content marketing research paper

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Content marketing: A review of academic literature and future research directions

• Songming Feng , Mart Ots
• Published 2015

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Content marketing tools and metrics in consulting firms: preliminary results, social media marketing as a supporting tool for brand building and its impact on customer purchase decision, the study of content marketing in b2c context, modelling the enablers for branded content as a strategic marketing tool in the covid-19 era, narrative visualizations: using interactive data stories in strategic brand communication, live streaming: a new platform for esl learning, the use of live streaming in marketing, related papers.

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Internet marketing: a content analysis of the research

• Special theme
• Open access
• Published: 31 January 2013
• Volume 23 , pages 177–204, ( 2013 )

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• J. Ken Corley II 1 ,
• Zack Jourdan 2 &
• W. Rhea Ingram 2  

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The amount of research related to Internet marketing has grown rapidly since the dawn of the Internet Age. A review of the literature base will help identify the topics that have been explored as well as identify topics for further research. This research project collects, synthesizes, and analyses both the research strategies (i.e., methodologies) and content (e.g., topics, focus, categories) of the current literature, and then discusses an agenda for future research efforts. We analyzed 411 articles published over the past eighteen years (1994-present) in thirty top Information Systems (IS) journals and 22 articles in the top 5 Marketing journals. The results indicate an increasing level of activity during the 18-year period, a biased distribution of Internet marketing articles focused on exploratory methodologies, and several research strategies that were either underrepresented or absent from the pool of Internet marketing research. We also identified several subject areas that need further exploration. The compilation of the methodologies used and Internet marketing topics being studied can serve to motivate researchers to strengthen current research and explore new areas of this research.

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Introduction

In the early years of the Internet Age, the potential of using the Internet as a distribution channel excited business managers who believed this tool would boost sales and increase organizational performance (Hansen 1995 ; Westland and Au 1997 ). These believers suspected an online presence could offer advantages to their customers, while providing a shopping experience similar to the traditional bricks-and-mortar store (Jarvenpaa and Todd 1996 ). The advantages included providing around the clock access for customers, reducing geographic boundaries to provide access to new markets, and enabling immediate communication with customers.

The prediction of an explosion of online shopping became a marriage between information technology experts and marketing professionals. Most would believe the information technology researchers were studying the Internet technology and its advantages, while the marketers were focused on the consumer’s use of the technology. As technology advanced, more marketing activities emerged to market goods and services via the Internet. Today, Internet marketing is defined as “the use of the Internet as a virtual storefront where products are sold directly to the customer” (Kiang et al. 2000 , p. 383), or another view includes “the strategic process of creating, distributing, promoting, and pricing products for targeted customers in the virtual environment of the Internet” (Pride et al. 2007 ). This research attempts to categorize the various Internet marketing activities in a broad context including strategies such as customer relationship management (Hwang 2009 ), electronic marketplaces (Novak and Schwabe 2009 ), online auctions (Loebbecke et al. 2010 ), and electronic branding (Otim and Grover 2010 ) in tandem with unique IS issues including web site evaluation (Chiou et al. 2010 ), piracy (Smith and Telang 2009 ), security (Ransbotham and Mitra 2009 ), and technology architecture (Du et al. 2008 ).

With concepts as varied as this in one research domain, a periodic review is necessary to discover and explore new technologies such as mobile banking (Sripalawat et al. 2011 ), virtual worlds (Sutanto et al. 2011 ), and social media (de Valck et al. 2009 ) as they emerge on the Internet marketing landscape. The following sections of the paper will examine the current literature to determine what is known about the concept of Internet marketing. First, a description of the methodology for the analysis of the Internet marketing research is presented. This is followed by the results including an analysis of a smaller sample of the Internet marketing research in the top Marketing journals. Finally, the research is summarized with a discussion of the limitations of this project and suggestions for future research.

Methodology

The approach to this analysis of the Internet marketing research is to first identify trends in the Information System (IS) literature. Specifically, we wished to capture the trends pertaining to (1) the number and distribution of Internet marketing articles published in the leading journals, (2) methodologies employed in Internet marketing research, and (3) the research topics being published in this area of research. During the analysis of the literature, we attempted to identify gaps and needs in the research and therefore discuss a research agenda which allows for the progression of research (Webster and Watson 2002 ). In short, we hope to paint a representative landscape of the current Internet marketing literature base in IS in order to influence the direction of future research efforts in this important area of study.

In order to examine the current state of research on Internet marketing, the authors conducted a literature review and analysis in three phases: Phase 1 accumulated a representative pool of articles; Phase 2 classified the articles by research method; and, Phase 3 classified the research by research topic. Each of the three phases is discussed in the following paragraphs.

Phase 1: accumulation of article pool

We used the Thomson Reuters Web of Science (WoS) citation database and Google Scholar to search for research articles with a focus on Internet marketing. The search parameters were constrained based on (a) a list of top ranked journals, (b) a specific time range, and (c) key search terms.

First, the researchers chose to use the top 30 journals from Peffers and Tang’s ( 2003 ) IS journals ranking (see Table  1 ). Peffers and Tang’s ( 2003 ) ranking of ‘pure’ IS journals was adopted for this study because it was based on the responses of IS researchers who were asked to rank journals by their “relative value to the researcher and the audience as an outlet for IS research.” In Peffers and Tang’s ( 2003 ) original ranking scheme two journals, ‘Communications of the Association of Information Systems’ and ‘Information and Management,’ tied for fifth place. Peffers and Tang resolved this issue by ranking both journals in the fifth position skipping the rank of the sixth position. As noted in Table  1 , 7 of the top 30 journals were not listed in the WoS database. Consequently, all 30 journals were searched using Google Scholar and only 23 journals were searched using the WoS database. The search parameters were further constrained to a specific timeframe.

Electronic commerce and Internet marketing did not exist prior to the widespread adoption and dissemination of the public Internet and the Worldwide Web (WWW). Therefore, the search parameters were further constrained based on the historical timeframe in which technologies capable of facilitating the development of e-commerce were first introduced. The graphical user interface based browser known as Netscape Navigator was launched as a free download for public use in 1994. Many experts identify the launch of Netscape Navigator as the historical event leading to the global public’s widespread adoption and use of the Internet and the World Wide Web (WWW) (Friedman 2006 ). Therefore, the search parameters for both WoS and Google Scholar were constrained to time period of 1994 through August of 2011.

The final constraint was based on the key search term “Internet Marketing.” In both WoS and Google Scholar the search engine scanned for the term ‘Internet Marketing’ and close variations of this term found in the title, abstract, and keywords of articles published in the top 30 IS journals between January of 1994 and August of 2011 when the search was executed. There was considerable overlap in the pool of articles returned from the two search engines (WoS and Google Scholar). Once duplicate entries and non-research articles (book reviews, editorials, commentary, etc.) were removed 453 articles remained in the composite data pool. The researchers then reviewed each article and identified 42 articles that were unrelated to the topic of Internet marketing. These 42 articles represented false positives returned from the WoS and Google Scholar search engines and were subsequently removed leaving 411 articles in the final composite article data pool for analysis.

Phase 2: classification by research strategy

Once the researchers identified the articles for the final data pool, each article was examined and categorized according to its research strategy. Due to the subjective nature of research strategy classification, content analysis methods were used for the categorization process. Figure  1 illustrates steps in the content analysis process adapted from Neuendorf ( 2002 ) and successfully employed by several similar research studies (Corley et al. 2011 ; Cumbie et al. 2005 ; Jourdan et al. 2008 ). First, the research categories were adopted from Scandura and Williams ( 2000 ) (see Table  2 ), who extended the research strategies initially described by McGrath ( 1982 ). Specifically, nine categories of research strategies were selected including: Formal theory/literature reviews, sample survey, laboratory experiment, experimental simulation, field study (primary data), field study (secondary data), field experiment, judgment task, and computer simulation.

Overview of literature analysis

Second, to guard against the threats to reliability (Neuendorf 2002 ), we performed a pilot test on articles meeting the search parameters from other top journals. That is, the articles used in the pilot test (a) were not part of the data set generated in Phase 1, and (b) the data generated from the pilot test were not included in the final data analysis for this study. Researchers independently categorized the articles in the pilot test based on the best fit among the nine research strategies. After all articles in the pilot test were categorized, the researchers compared their analyses. In instances where the independent categorizations did not match the researchers re-evaluated the article collaboratively by reviewing the research strategy definitions, discussing the disagreement thoroughly, and collaboratively assigning the article to a single category. This process allowed the researchers to develop a collaborative interpretation of the research strategy definitions. Simply stated, this pilot test served as a training session for accurately categorizing the articles for this study with respect to research strategy.

Each research strategy is defined by a specific design approach and each is also associated with certain tradeoffs that researchers must make when designing a study. These tradeoffs are inherent flaws that limit the conclusions that can be drawn from a particular research strategy. These tradeoffs refer to three aspects of a study that can vary depending on the research strategy employed. These variable aspects include: generalizability from the sample to the target population (external validity); precision in measurement and control of behavioural variables (internal and construct validity); and the issue of realism of context (Scandura and Williams 2000 ).

