• No results found

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.

63 Reference List

Ansari, S., Ansari, G., Ghori, M. U., & Kazi, A. G. (2019). Impact of Brand Awareness and Social Media Content Marketing on Consumer Purchase Decision. Journal of Public Value and Administration Insights, 2(2), 5–10. https://doi.org/10.31580/jpvai.v2i2.896 Arica, A. (2020, 17 November). BMW Approaches Content Marketing To Create Engagement

Between Consumer And Product. Digital Agency Network, available at:

https://digitalagencynetwork.com/bmw-approaches-content-marketing-to-create-engagement-between-consumer-and-product/ (accessed at 14 November 2021) Ayvaz, S., & Shiha, M. O. (2017). The Effects of Emoji in Sentiment Analysis. International

Journal of Computer and Electrical Engineering, 9(1), 360–369.

https://doi.org/10.17706/ijcee.2017.9.1.360-369

Baltes, L. (2015). Content marketing – the fundamental tool of digital marketing. Economic Sciences Vol. 8(57) No.2.

Barari, M., Ross, M., Thaichon, S., & Surachartkumtonkun, J. (2020). A meta‐analysis of customer engagement behaviour. International Journal of Consumer Studies, 45(4), 457–477. https://doi.org/10.1111/ijcs.12609

Bowden, J., & Mirzaei, A. (2021). Consumer engagement within retail communication channels: an examination of online brand communities and digital content marketing initiatives. European Journal of Marketing, 55(5), 1411–1439.

https://doi.org/10.1108/ejm-01-2018-0007

Cambridge Dictionary. (2021). Product category definition: a particular group of related products: Learn more. https://dictionary.cambridge.org/dictionary/english/product-category (accessed at 30 June 2021)

Caselli, T., Sprugnoli, R., & Moretti, G. (2021). Identifying communicative functions in discourse with content types. Language Resources and Evaluation. Published.

https://doi.org/10.1007/s10579-021-09554-4

Coelho, R. L. F., Oliveira, D. S. D., & Almeida, M. I. S. D. (2016). Does social media matter for post typology? Impact of post content on Facebook and Instagram metrics. Online Information Review, 40(4), 458–471. https://doi.org/10.1108/oir-06-2015-0176 Chang, K. T. T., Chen, W., & Tan, B. C. Y. (2012). Advertising Effectiveness in Social

Networking Sites: Social Ties, Expertise, and Product Type. IEEE Transactions on Engineering Management, 59(4), 634–643. https://doi.org/10.1109/tem.2011.2177665

64 Chemela, M.S.R. (2019, 20 February). The relation between content typology and consumer

engagement in Instagram. Universidade Catolica Porguguesa.

https://repositorio.ucp.pt/handle/10400.14/26921 (accessed at 30 June 2021) Daoud, J.I. (2017). Multicollinearity and Regression Analysis. Journal of Physics:

Conference Series, 949 012009

Dawson, J. F. (2013). Moderation in Management Research: What, Why, When, and How.

Journal of Business and Psychology, 29(1), 1–19. https://doi.org/10.1007/s10869-013-9308-7

Dataminer. (2021). Scrape data from any website with 1 Click. Available at:

https://dataminer.io/ (accessed at 13 December 2021)

Dessart, L., Veloutsou, C., & Morgan-Thomas, A. (2016). Capturing consumer engagement:

duality, dimensionality and measurement. Journal of Marketing Management, 32(5–

6), 399–426. https://doi.org/10.1080/0267257x.2015.1130738

Djafarova, E., & Bowes, T. (2021). ‘Instagram made Me buy it’: Generation Z impulse purchases in fashion category. Journal of Retailing and Consumer Services, 59, 102345. https://doi.org/10.1016/j.jretconser.2020.102345

DoubleVerify. (2020, 23 September). Global Online Content Consumption Doubles in 2020, Research Shows, available at:

https://www.globenewswire.com/news- release/2020/09/23/2097872/0/en/Global-Online-Content-Consumption-Doubles-in-2020-Research-Shows.html (accessed at 19 June 2020)

Du Plessis, C. (2017b). The role of content marketing in social media content communities.

SA Journal of Information Management, 19(1).

https://doi.org/10.4102/sajim.v19i1.866

Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain, V., Karjaluoto, H., Kefi, H., Krishen, A. S., Kumar, V., Rahman, M. M., Raman, R., Rauschnabel, P. A., hudder, J., Salo, J., Tran, G. A., & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168.

https://doi.org/10.1016/j.ijinfomgt.2020.102168

Fang, X., & Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big Data, 2(1). https://doi.org/10.1186/s40537-015-0015-2

Febrian, A., Ahadiat, A., & Bangsawan, S. (2021). Digital Content Marketing Strategy in Increasing Customer Engagement in Covid-19 Situation. International Journal of Pharmaceutical Research, 13(01). https://doi.org/10.31838/ijpr/2021.13.01.684

65 Garcia J.E., Pereira J.S., Cairrão Á. (2021) Social Media Content Marketing Strategy for

Higher Education: A Case Study Approach. In: Rocha Á., Reis J.L., Peter M.K., Cayolla R., Loureiro S., Bogdanović Z. (eds) Marketing and Smart Technologies.

