14.445 32.694
Industry_Dummy Travel = 1 (Electronics & all other = Basis = 0)
30.012 8.117 .314 3.697 .000 14.017 46.007
Platform (Instagram) X Type (Entertaining)
.770 10.760 .008 .072 .943 -20.434 21.974
Producttype (Hedonic) X Type (Entertaining)
24.919 15.429 .277 1.615 .108 -5.484 55.322
Producttype (Hedonic) X Type (Entertaining) X Industry (Fashion)
-1.989 13.312 -.017 -.149 .881 -28.221 24.242
Producttype (Utilitarian) X Type (Informative)
-20.808 16.120 -.174 -1.291 .198 -52.572 10.957
Producttype (Utilitarian) X Type (Informative) X Industry (Travel)
-14.756 15.238 -.079 -.968 .334 -44.783 15.271
Producttype (Utilitarian) X Type (Persuasive)
12.880 15.147 .083 .850 .396 -16.968 42.728
Producttype (Utilitarian) X Type (Persuasive) X Industry (Travel)
-14.170 43.204 -.020 -.328 .743 -99.306 70.966
Dependent Variable: Ratio Likes X 10,000
Note. R2 Model 1 = .271, R2 Model 2 = .298 and F Model 1 = 12.327***, F Model 2 = 6.816***. Significance level: ***p < 0.001
75 Model 2: All predictor variables and interaction variables and their effects on the outcome variable Ratio Comments/Followers
Unstandardized Coefficients
Standardized
Coefficients 95.0% Confidence Interval for B
Model 1 B Std. Error Beta t Sig. Lower Bound Upper Bound
(Constant) -.009 .382 -.023 .981 -.761 .743
Platform_Dummy Instagram = 1 (Twitter = Basis = 0)
.922 .198 .293 4.663 .000 .532 1.311
Informative .127 .251 .040 .507 .613 -.367 .621
Persuasive .018 .248 .005 .073 .942 -.470 .506
Entertaining -.409 .257 -.125 -1.594 .112 -.916 .097
Product_Type_Dummy Hedonic = 1 (Utilitarian = Basis = 0)
.455 .262 .127 1.733 .084 -.062 .971
Industry_Dummy Fashion = 1 (Electronics & all other = Basis = 0)
-.645 .283 -.193 -2.276 .024 -1.203 -.087
Industry_Dummy Travel = 1 (Electronics & all other = Basis = 0)
.413 .259 .124 1.598 .111 -.096 .923
Model 2
(Constant) -.249 .625 -.399 .690 -1.481 .982
Platform_Dummy Instagram = 1 (Twitter = Basis = 0)
1.269 .330 .403 3.848 .000 .619 1.919
Informative .188 .281 .059 .668 .505 -.365 .740
Persuasive -.107 .284 -.032 -.376 .707 -.667 .454
Entertaining .072 .460 .022 .156 .876 -.834 .978
Product_Type Dummy Hedonic = 1 (Utilitarian = Basis = 0)
.965 .711 .270 1.358 .176 -.435 2.366
Industry_Dummy Fashion = 1 (Electronics & all other = Basis = 0)
-1.181 .452 -.354 -2.611 .010 -2.072 -.290
Industry_Dummy Travel = 1 (Electronics & all other = Basis = 0)
.670 .307 .201 2.183 .030 .065 1.275
Platform (Instagram) X Type (Entertaining)
-.585 .407 -.177 -1.439 .152 -1.387 .216
Producttype (Hedonic) X Type (Entertaining)
-.939 .583 -.298 -1.610 .109 -2.089 .210
Producttype (Hedonic) X Type (Entertaining) X Industry (Fashion)
1.040 .503 .249 2.066 .040 .048 2.032
Producttype (Utilitarian) X Type (Informative)
-.002 .610 .000 -.003 .998 -1.203 1.119
Producttype (Utilitarian) X Type (Informative) X Industry (Travel)
-.767 .576 -.118 -1.332 .184 -1.903 .368