Cook and Campbell ( 1976 ) stated that a study has generalizability when the study has external validity across times, settings, and individuals. Formal theory/literature reviews and sample surveys have a high degree of generalizability by establishing the relationship between two constructs and illustrating that this relationship has external validity. A research strategy that has low external validity but high internal validity is the laboratory experiment. In the laboratory experiment, where the degree of measurement precision is high, cause and effect relationships may be determined, but these relationships may not be generalizable for other times, settings, and populations. While the formal theory/literature reviews and sample surveys have a high degree of generalizability and the laboratory experiment has a high degree of precision of measurement, these strategies have low degree of contextual realism. The only two strategies that maximize degree of contextual realism are field studies that use either primary or secondary data because the data is collected in an organizational setting (Scandura and Williams 2000 ).

The other four strategies maximize neither generalizability, nor degree of precision in measurement, nor degree of contextual realism. This point illustrates the futility of using only one strategy when conducting Internet marketing research. Because no single strategy can maximize all types of validity, it is best for researchers to use a variety of research strategies. Table  2 contains an overview of the nine strategies and their ranking on the three strategy tradeoffs (Scandura and Williams 2000 ).

Two coders independently reviewed and classified each article according to research strategy. Only a few articles were reviewed at one sitting to minimize coder fatigue and thus protect intercoder reliability (Neuendorf 2002 ). Upon completion of the independent classification, a tabulation of agreements and disagreements were computed, intercoder crude agreement (percent of agreement) was 91.8 % percent, and intercoder reliability using Cohen’s Kappa (Cohen 1960 ) was calculated ( k  = 0.847). These two calculations were well within the acceptable ranges for intercoder crude agreement and intercoder reliability (Neuendorf 2002 ). The reliability measures were calculated prior to discussing disagreements as mandated by Weber ( 1990 ). If the original reviewers did not agree on how a particular article was coded, an additional reviewer arbitrated the discussion of how the disputed article was to be coded. This process resolved the disputes in all cases.

Phase 3: categorization by internet marketing research topic

Typically the process of categorizing research articles by a specific research topic involves an iterative cycle of brainstorming and discussion sessions among the researchers. This iterative process helps to identify common themes within the data pool of articles. Through the collaborative discussions during this process researchers can synthesize a hierarchical structure within the literature of overarching research topics and more granular level subtopics. The final outcome is a better understanding of the current state of a particular stream of research. This iterative process was modified for this specific study on the topic of Internet marketing.

During the initial stages of the current project the researchers began investigating tentative outlets for publishing a literature review on the topic of Internet marketing. A special call for papers (CFP) on the topic of Internet marketing from the journal ‘Electronic Marketing’ was identified as a potential target journal by one of the authors. Further investigation revealed that the editors had outlined six specific research topic categories for the special CFP including: Business Models of Online Marketing, The Future of Search Strategies, The Internet Advertising Landscape, Commercial Exploitation of Web 2.0 in Consumer Marketing and in an Organizational Context, Evaluation of Online Performance, and Other Topics. Each of these six research topics was accompanied by a general definition and a few examples. The researchers adopted these six research topics to categorize the articles in the data pool.

A second pilot study was performed mirroring the first pilot test as a means of training for categorizing articles by research topic. Researchers independently categorized the articles in the pilot test based on the best fit among the six research topics. After all articles in the pilot test were categorized, the researchers compared their analyses. In instances where the independent categorizations did not match, the researchers re-evaluated the article collaboratively by reviewing the research category definitions, discussing the disagreement thoroughly, and collaboratively assigning the article to a single category. This process allowed the researchers to develop a collaborative interpretation of the research topic definitions (see Table  3 ).

Once we established the category definitions, we independently placed each article in one Internet marketing category. As before, we categorized only a few articles at a time to minimize coder fatigue and thus protect intercoder reliability (Neuendorf 2002 ). Upon completion of the classification process, we tabulated agreements and disagreements, intercoder crude agreement (percent of agreement) was 86.2 %, and intercoder reliability using Cohen’s Kappa (Cohen 1960 ) for each category was calculated ( k  = .08137). Again, the latter two calculations were well within the acceptable ranges (Neuendorf 2002 ). We again calculated the reliability measures prior to discussing disagreements as mandated by Weber ( 1990 ). If the original reviewers did not agree on how a particular article was coded, a third reviewer arbitrated the discussion of how the disputed article was to be coded. This process also resolved the disputes in all cases.

In order to identify gaps and needs in the research (Webster and Watson 2002 ), we hope to paint a representative landscape of the current Internet marketing literature base in order to influence the direction of future research efforts in this important area of study. In order to examine the current state of this research, the authors conducted a literature review and analysis in three phases. Phase 1 accumulated a representative pool of Internet marketing articles, and the articles were then analyzed with respect to year of publication and journal. Phase 2 contains a short discussion of the research strategies set forth by Scandura and Williams ( 2000 ) and the results of the classification of the articles by those research strategies. Phase 3 involved the creation and use of six Internet marketing research topics, a short discussion of each topic, and the results of the classification of each article within the research topics. These results are discussed in the following paragraphs.

Results of phase 1

Using the described search criteria within the selected journals, we collected a total of 411 articles (For the complete list of articles in our sample, see Appendix A .) In phase 1, we further analyzed the articles’ year of publication and journal. Figure  2 shows the number of articles per year in our sample. Please note that 2011 only represents articles acquired using WoS and Google Scholar search engines which were available at the time (August 2011) the search was conducted. There is a general increasing trend over the 18 year period, but no articles were found to be published in 1994 & 1996. The year 2010 shows the most activity with 52 articles (12.7 %). With Internet marketing issues becoming ever more important to researchers and practitioners, this comes as no surprise. Understanding 2011 was only a partial year in our sample, we were not concerned by the difference in quantity of publications over time.

Number of Internet Marketing Articles Published Per Year

In order to identify the research strategies used by Internet marketing research articles in the top 30 Information Systems (IS) journals in our sample, Table  4 was created to show the number of Internet marketing articles in each journal broken down by research strategy. This table illustrates the high level of Internet marketing publications that use the Formal Theory/Literature Review, Sample Survey, Field Study – Primary, and Field Study – Secondary research strategies. This indicates a body of research that is still in the exploratory stages. This table also illustrates the proclivity of some journals to accept certain research strategies over others. For example, the journals Decision Support Systems , International Journal of Electronic Commerce , and Journal of Management Information Systems had articles in this data set using seven of the nine research strategies. With this information, researchers that favour certain research strategies can target their research papers to journals that favour these strategies.

Number of Internet Marketing Articles Published in Each Research Strategy Category

Results of phase 2

The results of the categorization of the 411 articles according to the nine research strategies described by Scandura and Williams ( 2000 ) are summarized in Fig.  3 and Table  5 . Of the 411 articles, 110 articles (26.8 %) were classified as Formal Theory/Literature review making it the most prevalent research strategy. This was followed by Sample Survey with 94 articles (or 22.9 %), Field Study – Secondary Data with 91 articles (22.1 %), Field Study – Primary Data with 66 articles (16.1 %), and Computer Simulation with 25 articles (6.1 %). These five research strategies composed 94 % of the articles in the sample. No articles were classified as a Judgment Task. So, the remaining three research strategies represented the remaining six percent of the sample which included Lab Experiment with 11 articles (2.7 %), Field Experiment with 11 articles (2.7 %), and Experimental Simulation with 3 articles (0.7 %).

Further analysis showing the research strategies over the 18 year period from 1994 to August 2011 (Table  6 ) illustrates that Formal Theory/Literature Review, Sample Survey, Field Study – Secondary Data, and Field Study – Primary Data are represented in almost every year of the timeframe. No articles were found in the years 1994 & 1996, and only one article was found in 1995. These four strategies are exploratory in nature and indicate the beginnings of a body of research (Scandura and Williams 2000 ). Further categorization and analysis of the articles with respect to Internet marketing topic categories was conducted in the third phase of this research project.

Results of phase 3

Table  7 shows the number of articles per Internet marketing research topic category. These six categories provided a topic area classification for all of the 411 articles in our research sample. Of the 411 articles, 41.1 % were classified as ‘Business Models of Online Marketing’ making it the most prevalent Internet marketing topic category. This category was followed by ‘The Internet Advertising Landscape’ (22.4 %), ‘Evaluation of Online Performance’ (16.5 %), and ‘Other’ (10.0 %). These four research strategies accounted for 90 % of the articles in the sample. The topic categories titled ‘Commercial Exploitation of Web 2.0 in Consumer Marketing and in an Organizational Context’ and ‘The Future of Search Strategies’ represented the remaining six per cent (5.8 %) and four percent (4.1 %) of the articles. This illustration of the share of Internet marketing research that is represented by each category reveals the amount of attention topic categories of Internet marketing research have historically received among the top 30 IS journals.

By plotting Internet marketing research topics against research strategies (Table  8 ), many of the gaps in Internet marketing research are exposed. The gaps are at the intersection of less used methodologies (Judgement Task, Experimental Simulation, Lab Experiment) and less studied domains in Internet marketing (Search Strategies and Web 2.0). We believe these gaps exist for two reasons. First, some of these research strategies are not prevalent in IS research, and some top IS journals do not accept papers that use unusual research strategies. So, researchers avoid unorthodox strategies. The reason some of these categories have not been studied is because they represent relatively new phenomena, and the research has not caught up with the business reality. The great news for researchers interested in Internet marketing is that this domain should provide research opportunities for years to come. To better illustrate the categorization process, Table  9 presents a sample of articles noting their corresponding research strategy and research topic. These articles were randomly selected as typical examples and are not meant to serve as hallmarks of a particular research strategy or research topic within Internet marketing research.