Smart Innovation, Systems and Technologies, vol 205. Springer, Singapore.

https://doi.org/10.1007/978-981-33-4183-8_39

Hollebeek, L. D., & Macky, K. (2019). Digital Content Marketing’s Role in Fostering

Consumer Engagement, Trust, and Value: Framework, Fundamental Propositions, and Implications. Journal of Interactive Marketing, 45, 27–41.

https://doi.org/10.1016/j.intmar.2018.07.003

Holliman, G., & Rowley, J. (2014). Business to business digital content marketing: marketers’

perceptions of best practice. Journal of Research in Interactive Marketing, 8(4), 269–

293. https://doi.org/10.1108/jrim-02-2014-0013

Hudders, L., Van Reijmersdal, E. A., & Poels, K. (2019). Editorial: Digital advertising and consumer empowerment. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 13(2). https://doi.org/10.5817/cp2019-2-xx

Hsiao, C. C. (2020). Understanding content sharing on the internet: test of a cognitive-affective-conative model. Online Information Review, 44(7), 1289–1306.

https://doi.org/10.1108/oir-11-2019-0350

Injadat, M., Salo, F., & Nassif, A. B. (2016). Data mining techniques in social media: A survey. Neurocomputing, 214, 654–670. https://doi.org/10.1016/j.neucom.2016.06.045 Kim, M., Lee, J. K., & Lee, K. Y. (2019). Interplay of content type and product type in the

consumer response to native advertising on social media. Asian Journal of

Communication, 29(6), 464–482. https://doi.org/10.1080/01292986.2019.1679852 Kırcova, B., Yaman, Y., & Gizem Köse, I. (2018). Instagram, Facebook or Twitter: Which

Engages Best? A Comparative Study of Consumer Brand Engagement and Social Commerce Purchase Intention. European Journal of Economics and Business Studies, 4(1), 268–278. https://doi.org/10.2478/ejes-2018-0031

Koiso-Kanttila, N. (2004). Digital Content Marketing: A Literature Synthesis. Journal of Marketing Management, 20(1–2), 45–65.

https://doi.org/10.1362/026725704773041122

Kotler, P., & Kartajaya, H. (2017). Marketing 4.0 (1st edition). Wiley.

Krippendorff, K. (2018). Content Analysis (4th edition). SAGE Publications.

66 Kuvykaitė, R., & Tarutė, A. (2015). A Critical Analysis of Consumer Engagement

Dimensionality. Procedia - Social and Behavioural Sciences, 213, 654–658.

https://doi.org/10.1016/j.sbspro.2015.11.468

Lee, D., Hosanagar, K., & Nair, H. S. (2018). Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook. Management Science, 64(11), 5105–5131. https://doi.org/10.1287/mnsc.2017.2902

Li, J., Abbasi, A., Cheema, A., & Abraham, L. B. (2020). Path to Purpose? How Online Customer Journeys Differ for Hedonic Versus Utilitarian Purchases. Journal of Marketing, 84(4), 127–146. https://doi.org/10.1177/0022242920911628

Lou, C., & Xie, Q. (2020). Something social, something entertaining? How digital content marketing augments consumer experience and brand loyalty. International Journal of Advertising, 40(3), 376–402. https://doi.org/10.1080/02650487.2020.1788311

Lyman, G. H., & Kuderer, N. M. (2005). The strengths and limitations of meta-analyses based on aggregate data. BMC Medical Research Methodology, 5(1).

https://doi.org/10.1186/1471-2288-5-14

Montgomery, D. C., & Peck, E. A. (2021). Introduction to Linear Regression Analysis (6th Edition). Wiley.

Moz. (2019). Mozlow’s hierarchy of SEO needs. Retrieved from https://moz.com/beginners-guide-to-seo (accessed on 14 June 2021)

Muñoz-Expósito, M., Oviedo-García, M. N., & Castellanos-Verdugo, M. (2017). How to measure engagement in Twitter: advancing a metric. Internet Research, 27(5), 1122–

1148. https://doi.org/10.1108/intr-06-2016-0170

Nayak, A. S. (2020). Management Research Methods for Business Administration [Lecture slides]. University of Amsterdam. Available at: https://canvas.uva.nl/6612ZB016Y Pelletier, M. J., Krallman, A., Adams, F. G., & Hancock, T. (2020). One size doesn’t fit all: a

uses and gratifications analysis of social media platforms. Journal of Research in Interactive Marketing, 14(2), 269–284. https://doi.org/10.1108/jrim-10-2019-0159 Pöyry, E., Parvinen, P., & Malmivaara, T. (2013). Can we get from liking to buying?