Producttype (Utilitarian) X Type (Persuasive)
.328 .573 .060 .573 .567 -.801 1.457
Producttype (Utilitarian) X Type (Persuasive) X Industry (Travel)
-.006 1.634 .000 -.004 .997 -3.225 3.214
Dependent Variable: Ratio Comments X 10,000
Note. R2 Model 1 = .144, R2 Model 2 = .179 and F Model 1 = 5.581***, F Model 2 = 3.510***. Significance level: ***p < 0.001
76 Model 3: All predictor variables and interaction variables and their effects on the outcome variable Engagement Rate (%)
Unstandardized Coefficients
Standardized
Coefficients 95.0% Confidence Interval for B
Model 1 B Std. Error Beta t Sig. Lower Bound Upper Bound
(Constant) -1173.513 1018.704 -1.152 .251 -3180.607 833.581
Platform_Dummy Instagram = 1 (Twitter = Basis = 0)
4066.763 527.679 .445 7.707 .000 3027.108 5106.419
Informative 394.667 669.521 .043 .589 .556 -924.450 1713.785
Persuasive -599.782 660.971 -.062 -.907 .365 -1902.056 702.492
Entertaining 47.829 685.787 .005 .070 .944 -1303.338 1398.996
Product_Type_Dummy Hedonic = 1 (Utilitarian = Basis = 0)
1027.873 700.346 .099 1.468 .144 -351.977 2407.724
Industry_Dummy Fashion = 1 (Electronics & all other = Basis = 0)
116.138 756.463 .012 .154 .878 -1374.277 1606.553
Industry_Dummy Travel = 1 (Electronics & all other = Basis = 0)
2480.039 690.449 .256 3.592 .000 1119.688 3840.390
Model 2
(Constant) 384.551 1672.139 .230 .818 -2910.504 3679.607
Platform_Dummy Instagram = 1 (Twitter = Basis = 0)
3987.462 882.679 .437 4.517 .000 2248.088 5726.836
Informative 1216.851 750.959 .133 1.620 .107 -262.961 2696.663
Persuasive -901.334 761.049 -.094 -1.184 .238 -2401.029 598.362
Entertaining -1558.773 1230.321 -.165 -1.267 .206 -3983.198 865.652
Product_Type Dummy Hedonic = 1 (Utilitarian = Basis = 0)
-1820.089 1902.073 -.175 -.957 .340 -5568.244 1928.066
Industry_Dummy Fashion = 1 (Electronics & all other = Basis = 0)
794.391 1210.399 .082 .656 .512 -1590.777 3179.559
Industry_Dummy Travel = 1 (Electronics & all other = Basis = 0)
3068.182 821.398 .317 3.735 .000 1449.565 4686.800
Platform (Instagram) X Type (Entertaining)
18.503 1088.912 .002 .017 .986 -2127.267 2164.273
Producttype (Hedonic) X Type (Entertaining)
2397.927 1561.332 .263 1.536 .126 -678.777 5474.631
Producttype (Hedonic) X Type (Entertaining) X Industry (Fashion)
-94.954 1347.085 -.008 -.070 .944 -2749.470 2559.561
Producttype (Utilitarian) X Type (Informative)
-2080.975 1631.249 -.172 -1.276 .203 -5295.455 1133.504
Producttype (Utilitarian) X Type (Informative) X Industry (Travel)
-1552.313 1542.025 -.082 -1.007 .315 -4590.971 1486.344
Producttype (Utilitarian) X Type (Persuasive)
1320.791 1532.814 .083 .862 .390 -1699.717 4341.299
Producttype (Utilitarian) X Type (Persuasive) X Industry (Travel)
-1417.571 4372.115 -.020 -.324 .746 -10033.100 7197.958
Dependent Variable: Engagement Rate X 10,000
Note. R2 Model 1 = .275, R2 Model 2 = .302 and F Model 1 = 12.578***, F Model 2 = 6.940***. Significance level: ***p < 0.001