About half (49 %) of the journal articles in this study use the Formal Theory/Literature Review and Sample Survey research strategies indicating the exploratory nature of the current research. We speculate the strategies used to study these topics were prevalent for several reasons. First, these strategies are the most appropriate for the early stages of research. In these exploratory years of Internet marketing research, formal theory/literature reviews are appropriate in order to determine what other strategies are being used in the research, define the topics under investigation, and find research in reference disciplines that are conducting similar research. Second, many researchers in business schools may prefer to administer sample surveys and field studies instead of laboratory experiment, experimental simulation, judgment task, and computer simulation because of the preferences for certain research strategies in the top journals in Information Systems and Marketing. Finally, organizations are less likely to commit to certain strategies (i.e. primary & secondary field studies and field experiments) because these strategies are more expensive for the organizations. These types of research strategies are very labour intensive to the organization being studied because records will need to be examined, personnel will need to be interviewed, and senior managers will be required to devote large amounts of their expensive time to help facilitate the research project. It is interesting to note that many of the articles coded as Field Study – Secondary and Computer Simulation used historical auction and pricing data freely available from the World Wide Web to avoid this issue.

Investigating the marketing literature

In order to investigate the Internet marketing research being conducted in the top Marketing Journals, we also performed a smaller literature review using the top five ranked marketing research journals following the same methodology previously described for the top 30 ranked IS journals. This list was compiled from three recent marketing journal rankings (Hofacker et al. 2009 ; Moussa and Touzani 2010 ; and Polonsky and Whitelaw 2006 ). The data pool included 24 articles, and after screening out irrelevant articles (book reviews, opinion pieces, etc.) the remaining 22 articles were categorized by research strategy and research topic (see Appendix B ). Upon completion of the categorization process, we tabulated agreements and disagreements. Intercoder crude agreement (percent of agreement) was 95.4 % for research strategy and 90.9 % for research topic. Cohen’s Kappa could not be calculated because the sample size was too small. These two calculations were well within the acceptable ranges (Neuendorf 2002 ). The results of the literature review of the top five marketing journals are displayed in Tables  10 and 11 .

The number of articles published on the topic of Internet marketing in each of the top five ranked marketing journals is presented in Table  10 . It is interesting to note that no articles were found in Journal of Consumer Research while 16 of the 22 (72.7 %) articles in the data pool were published in Marketing Science . This could indicate (a) Marketing Science is a top outlet for Internet marketing research or (b) the other Marketing journals use keywords other than “Internet marketing” to classify this area of research. The number of articles categorized based on both research strategy and research topic is presented in Table  11 . The three research strategies with the largest number of articles among the top five marketing journals were “Formal Theory / Lit Review” (45.5 %), “Field Study - Secondary” (27.3 %), and “Field Study – Primary” (18.2 %). This indicates, like the research published in the top IS journals, the Internet marketing research published in the top marketing journals is also still in the exploratory stages.

Fourteen of the twenty-two articles (63.6 %) were categorized within the research topic labelled “the Internet Advertising Landscape” while no articles were categorized within the research topics “Commercial Exploitation of Web 2.0” or “Evaluation of Online Performance.” In contrast to the analysis of the top thirty ranked IS journals in which the top three research topics were “Business Models of Online Marketing” (41.1 %), “the Internet Advertising Landscape” (22.4 %), and Evaluation of Online Performance (16.5 %); the top three research topics within the top five marketing journals were “the Internet marketing Landscape” (63.6 %), “Business Models of Online Marketing” (13.6 %), and “Other Topics” (13.6 %). Due to the small number of articles in the sample, it is difficult to make any statements regarding trends in the Internet marketing research in the top Marketing journals.

Limitations and directions for future research

The current analysis of the Internet marketing literature is not without limitations and should be offset with future efforts. In summary, this literature review highlights the upward trend of Internet marketing research but also the limitations of both the research strategies employed and the topics investigated. The authors would suggest future literature reviews should expand article searches to full article text searches, search a broader domain of research outlets, and include other Internet marketing related search terms. Our literature analysis is meant to serve as a representative sample of articles and not a comprehensive or exhaustive analysis of the entire population of articles published on the topic of ‘Internet marketing.’ To further investigate this body of research, future research studies could explore the diversity of the Internet marketing research domain (Lee et al. 2007 ) or revisit Ngai and Wat’s ( 2002 ) electronic commerce literature review to assess the progress of that research stream. Other studies could take a more in depth look at the various business models or Internet advertising strategies associated with Internet marketing by reviewing the literature in areas such as electronic auctions, search strategies, social media, e-tailing, and various other research domains.

As Internet marketing continues to grow, future studies should consider the role of research relative to generalizability, precision of measure, and realism of context. Future research efforts should adopt more precise measures of what is occurring in this domain. Much of the research in our sample reports the new technologies and issues in Internet marketing without attempting to explain the fundamental issues of IS research. This is to be expected as this research domain appears to still be in the exploratory stages. For researchers to continue to attempt to answer the important questions in Internet marketing, future studies need to employ a wider variety of research strategies to investigate these important issues. Scandura and Williams ( 2000 ) stated that looking at research strategies employed over time by triangulation in a given subject area can provide useful insights into how theories are developing. In addition to the lack of variety in research strategy, very little triangulation has occurred during the timeframe used to conduct this literature review. This absence of coordinated theory development causes the research in Internet marketing to appear haphazard and unfocused.

However, the good news is that many of the research strategies and topics in this research are available for future research efforts. Of particular interest to researchers and practitioners would be studies observing consumer behaviour in real time using lab and field experiments or measuring purchasing behaviour from using stored click stream data in a secondary field study. We encourage researchers in fields of IS and Marketing to continue developing the body of research on this important topic using cross-disciplinary teams composed of researchers from business and the behavioural sciences. In addition, future studies could consider the six Internet marketing categories with respect to the research strategies. More specifically, each ‘zero’ appearing in Tables  8 and 11 represent gaps in the literature which provide countless opportunities for researchers to build upon the current body of published research. With this in mind, we hope this research analysis lays a foundation for developing a more complete body of knowledge relative to Internet marketing research within the fields of Information Systems and Marketing.

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Walker College of Business, Computer Information Systems, Appalachian State University, 2113 Raley Hall, ASU Box 32049, Boone, NC, 28608-2049, USA

J. Ken Corley II

IS & DS, School of Business, Auburn University at Montgomery, 310H Clement Hall, PO Box 244023, Montgomery, AL, 36124-4023, USA

Zack Jourdan & W. Rhea Ingram

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Correspondence to J. Ken Corley II .

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Responsible Editor: Christopher Patrick Holland

Appendix A – data sample (411 information systems articles)

Abbasi, A., Chen, H. C., & Nunamaker, J. F. (2008). Stylometric Identification in Electronic Markets: Scalability and Robustness. Journal of Management Information Systems, 25 (1), 49–78. doi: 10.2753/mis0742-1222250103

Adam, S. (2002). A model of Web use in direct and online marketing strategy. Electronic Markets, 12 (4), 262–269.

Albrecht, C. C., Dean, D. L., & Hansen, J. V. (2005). Marketplace and technology standards for B2B e-commerce: progress, challenges, and the state of the art. Information & Management, 42 (6), 865–875. doi: 10.1016/j.im.2004.09.003

Allen, G., & Wu, J. A. (2010). How well do shopbots represent online markets? A study of shopbots’ vendor coverage strategy. European Journal of Information Systems, 19 (3), 257–272. doi: 10.1057/ejis.2010.6

Amblee, N., & Bui, T. (2008). Can brand reputation improve the odds of being reviewed on-line? International Journal of Electronic Commerce, 12 (3), 11–28.

Amir, Y., Awerbuch, B., & Borgstrom, R. S. (2000). A cost-benefit framework for online management of a metacomputing system. Decision Support Systems, 28 (1–2), 155–164. doi: 10.1016/s0167-9236(99)00081-0

Anckar, B., & Walden, P. (2000). Destination Maui? An exploratory assessment of the efficacy of self-booking in travel. Electronic Markets, 10 (2), 110–119.

Animesh, A., Ramachandran, V., & Viswanathan, S. (2010). Quality Uncertainty and the Performance of Online Sponsored Search Markets: An Empirical Investigation. Information Systems Research, 21 (1), 190–201. doi: 10.1287/isre.1080.0222

Animesh, A., Viswanathan, S., & Agarwal, R. (2011). Competing “Creatively” in Sponsored Search Markets: The Effect of Rank, Differentiation Strategy, and Competition on Performance. Information Systems Research, 22 (1), 153–169.

Antony, S., Lin, Z. X., & Xu, B. (2006). Determinants of escrow service adoption in consumer-to-consumer online auction market: An experimental study. Decision Support Systems, 42 (3), 1889–1900. doi: 10.1016/j.dss.2006.04.012

Apigian, C. H., Ragu-Nathan, B. S., & Ragu-Nathan, T. (2006). Strategic profiles and Internet Performance: An empirical investigation into the development of a strategic Internet system. Information & Management, 43 (4), 455–468.

Aron, R., & Clemons, E. K. (2001). Achieving the optimal balance between investment in quality and investment in self-promotion for information products. Journal of Management Information Systems, 18 (2), 65–88.