Behavioural differences in hedonic and utilitarian Facebook usage. Electronic Commerce Research and Applications, 12(4), 224–235.

https://doi.org/10.1016/j.elerap.2013.01.003

Rasool, A., Shah, F. A., & Islam, J. U. (2020). Customer engagement in the digital age: a review and research agenda. Current Opinion in Psychology, 36, 96–100.

https://doi.org/10.1016/j.copsyc.2020.05.003

67 Rowley, J. (2008). Understanding digital content marketing. Journal of Marketing

Management, 24(5–6), 517–540. https://doi.org/10.1362/026725708x325977

Shahbaznezhad, H., Dolan, R., & Rashidirad, M. (2021). The Role of Social Media Content Format and Platform in Users’ Engagement Behaviour. Journal of Interactive Marketing, 53, 47–65. https://doi.org/10.1016/j.intmar.2020.05.001

Silva, M. J. D. B., Farias, S. A. D., Grigg, M. K., & Barbosa, M. D. L. D. A. (2019). Online Engagement and the Role of Digital Influencers in Product Endorsement on

Instagram. Journal of Relationship Marketing, 19(2), 133–163.

https://doi.org/10.1080/15332667.2019.1664872

Singh, A., & Mathur, S. (2019). The Insight of Content Marketing at Social Media Platforms.

A Journal of Management Sciences, Volume 9, Issue 2.

https.doi.org/10.21567/adhyayan.v9i2.4

Smith, K. (2019). 126 Amazing Social Media Statistics and Facts, available at:

https://www.brandwatch.com/blog/amazing-social-media-statistics-and-facts/

(accessed at 18 June 2021)

Smith, K., Blazovich, J.L., & Smith, L.M. (2015). Social Media Adoption by Corporations:

An Examination by Platform, Category, Size, and Financial Performance. Academy of Marketing Studies, Journal, 19(2).

Statista. (2020). Global digital population as of January 2020. Available at:

https://www.statista.com/statistics/617136/digital-population-worldwide/. (accessed at 6 January 2022)

Statista. (2020). Number of social network users worldwide from 2010 to 2023. Available at:

https://www.statista.com/statistics/278414/number-of-worldwide-social-network users/. (accessed at 6 January 2022)

Stephen, A. (2016). The role of digital and social media marketing in consumer behaviour.

Current Opinion in Psychology, 10, 17-21.

Stevens, J. L., Spaid, B. I., Breazeale, M., & Esmark Jones, C. L. (2018). Timeliness, transparency, and trust: A framework for managing online customer complaints.

Business Horizons, 61(3), 375–384. https://doi.org/10.1016/j.bushor.2018.01.007 Tellis, G. J., MacInnis, D. J., Tirunillai, S., & Zhang, Y. (2019). What Drives Virality

(Sharing) of Online Digital Content? The Critical Role of Information, Emotion, and Brand Prominence. Journal of Marketing, 83(4), 1–20.

https://doi.org/10.1177/0022242919841034

68 UCLA Institute for Digital Research & Education Statistical Consulting. (2021). What is the

difference between categorical, ordinal and interval variables?, available at:

https://stats.idre.ucla.edu/other/mult-pkg/whatstat/what-is-the-difference-between-categorical-ordinal-and-interval-variables/ (accessed at 14 December 2021) Van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C.

(2010). Customer Engagement Behaviour: Theoretical Foundations and Research Directions. Journal of Service Research, 13(3), 253–266.

https://doi.org/10.1177/1094670510375599

Van den Broucke, S., & Baesens, B. (2018). Practical Web Scraping for Data Science.

(accessed at 11 October 2021)

Vohra, A., & Bhardwaj, N. (2016, 1 January). A Conceptual Presentation of Customer Engagement in the context of Social Media – An Emerging Market. . . ResearchGate.

https://www.researchgate.net/publication/301231912_A_Conceptual_Presentation_of_

Customer_Engagement_in_the_context_of_Social_Media_-_An_Emerging_Market_Perspective

Voorveld, H. A. M., & Viswanathan, V. (2015). An Observational Study on How Situational Factors Influence Media Multitasking With TV: The Role of Genres, Dayparts, and Social Viewing. Media Psychology, 18(4), 499–526.

https://doi.org/10.1080/15213269.2013.872038

Voorveld, H. A. M., van Noort, G., Muntinga, D. G., & Bronner, F. (2018). Engagement with Social Media and Social Media Advertising: The Differentiating Role of Platform Type. Journal of Advertising, 47(1), 38–54.

https://doi.org/10.1080/00913367.2017.1405754

Williams, D. L., Crittenden, V. L., Keo, T., & McCarty, P. (2012). The use of social media:

an exploratory study of usage among digital natives. Journal of Public Affairs, 12(2), 127–136. https://doi.org/10.1002/pa.1414

Żyminkowska, K. (2019). Hedonic and Utilitarian Drivers of Customer Engagement. Central European Business Review, 7(4), 15–33. https://doi.org/10.18267/j.cebr.204

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