Arunkundram, R., & Sundararajan, A. (1998). An economic analysis of electronic secondary markets: installed base, technology, durability and firm profitability. Decision Support Systems, 24 (1), 3–16. doi: 10.1016/s0167-9236(98)00059-1

Ayanso, A., & Yoogalingam, R. (2009). Profiling Retail Web Site Functionalities and Conversion Rates: A Cluster Analysis. International Journal of Electronic Commerce, 14 (1), 79–113. doi: 10.2753/jec1086-4415140103

Ba, S., Stallaert, J., Whinston, A. B., & Zhang, H. (2005). Choice of transaction channels: The effects of product characteristics on market evolution. Journal of Management Information Systems, 21 (4), 173–197.

Bai, X. (2011). Predicting consumer sentiments from online text. Decision Support Systems, 50 (4), 732–742. doi: 10.1016/j.dss.2010.08.024

Bakos, J. Y., & Nault, B. R. (1997). Ownership and investment in electronic networks. Information Systems Research, 8 (4), 321–341. doi: 10.1287/isre.8.4.321

Bakos, Y., & Katsamakas, E. (2008). Design and ownership of two-sided networks: Implications for Internet platforms. Journal of Management Information Systems, 25 (2), 171–202. doi: 10.2753/mis0742-1222250208

Bakos, Y., Lucas, H. C., Oh, W., Simon, G., Viswanathan, S., & Weber, B. W. (2005). The impact of e-commerce on competition in the retail brokerage industry. Information Systems Research, 16 (4), 352–371. doi: 10.1287/isre.1050.0064

Bampo, M., Ewing, M. T., Mather, D. R., Stewart, D., & Wallace, M. (2008). The effects of the social structure of digital networks on viral marketing performance. Information Systems Research, 19 (3), 273–290.

Bapna, R., Chang, S. A., Goes, P., & Gupta, A. (2009). Overlapping online auctions: empirical characterization of bidder strategies and auction prices. MIS Quarterly, 33 (4), 763–783.

Bapna, R., Goes, P., & Gupta, A. (2003). Replicating online Yankee auctions to analyze auctioneers’ and bidders’ strategies. Information Systems Research, 14 (3), 244–268. doi: 10.1287/isre.14.3.244.16562

Bapna, R., Jank, W., & Shmueli, G. (2008). Price formation and its dynamics in online auctions. Decision Support Systems, 44 (3), 641–656. doi: 10.1016/j.dss.2007.09.004

Barrot, C., Albers, S., Skiera, B., & Schafers, B. (2010). Vickrey vs. eBay: Why Second-Price Sealed-Bid Auctions Lead to More Realistic Price-Demand Functions. International Journal of Electronic Commerce, 14 (4), 7–38. doi: 10.2753/jec1086-4415140401

Basu, A., & Muylle, S. (2003). Online support for commerce processes by web retailers* 1. Decision Support Systems, 34 (4), 379–395.

Beech, J., Chadwick, S., & Tapp, A. (2000). Scoring with the Net-the Cybermarketing of English Football Clubs. Electronic Markets, 10 (3), 176–184.

Belanger, F., Hiller, J. S., & Smith, W. J. (2002). Trustworthiness in electronic commerce: the role of privacy, security, and site attributes. The Journal of Strategic Information Systems, 11 (3–4), 245–270.

Bell, D., de Cesare, S., Iacovelli, N., Lycett, M., & Merico, A. (2007). A framework for deriving semantic web services. Information Systems Frontiers, 9 (1), 69–84. doi: 10.1007/s10796-006-9018-z

Benbunan-Fich, R., & Fich, E. M. (2004). Effects of Web traffic announcements on firm value. International Journal of Electronic Commerce, 8 (4), 161–181.

Bergen, M. E., Kauffman, R. J., & Lee, D. (2005). Beyond the hype of frictionless markets: Evidence of heterogeneity in price rigidity on the Internet. Journal of Management Information Systems, 22 (2), 57–89.

Bhargava, H. K., & Choudhary, V. (2004). Economics of an information intermediary with aggregation benefits. Information Systems Research, 15 (1), 22–36. doi: 10.1287/isre.1040.0014

Bhatnagar, A., & Papatla, P. (2001). Identifying locations for targeted advertising on the Internet. International Journal of Electronic Commerce, 5 (3), 23–44.

Bhattacharjee, S., Gopal, R., Lertwachara, K., & Marsden, J. R. (2006). Whatever happened to payola? An empirical analysis of online music sharing. Decision Support Systems, 42 (1), 104–120.

Blount, Y. (2011). Employee management and service provision: a conceptual framework. Information Technology & People, 24 (2), 134–157. doi: 10.1108/09593841111137331

Bock, G. W., Lee, S. Y. T., & Li, H. Y. (2007). Price comparison and price dispersion: products and retailers at different Internet maturity stages. International Journal of Electronic Commerce, 11 (4), 101–124.

Bockstedt, J. C., Kauffman, R. J., & Riggins, F. J. (2006). The move to artist-led on-line music distribution: A theory-based assessment and prospects for structural changes in the digital music market. International Journal of Electronic Commerce, 10 (3), 7–38. doi: 10.2753/jec1086-4415100301

Bolton, G., Loebbecke, C., & Ockenfels, A. (2008). Does competition promote trust and trustworthiness in online trading? An experimental study. Journal of Management Information Systems, 25 (2), 145–169. doi: 10.2753/mis0742-1222250207

Browne, G. J., Durrett, J. R., & Wetherbe, J. C. (2004). Consumer reactions toward clicks and bricks: investigating buying behaviour on-line and at stores. Behaviour & Information Technology, 23 (4), 237–245. doi: 10.1080/01449290410001685411

Bunduchi, R. (2005). Business relationships in Internet-based electronic markets: the role of goodwill trust and transaction costs. Information Systems Journal, 15 (4), 321–341. doi: 10.1111/j.1365-2575.2005.00199.x

Burgess, S., Sellitto, C., Cox, C., & Buultjens, J. (2009). Trust perceptions of online travel information by different content creators: Some social and legal implications. Information Systems Frontiers , 1–15.

Byers, R. E., & Lederer, P. J. (2001). Retail bank services strategy: A model of traditional, electronic, and mixed distribution choices. Journal of Management Information Systems, 18 (2), 133–156.

Cao, Q., Duan, W., & Gan, Q. (2010). Exploring Determinants of Voting for the. Decision Support Systems .

Cao, Y., Gruca, T. S., & Klemz, B. R. (2003). Internet pricing, price satisfaction, and customer satisfaction. International Journal of Electronic Commerce, 8 (2), 31–50.

Castañeda, J. A., Muñoz-Leiva, F., & Luque, T. (2007). Web Acceptance Model (WAM): Moderating effects of user experience. Information & Management, 44 (4), 384–396.

Cazier, J. A., Shao, B. B. M., & Louis, R. D. S. (2007). Sharing information and building trust through value congruence. Information Systems Frontiers, 9 (5), 515–529.

Chang, H. H., & Chen, S. W. (2009). Consumer perception of interface quality, security, and loyalty in electronic commerce. Information & Management, 46 (7), 411–417.

Chang, M. K., Cheung, W. M., & Lai, V. S. (2005). Literature derived reference models for the adoption of online shopping. Information & Management, 42 (4), 543–559. doi: 10.1016/s0378-7206(04)00051-5

Changa, K. C., Jackson, J., & Grover, V. (2003). E-commerce and corporate strategy: an executive perspective. Information & Management, 40 (7), 663–675. doi: 10.1016/s0378-7206(02)00095-2

Chellappa, R. K., & Kumar, K. R. (2005). Examining the role of “Free” product-augmenting Online services in pricing and customer retention strategies. Journal of Management Information Systems, 22 (1), 355–377.

Chellappa, R. K., & Shivendu, S. (2003). Economic implications of variable technology standards for movie piracy in a global context. Journal of Management Information Systems, 20 (2), 137–168.

Chellappa, R. K., Sin, R. G., & Siddarth, S. (2011). Price Formats as a Source of Price Dispersion: A Study of Online and Offline Prices in the Domestic US Airline Markets. Information Systems Research, 22 (1), 83–98. doi: 10.1287/isre.1090.0264

Chen, C. C., Wu, C. S., & Wu, R. C. F. (2006). e-Service enhancement priority matrix: The case of an IC foundry company. Information & Management, 43 (5), 572–586. doi: 10.1016/j.im.2006.01.002

Chen, L. D., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: an extended technology acceptance perspective. Information & Management, 39 (8), 705–719. doi: 10.1016/s0378-7206(01)00127-6

Chen, P. Y., & Hitt, L. M. (2002). Measuring switching costs and the determinants of customer retention in Internet-enabled businesses: A study of the Online brokerage industry. Information Systems Research, 13 (3), 255–274. doi: 10.1287/isre.13.3.255.78

Cheng, F. F., & Wu, C. S. (2010). Debiasing the framing effect: The effect of warning and involvement. Decision Support Systems, 49 (3), 328–334.

Cheng, H. K., & Dogan, K. (2008). Customer-centric marketing with Internet coupons. Decision Support Systems, 44 (3), 606–620. doi: 10.1016/j.dss.2007.09.001

Cheng, T. C. E., Lam, D. Y. C., & Yeung, A. C. L. (2006). Adoption of Internet banking: An empirical study in Hong Kong. Decision Support Systems, 42 (3), 1558–1572. doi: 10.1016/j.dss.2006.01.002

Cheng, Z., & Nault, B. R. (2007). Internet channel entry: retail coverage and entry cost advantage. Information Technology & Management, 8 (2), 111–132. doi: 10.1007/s10799-007-0015-9

Cheung, K. W., Kwok, J. T., Law, M. H., & Tsui, K. C. (2003). Mining customer product rating for personalized marketing. Decision Support Systems, 35 (2), 231–243. doi: 10.1016/s0167-9236(02)00108-2

Chiou, W. C., Lin, C. C., & Perng, C. (2010). A strategic framework for website evaluation based on a review of the literature from 1995–2006. Information & Management, 47 (5–6), 282–290.

Chircu, A. M., & Kauffman, R. J. (2000a). Limits to value in electronic commerce-related IT investments. Journal of Management Information Systems, 17 (2), 59–80.

Chircu, A. M., & Kauffman, R. J. (2000b). Reintermediation strategies in business-to-business electronic commerce. International Journal of Electronic Commerce, 4 (4), 7–42.

Chircu, A. M., & Mahajan, V. (2006). Managing electronic commerce retail transaction costs for customer value. Decision Support Systems, 42 (2), 898–914. doi: 10.1016/j.dss.2005.07.011

Cho, V. (2006a). Factors in the adoption of third-party B2B portals in the textile industry. Journal of Computer Information Systems, 46 (3), 18–31.

Cho, V. (2006b). A study of the roles of trusts and risks in information-oriented online legal services using an integrated model. Information & Management, 43 (4), 502–520. doi: 10.1016/j.im.2005.12.002

Choi, J., Lee, S. M., & Soriano, D. R. (2009). An empirical study of user acceptance of fee-based online content. Journal of Computer Information Systems, 49 (3), 60–70.

Choudhary, V. (2010). Use of pricing schemes for differentiating information goods. Information Systems Research, 21 (1), 78.

Choudhury, V., & Karahanna, E. (2008). The relative advantage of electronic channels: A multidimensional view. MIS Quarterly, 32 (1), 179–200.

Christiaanse, E., Van Diepen, T., & Damsgaard, J. (2004). Proprietary versus Internet technologies and the adoption and impact of electronic marketplaces. Journal of Strategic Information Systems, 13 (2), 151–165. doi: 10.1016/j.jsis.2004.02.004

Chua, C. E. H., & Wareham, J. (2008). Parasitism and Internet auction fraud: An exploration. Information and Organization, 18 (4), 303–333. doi: 10.1016/j.infoandorg.2008.01.001

Chua, C. E. H., Wareham, J., & Robey, D. (2007). The role of online trading communities in managing Internet auction fraud. MIS Quarterly, 31 (4), 759–781.

Chun, S. H., & Kim, J. C. (2005). Pricing strategies in B2C electronic commerce: analytical and empirical approaches. Decision Support Systems, 40 (2), 375–388. doi: 10.1016/j.dss.2004.04.012

Clemons, E. K. (2009a). Business models for monetizing Internet applications and Web sites: Experience, theory, and predictions. Journal of Management Information Systems, 26 (2), 15–41.

Clemons, E. K. (2009b). The complex problem of monetizing virtual electronic social networks. Decision Support Systems, 48 (1), 46–56.

Crowston, K., & Myers, M. D. (2004). Information technology and the transformation of industries: three research perspectives. Journal of Strategic Information Systems, 13 (1), 5–28. doi: 10.1016/j.jsis.2004.02.001

Currie, W. L., & Parikh, M. A. (2006). Value creation in web services: An integrative model. Journal of Strategic Information Systems, 15 (2), 153–174. doi: 10.1016/j.jsis.2005.10.001

Cyr, D., Bonanni, C., Bowes, J., & Ilsever, J. (2005). Beyond trust: Web site design preferences across cultures. Journal of Global Information Management, 13 (4), 25.

Dai, Q. Z., & Kauffman, R. J. (2002). Business models for Internet-based B2B electronic markets. International Journal of Electronic Commerce, 6 (4), 41–72.

Datta, P. (2011). A preliminary study of ecommerce adoption in developing countries. Information Systems Journal, 21 (1), 3–32. doi: 10.1111/j.1365-2575.2009.00344.x

Datta, P., & Chatterjee, S. (2008). The economics and psychology of consumer trust in intermediaries in electronic markets: the EM-Trust Framework. European Journal of Information Systems, 17 (1), 12–28. doi: 10.1057/palgrave.ejis.3000729

Davis, A., & Khazanchi, D. (2008). An empirical study of online word of mouth as a predictor for multi product category e-Commerce Sales. Electronic Markets, 18 (2).

de Valck, K., van Bruggen, G. H., & Wierenga, B. (2009). Virtual communities: A marketing perspective. Decision Support Systems, 47 (3), 185–203. doi: 10.1016/j.dss.2009.02.008

De Wulf, K., Schillewaert, N., Muylle, S., & Rangarajan, D. (2006). The role of pleasure in web site success. Information & Management, 43 (4), 434–446.

Dehning, B., Richardson, V. J., Urbaczewski, A., & Wells, J. D. (2004). Reexamining the value relevance of e-commerce initiatives. Journal of Management Information Systems, 21 (1), 55–82.

Dellaert, B. G. C., & Dabholkar, P. A. (2009). Increasing the attractiveness of mass customization: The role of complementary on-line services and range of options. International Journal of Electronic Commerce, 13 (3), 43–70.

Dellarocas, C., Gao, G. D., & Narayan, R. (2010). Are consumers more likely to contribute online reviews for hit or niche products? Journal of Management Information Systems, 27 (2), 127–157. doi: 10.2753/mis0742-1222270204

Devaraj, S., Fan, M., & Kohli, R. (2006). Examination of online channel preference: Using the structure-conduct-outcome framework. Decision Support Systems, 42 (2), 1089–1103. doi: 10.1016/j.dss.2005.09.004

Dewan, R., Jing, B., & Seidmann, A. (2000). Adoption of Internet-based product customization and pricing strategies. Journal of Management Information Systems, 17 (2), 9–28.

Dewan, R. M., & Freimer, M. L. (2003). Consumers prefer bundled add-ins. Journal of Management Information Systems, 20 (2), 99–111.

Dewan, R. M., Freimer, M. L., Seidmann, A., & Zhang, J. (2004). Web portals: Evidence and analysis of media concentration. Journal of Management Information Systems, 21 (2), 181–199.

Dewan, S., & Ren, F. (2007). Risk and return of information technology initiatives: Evidence from electronic commerce announcements. Information Systems Research, 18 (4), 370–394. doi: 10.1287/isre.1070.0120

Dhar, V., & Ghose, A. (2010). Sponsored Search and Market Efficiency. Information Systems Research, 21 (4), 760–772. doi: 10.1287/isre.1100.0315

Dos Santos, B. L., & Peffers, K. (1998). Competitor and vendor influence on the adoption of innovative applications in electronic commerce. Information & Management, 34 (3), 175–184. doi: 10.1016/s0378-7206(98)00053-6

Dou, W. Y., Lim, K. H., Su, C. T., Zhou, N., & Cui, N. (2010). Brand positioning strategy using search engine marketing. MIS Quarterly, 34 (2), 261–279.

Du, A. Y., Geng, X. J., Gopal, R. D., Ramesh, R., & Whinston, A. B. (2008). Topographically discounted Internet infrastructure resources: a panel study and econometric analysis. Information Technology & Management, 9 (2), 135–146. doi: 10.1007/s10799-007-0034-6

Du, T. C., Li, E. Y., & Wei, E. (2005). Mobile agents for a brokering service in the electronic marketplace. Decision Support Systems, 39 (3), 371–383.

Duan, W., Gu, B., & Whinston, A. B. (2009). Informational cascades and software adoption on the internet: an empirical investigation. MIS Quarterly, 33 (1), 23–48.

Duan, W. J. (2010). Analyzing the impact of intermediaries in electronic markets: an empirical investigation of online consumer-to-consumer (C2C) auctions. Electronic Markets, 20 (2), 85–93. doi: 10.1007/s12525-010-0034-y

Dutta, A. (2001). Business planning for network services: A systems thinking approach. Information Systems Research, 12 (3), 260–285. doi: 10.1287/isre.12.3.260.9713

Dwivedi, Y. K., Papazafeiropoulou, A., Brinkman, W. P., & Lal, B. (2010). Examining the influence of service quality and secondary influence on the behavioural intention to change Internet service provider. Information Systems Frontiers, 12 (2), 207–217. doi: 10.1007/s10796-008-9074-7

Easley, R. F., Wood, C. A., & Barkataki, S. (2010). Bidding Patterns, Experience, and Avoiding the Winner’s Curse in Online Auctions. Journal of Management Information Systems, 27 (3), 241–268. doi: 10.2753/mis0742-1222270309

Edelman, B., & Ostrovsky, M. (2007). Strategic bidder behavior in sponsored search auctions. Decision Support Systems, 43 (1), 192–198. doi: 10.1016/j.dss.2006.08.008

El Sawy, O. A., Malhotra, A., Gosain, S., & Young, K. M. (1999). IT-intensive value innovation in the electronic economy: Insights from Marshall Industries. MIS Quarterly, 23 (3), 305–335.

Erat, P., Desouza, K. C., Schafer-Jugel, A., & Kurzawa, M. (2006). Business customer communities and knowledge sharing: exploratory study of critical issues. European Journal of Information Systems, 15 (5), 511–524. doi: 10.1057/palgrave.ejis.3000643

Even, A., Shankaranarayanan, G., & Berger, P. D. (2010). Evaluating a model for cost-effective data quality management in a real-world CRM setting. Decision Support Systems .

Flavián, C., Guinalíu, M., & Gurrea, R. (2006). The role played by perceived usability, satisfaction and consumer trust on website loyalty. Information & Management, 43 (1), 1–14.

Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19 (3), 291–313. doi: 10.1287/isre.1080.0193

Gallaugher, J. M., Auger, P., & BarNir, A. (2001). Revenue streams and digital content providers: an empirical investigation. Information & Management, 38 (7), 473–485. doi: 10.1016/s0378-7206(00)00083-5

Gao, S. J., Wang, H. Q., Xu, D. M., & Wang, Y. F. (2007). An intelligent agent-assisted decision support system for family financial planning. Decision Support Systems, 44 (1), 60–78. doi: 10.1016/j.dss.2007.03.001

Garcia, R., & Gil, R. (2008). A web ontology for copyright contract management. International Journal of Electronic Commerce, 12 (4), 99–113. doi: 10.2753/jec1086-4415120404

Gauzente, C. (2009). Information search and paid results—proposition and test of a hierarchy-of-effect model. Electronic Markets, 19 (2), 163–177.

Gefen, D., Rose, G. M., Warkentin, M., & Pavlou, P. A. (2005). Cultural diversity and trust in IT adoption: A comparison of potential e-voters in the USA and South Africa. Journal of Global Information Management, 13 (1), 54–78. doi: 10.4018/jgim.2005010103

Ghose, A. (2009). Internet exchanges for used goods: an empirical analysis of trade patterns and adverse selection. MIS Quarterly, 33 (2), 263–291.

Ghose, A., Mukhopadhyay, T., & Rajan, U. (2007). The impact of Internet referral services on a supply chain. Information Systems Research, 18 (3), 300–319. doi: 10.1287/isre.1070.0130

Ghose, A., Smith, M. D., & Telang, R. (2006). Internet exchanges for used books: An empirical analysis of product cannibalization and welfare impact. Information Systems Research, 17 (1), 3–19. doi: 10.1287/isre.1050.0072

Ghose, A., & Yao, Y. L. (2011). Using Transaction Prices to Re-Examine Price Dispersion in Electronic Markets. Information Systems Research, 22 (2), 269–288. doi: 10.1287/isre.1090.0252

Glover, S., & Benbasat, I. (2010). A Comprehensive Model of Perceived Risk of E-Commerce Transactions. International Journal of Electronic Commerce, 15 (2), 47–78.

Gopal, R. D., Ramesh, R., & Whinston, A. B. (2003). Microproducts in a digital economy: Trading small, gaining large. International Journal of Electronic Commerce, 8 (2), 9–29.

Gopal, R. D., Tripathi, A. K., & Walter, Z. D. (2006). Economics of first-contact email advertising. Decision Support Systems, 42 (3), 1366–1382.

Gorman, M. F., Salisbury, W. D., & Brannon, I. (2009). Who wins when price information is more ubiquitous? An experiment to assess how infomediaries influence price. Electronic Markets, 19 (2–3), 151–162. doi: 10.1007/s12525-009-0009-z

Granados, N., Gupta, A., & Kauffman, R. J. (2008). Designing online selling mechanisms: Transparency levels and prices. Decision Support Systems, 45 (4), 729–745. doi: 10.1016/j.dss.2007.12.005

Granados, N., Gupta, A., & Kauffman, R. J. (2010). Information Transparency in Business-to-Consumer Markets: Concepts, Framework, and Research Agenda. Information Systems Research, 21 (2), 207–226. doi: 10.1287/isre.1090.0249

Granados, N. F., Gupta, A., & Kauffman, R. J. (2006). The impact of IT on market information and transparency: A unified theoretical framework. Journal of the Association for Information Systems, 7 (3), 148–178.

Granados, N. F., Kauffman, R. J., & King, B. (2008). How has electronic travel distribution been transformed? A test of the theory of newly vulnerable markets. Journal of Management Information Systems, 25 (2), 73–95. doi: 10.2753/mis0742-1222250204

Gregg, D. G., & Scott, J. E. (2006). The role of reputation systems in reducing on-line auction fraud. International Journal of Electronic Commerce, 10 (3), 95–120. doi: 10.2753/jec1086-4415100304

Gregor, S., & Jones, K. (1999). Beef producers online: Diffusion theory applied. Information Technology & People, 12 (1), 71–85.

Grenci, I. T. (2004). An adaptable customer decision support system for custom configurations. Journal of Computer Information Systems, 45 (2), 56–62.

Grover, V., & Saeed, K. A. (2004). Strategic orientation and performance of Internet-based businesses. Information Systems Journal, 14 (1), 23–42. doi: 10.1111/j.1365-2575.2004.00161.x

Gundepudi, P., Rudi, N., & Seidmann, A. (2001). Forward versus spot buying of information goods. Journal of Management Information Systems, 18 (2), 107–131.

Gupta, A., Su, B., & Walter, Z. (2004). Risk profile and consumer shopping behavior in electronic and traditional channels. Decision Support Systems, 38 (3), 347–367.

Gupta, A., Su, B. C., & Walter, Z. (2004). An empirical study of consumer switching from traditional to electronic channels: A purchase-decision process perspective. International Journal of Electronic Commerce, 8 (3), 131–161.

Gupta, S., & Kim, H. W. (2007). The moderating effect of transaction experience on the decision calculus in on-line repurchase. International Journal of Electronic Commerce, 12 (1), 127–158.

Hansen, H. R. (1995). Conceptual-framework and guidelines for the implementation of mass information-systems. Information & Management, 28 (2), 125–142. doi: 10.1016/0378-7206(95)94021-4

Harrison McKnight, D., Choudhury, V., & Kacmar, C. (2002). The impact of initial consumer trust on intentions to transact with a web site: a trust building model. The Journal of Strategic Information Systems, 11 (3–4), 297–323.

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Corley, J.K., Jourdan, Z. & Ingram, W.R. Internet marketing: a content analysis of the research. Electron Markets 23 , 177–204 (2013). https://doi.org/10.1007/s12525-012-0118-y

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Received : 08 November 2011

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Issue Date : September 2013

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Evaluating the Effectiveness of Digital Content Marketing Under Mixed Reality Training Platform on the Online Purchase Intention

1 School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, Hong Kong SAR, China

O. L. K. Chan

2 Division of Business and Hospitality Management, College of Professional and Continuing Education, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China

Xiangying Zhang

3 Institute of Industrial Engineering, School of Mechanical Engineering, Zhejiang University, Hangzhou, China

4 Re-Industrialisation, Hong Kong Science and Technology Parks Cooperation, Hong Kong, Hong Kong SAR, China

5 Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, China

K. L. Keung

Associated data.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The purpose of this research is to investigate the effectiveness of Digital Content Marketing (DCM) on a Mixed Reality (MR) training platform environment with the consideration of online purchase intention (OPI) through social media. E-commerce today encounters several common issues that cause customers to have reservations to purchase online. With the absence of physical contact points, customers often perceive more risks when making purchase decisions. Furthermore, online retailers often find it hard to engage customers and develop long-term relationships. In this research, a Structural Equation Model (SEM) is proposed to examine the efficacy of DCM from both immediate and long-term OPI. The results examine whether adopting DCM on an MR training platform environment through social media brings positive results in OPI. Empirical research was carried out through online questionnaires collected in 2021 and 2022. A total of 374 questionnaires were qualified for data analysis in this study, conducted with IBM SPSS and AMOS. The results imply that DCM is critical to stimulating both immediate and long-term OPI. The immediate OPI is positively affected by increasing perceived value through MR in DCM. Regarding the long-term OPI, increased customer engagement with DCM under MR environment can cultivate brand trust and significantly affect the long-term OPI.

Introduction

Marketing strategy is crucial in a business plan. Apart from triggering short-term sales, it determines corporate image and acts as a bridge between customers and sellers so that both parties can communicate and build a relationship. Advertising draws people’s attention to a brand’s message on a product, service, information, or idea. Traditional advertising is often displayed on billboards, external walls of buildings, magazines, newspapers, leaflets, and TV commercials. Digital Content Marketing (DCM) is a way of marketing by creating and distributing content online to deliver valuable and engaging content to customers ( Rowley, 2008 ; Holliman, 2014 ). Since the last decade, digital advertising has become much more critical than before as it is more affordable and can reach larger audiences. 5G networking is being adopted worldwide increasingly so the access speed of digital content can be instantaneous with the aid of the Internet of Things ( Keung et al., 2018 , 2020 , 2021 , 2022a , b ; Lee et al., 2018b ; Liu et al., 2019 ; Li et al., 2021b , c , d ; Xia et al., 2021 ; Zheng et al., 2021 ; Fan et al., 2022 ; Zhang et al., 2022 ). DCM aims to build a close connection with customers through continuous conversation to convince their leads over time. A company can show its expertise and strengths through delivered content so that customers may be convinced that its offering is valuable and worth purchasing ( Hollebeek and Macky, 2019 ) and can realize more on their actual needs of a product ( Thomson and Laing, 2003 ; Grant et al., 2007 ; Dawes and Nenycz-Thiel, 2014 ). Consequently, DCM can reach more potential customers, boost online purchase intention (OPI), and retain customer loyalty ( Rowley, 2008 ; Holliman, 2014 ). Considering the online seller’s perspective, a reliable and valuable e-commerce environment can retain customers, reach higher customer retention and boost sales, as the traditional marketing industry is progressively burdensome and cost-ineffective ( Khan and Siddiqui, 2013 ).

Meanwhile, misleading advertising becomes an increasing concern for digital marketing, as customers cannot do a hands-on inspection to ensure product quality. It may create false belief in the expected product performance, which varies from the actual product information to deceit, hidden contracts, fees, inexistent benefits, and exaggeration ( Sharma and Chander, 2011 ; Kumar and Gunaseelan, 2016 ). Sharma and Chander (2011) found that even though people understood the existence of misleading advertising, they could not always distinguish the trustfulness and authenticity of the product. This phenomenon may eventually affect customers’ decision-making on whether to repurchase a product or not ( Jacoby et al., 1982 ). Compared with shopping in a physical store, consumers perceive more risk of misleading and deceptive practices when online shopping ( Ahmad et al., 2016 ; Consumer Council, 2016 ). In the long term, it will affect the customer benefit and satisfaction and lead to low OPI and consumer confidence in the future ( Hollebeek et al., 2016 ; Marbach et al., 2016 ; Azer and Alexander, 2020 ).

The second issue in digital marketing is that its payoff is not straightly associated with an advertisement’s spending. Paid advertisements are too intrusive, so that customers may feel annoyed and ignore them; therefore, the effectiveness of paid advertisements is likely to become minimal ( Barreto, 2013 ). Tudoran (2019) found that the majority of people had a negative perception of intrusive marketing, as it is always one-way and irrelevant. Some responses received by Truong and Simmons (2010) stated that the responders were rarely concerned with banner advertisements and felt annoyed when they were searching for other valuable information. Internet users can access thousands of information every day, distinguishing the authenticity of an advertisement’s information. Most content is provided free of charge or at a low cost since web 3.0 started. Therefore, selling a product to Internet users becomes more challenging through traditional marketing strategies. If the contents of an advertisement bores its audience or fails to give confidence to the audience, most of them will close the pop-up commercials as soon as possible and even install pop-up blockers. In contrast, Internet users today appreciate advertising messages that are customized, valuable, and under control ( Xu et al., 2020 ; Ali et al., 2021 ; Liu et al., 2021 ; Siddique et al., 2021 ; Jamil et al., 2022 ; Shiyong et al., 2022 ; Wang et al., 2022 ).

Mixed Reality (MR) is an integration of augmented and virtual reality. As the latest immersive technology among the three, MR is involved in functional mockups, military training, medical care, and many other fields. It combines digital and real worlds to unblock the linkage between human, computer, and environment interaction. Users can communicate with digital items placed in the physical world in real-time ( Liu et al., 2017 ; Li et al., 2021d ). The virtual objects will be able to respond to users when they are equipped with the necessary equipment. For example, the MR Headset is adopted to deliver a credible and three-dimensional mixed-reality experience. To enhance the overall customer experience for online shopping, MR technologies could be adopted to create technology-enhanced customer experiences ( Alcañiz et al., 2019 ; Flavián et al., 2019 ). Castillo and Bigne (2021) proposed a model that extends the technology acceptance model by introducing factors that affect the consumers’ acceptance of augmented reality (AR) self-service technologies, providing new understandings for retailers on the adoption of AR at the point of sale. Wedel et al. (2020) proposed a conceptual framework for VR/AR research in consumer marketing that intensifies around customer experiences provided by VR/AR implementation along the consumer journey and the effectiveness of such VR/AR implementation toward consumer marketing. Alcañiz et al. (2019) further extended the VR in marketing and proposed a research agenda for VR in marketing. However, the current literature has not considered the MR-based platform for the DCM, primarily through social media. The current hypothesis models have not been tested under an MR-based platform for DCM. When compared to traditional DCM-based research with questionnaires, we further extend the scope of the field for exploring the DCM strategies that will affect the immediate and long-term OPI under the MR training platform.

Besides achieving the immediate purchase intention (in terms of product/service) by delivering helpful content to the audience, DCM can cultivate trust and customer loyalty by customer engagement. An effective way to retain customer loyalty is to build the relationship through many conversations and deliver valuable and accurate information to the audience. This way, companies can affect the mindset of the audiences over a long duration of time. Compared to traditional marketing, digital marketing technology is more affordable and easy to use. With DCM, even the SMEs can achieve an effective marketing campaign and access to their targeted customer with great content. Li et al. (2002) pointed out that reducing intrusiveness has a significant positive impact on advertising effectiveness and customer engagement. Therefore, e-commerce can capture their customer’s favor, a massive amount of data during the conversation and provide a customized product. During the first one and half years, a paid search campaign is effective. However, leads from paid search campaigns are constant, while content marketing has exponential growth. Content marketing can produce three times more than a paid search campaign in the last month of the third year. Thus, the SMEs should not give up on developing DCM, and they cannot initially observe a decisive result. The long-tail effect of DCM under an MR-based training platform will surprise everyone, as it requires time to have an exponential effect. Social media networks are the most popular way people are willing to grasp information. Users are willing to search, follow, like, and comment on a post they are interested in; hence, user-generated content can be developed. Therefore, DCM seems able to present selling messages to their targeted customer effectively, avoiding the issues of traditional paid advertisements, and at the same time is price valued. With extraordinary performance, DCM can achieve a company’s marketing objectives at a low cost. SMEs should involve the DCM in their marketing activities. The aims of this paper include:

• To evaluate the effectiveness of DCM through social media under the MR-based platform to immediate and long-term OPI.
• To evaluate the mediating effect of perceived value, customer engagement, and brand trust.
• To discuss the managerial implications of using DCM in an MR-based environment.

Adopting DCM with social media under an MR-based environment provides valuable and engaging content to raise immediate OPI and enables customer engagement to build trust and long-term OPI. This study develops a hypothesis model of DCM under an MR-based platform to conduct the empirical study for evaluating the effectiveness of DCM on OPI through social media by using Structural Equation Modeling (SEM). The confirmatory factor analysis (CFA) is adopted to test the developed conceptual model. The perceptions of Hong Kong citizens, active social media users, on DCM are captured from the questionnaires. The analysis is concentrated on DCM on Instagram, a popular online social media platform in Hong Kong. Section “Literature Review and Hypothesis” presents the theoretical background and a hypothesis model of the research. Section “Methodology” presents the research methodology. CFA is performed after the hypothesis model has been developed. The perception of Hong Kong citizens, who are active social media users, on DCM is captured by questionnaire. The results and discussion of the effectiveness of DCM are presented in Section “Data Analysis and Results,” respectively. The survey results provide theoretical and managerial implications in Section “Discussion.” Conclusion, limitations, and future research are discussed in the below section.

Literature Review and Hypothesis

Digital marketing is the component of marketing that utilizes the Internet and online-based digital technologies to promote products and services, such as desktop computers and mobile phones. Digital marketing campaigns have become prevalent as the number of digital platforms and e-commerce platforms increase, and as people discover that online shopping is more convenient and time-efficient. It employs combinations of search engine optimization (SEO), search engine marketing, content marketing, influencer marketing, data-driven marketing, e-commerce marketing, social media marketing (SMM), direct email marketing, and advertising ( Wang and McCarthy, 2020 ; Bowden and Mirzaei, 2021 ; Mathew and Soliman, 2021 ; Yaghtin et al., 2021 ).

Social Media, built on Web 2.0 technology, allows users to share, discuss, and exchange content. It is open, accessible, and content-based so users can access the content on either technology, time, geographical, ability, or identity. Users can share content instantaneously and access the audience anytime and anywhere. There is an increasing number of online sellers advertising and selling their products on social media. The above strategy is called SMM. There is nearly no additional cost to e-commerce, and sellers will face a minimal entry barrier. Sellers can also understand their customers through direct conversation and interaction, such as discovering and sharing product information and delivering valuable opinions. Therefore, the sellers can target the customers who have an enormous willingness to buy and suggest appropriate products to them, and finally, customers can make purchase decisions.

Customer engagement can be cultivated by participating in commercial activities, marketing campaigns, and interaction, including viewing, liking, commenting, and sharing the content ( Amblee and Bui, 2011 ). Positive electronic word-of-mouth (eWOM) can be created when the posts have numerous likes with encouraging comments. Potential customers will have a more favorable attitude and confidence toward the product. It can raise trust in online sellers and boost purchase intention ( Hajli, 2014 ).

Instagram, a popular social media platform in Hong Kong, is mainly a visual-based photo and video-sharing social networking platform. By sharing products’ information, online sellers can attract potential consumers and drive consumer engagement through the photography-based function of Instagram ( Bergström and Bäckman, 2013 ). A simple and most crucial rule to gain advantage in the Instagram algorithm is to generate quality content and deliver it to users. People have found that when an account with more than 5,000 followers creates 5–6 posts every day, Instagram will deliver its posts to other users who have not followed the account. Moreover, a brand can gain an advantage with UGC, which is the content developed or created by general users. For example, “like,” share, and comment can increase attention and browse traffic, tagged posts can be found in the brand’s profile, and users can generate more quality content. With UGC, which manifestly aligns with increasing trends, Instagram empowers consumers to determine media content, rather than paid experts, to be primarily distributed on the Internet ( Holliman, 2014 ; Hollebeek and Macky, 2019 ).

The content farm employs freelancers, including bloggers and part-time writers, to produce content on trending topics, resulting in a high search and browse traffic to the websites ( Bakker, 2012 ). Being online, however, means the presence of duplicators, as free content can be assessed, copied, and republished by others effortlessly. This situation is now occurring on Instagram as well. Some users possess several accounts related to different hot areas to raise their income by attracting various audiences. However, they cannot manage every account well by posting 5–6 quality photos every day. So, they purchase photos with a caption from a part-time photo designer. Usually, the pictures in this transaction are low quality, useless, or even copied from other accounts. Low-quality content cannot build purchase intention, even if it hits the trend and favor of the audience. Nevertheless, content marketing can deliver valuable information ( Bakker, 2012 ).

One of the traditional digital marketing campaigns is paid advertisement, which includes pop-up and embedded ads in a website and search engine, as well as intersection commercials before and during videos. On Instagram, paid advertisements will appear as intrusive advertisements on Instagram stories and on the home page. As mentioned in the research of Li et al. (2002) , intrusiveness occurs when commercials disturb the ongoing entertaining activities of the user. Forced and intrusive businesses will injure consumer perceived value, and the consumer may even respond negatively. Diversely, the perceived intrusiveness level of an advertisement will be decreased when the user finds the content is valuable and consistent with the websites or editorials ( Ying et al., 2009 ). Paid advertisements finally decrease purchase intention ( Goodrich et al., 2015 ).

This section will be described from the digital marketing to SMM and DCM, as DCM is in the subset of digital marketing, and adopting DCM on Instagram is one of the SMM methods. The attributes and advantages of DCM are also captured from the literature. Some popular content marketing frameworks are included to illustrate how to develop an effective DCM and why DCM can have those benefits.

Digital Content Marketing

Digital Content Marketing refers to the act of conducting all marketing-related activities through the Internet, including advertising, purchasing process, customer service, and delivery service ( Koiso-Kanttila, 2004 ; Ahmad et al., 2016 ; Naidoo and Hollebeek, 2016 ). Holliman (2014) proposed that inbound marketing is more efficient and effective in costing, spreading, extending the customer boundary, and co-creating value. DCM, which delivers valuable and interactive content to potential customers, is a technique to support inbound marketing.

Instead of putting significant effort and resources into outreaching leads, DCM focuses on creating excellent content that can provide a long-tail effect ( Ahmad et al., 2016 ; Hollebeek and Macky, 2019 ). People describe DCM as an art of communicating with the customer, but without directly selling a product ( Koiso-Kanttila, 2004 ; Ahmad et al., 2016 ; Hollebeek and Macky, 2019 ). By creating great content and e-conversation, a company can build a relationship with its existing customers, acquire new customers, retain customer loyalty, and build a reliable brand name. Moreover, the company can cultivate sales activity through customer engagement, loyalty, and relationship in the long run ( Holliman, 2014 ). Killing content has the features of all branded content, random content, and the content the customer wants to know. Therefore, it is interestingly relevant to the customer engaging and syndicating. DCM is able to share valuable and free content related to the brand or the field. Moreover, DCM can attract and convert audiences to customers and repeat consumers ( Le, 2013 ). People come to read, see, learn, and experience; therefore, the company usually tells its unique and meaningful stories to grasp and retain customers’ attention, which also comes within DCM’s scope and as a particular content form of company images ( Holliman, 2014 ). Pulizzi and Barrett (2009) explained that companies are the experts in their business fields, and they can capture the most reliable and latest content resources. Therefore, a lead interested in the content may be willing to search or investigate certain content. Expert content can draw the attention of potential customers, who can understand the value of the content and product provided ( Stone and Woodcock, 2014 ).

By creating great content and customer engagement, a company can build brand awareness, acquire new customers, retain customer loyalty, and finally achieve repeat sales ( Holliman, 2014 ; Hollebeek and Macky, 2019 ). The customers have a more favorable attitude and confidence toward the product and the brand, as they are the hot trend between peers. It can raise the trust of online sellers, and the OPI can be boosted ( Hajli, 2014 ). The brand, adopting DCM strategies, is discovered by customers when they demand the relevant content or product, thereby revealing a more significant consumer-engaged attitude. It is different from intrusive advertisements, which interrupts current activity while delivering a sales message ( Holliman, 2014 ; Hollebeek and Macky, 2019 ). Through direct conversation and continuous interaction, sellers can engage their customers who are willing to buy. Sellers can also suggest appropriate products to customers to make purchase decisions collaboratively ( Chen et al., 2011 ). Moreover, quality content helps maintain customer loyalty with two-way conversation ( Koiso-Kanttila, 2004 ; Ahmad et al., 2016 ; Naidoo and Hollebeek, 2016 ).

As shown in Figure 1 , adopting DCM on social media is a part of Social Network Marketing (SNM), and companies not only grasp the opportunity, the emerging trends of SNM, to cultivate long-term OPI, but also boost immediate OPI by delivering better content ( Madsen and Slåtten, 2015 ; Ahmad et al., 2016 ; Wertalik, 2017 ; Mason et al., 2021b ; Abbas et al., 2022 ). The current literature has been found to describe the attributes and advantages of DCM, and numerous empirical studies have been conducted to find the influences of SNM on OPI ( Yang et al., 2016 ; Bolat and O’Sullivan, 2017 ). Research on the impact of Social Media Content Marketing (SMCM) on brand health indicated that SMCM plays a vital role in brand health since it acts as the inter-connection for a potential customer to grasp the brand’s information ( Canhoto et al., 2015 ; Ahmad et al., 2016 ; Kareem et al., 2016 ; Mason et al., 2021a ). However, no study has investigated the effectiveness of DCM on both immediate and long-term OPI simultaneously. Conducting DCM on social media is an SNM strategy, but not every marketing in social media can be concluded as DCM. Thus, the influences of DCM via social media may not be fully equal to SNM, which may be affected by more attributes ( Dessart, 2017 ; Valos et al., 2017 ; Tafesse and Wien, 2018 ). For example, the familiarity and the perceived value of a product. This study is an empirical investigation to evaluate the effectiveness of DCM on both immediate and long-term OPI. A conceptual model based on the extant literature is developed to evaluate the DCM’s efficacy with SEM.

A conceptual framework of digital marketing.

Immediate Purchase Intension

In the research investigated by Chinomona et al. (2013) , the significant positive impact of perceived product quality on perceived product value was investigated. The customer’s perceived value of the product can affect OPI. The researchers proved that the interviewees perceived the product as valuable when they recognized that the product had good quality and excellent attributes. The perceived product quality is about cognitive familiarity based on impressions, advertisements, and comments from others on the product. Therefore, OPI can reduce the perceived risk and increase transaction intentions ( Chinomona et al., 2013 ). Hollebeek and Macky (2019) believed that content marketing is customer-oriented, as it aims to offer suitable solutions to customers with persuasive argumentation and helps customers to recognize the outstanding advantage of the product before they buy it, but not force them to buy ( Koiso-Kanttila, 2004 ; Rowley, 2008 ; Holliman, 2014 ). The valuable content can comfort customers by telling them how the product/service meets their demands and what they can gain from it.

H1 : DCM under MR environment on social media is positively related to the perceived value on the product/service.

Familiarizing with the perceived value can build trust in the product, and a higher level of trust can unlock customer’ OPI. It has been proven that the product’s perceived value can reinforce the immediate OPI through trust, reducing consumer perceived risk ( Chinomona et al., 2013 ). Therefore, the perceived value of OPI can be immediately obtained, when customers receive the engaging content that entices people to take some kind of action. In addition, branded content, that combining both advertising and entertainment into one marketing communication content, could link to organization brand.

H2 : Perceived value on the product/service is positively related to the immediate OPI.

Long-Term Purchase Intention

Social Network Marketing adopts social media as a platform for brands to interact with their customers to develop further purchaser relationships, which helps to maintain loyalty and repeat purchase ( Meyer-Waarden and Benavent, 2006 ; Wu et al., 2008 ; Papagiannidis et al., 2013 ; Malthouse et al., 2016 ; Nabec et al., 2016 ). Adopting DCM on social media, the seller can deliver quality content in different media types (text/audio/photo/video) on the post, story, and profile ( Ahmad et al., 2016 ). The bargaining power in the market has shifted from sellers to buyers through the capability of the Internet, which significantly leverages the consumer’s voice ( Mohamad et al., 2018 ). Companies can no longer make a unilateral decision regarding the price, quality, and after-sales service, and are being pushed to participate in conversations with customers to understand their needs and cultivate a close relationship through customer engagement. Therefore, excellent customer engagement can be cultivated by adopting DCM on social media, as SNM has been proven to have a significant influence on customer engagement ( Areeba et al., 2017 ; Mohamad et al., 2018 ; Rozina et al., 2019 ; Hartiwi et al., 2020 ).

Brand trust indicates that consumers feel comfortable and are willing to make OPI, even in a situation of uncertainty ( Laroche et al., 2012 ). Enduring involvement with the product has been demonstrated to positively influence brand trust ( Erik, 2019 ). The enduring content allows customers to get familiarized with the product and the brand communities. Customers, therefore, cultivate more brand trust as they have perceived less risk and reduced uncertainty ( Laroche et al., 2012 ). Especially for new leads, initial trust is formed through the brand impression by the available information of the product and brand communities, which is the critical element that DCM will deliver ( Stouthuysen et al., 2018 ).

H3 : DCM under MR environment on social media is positively related to customer engagement.

H4 : DCM under MR environment on social media is positively affecting brand trust.

Enduring conversation and excellent customer engagement can increase familiarity between the seller and customer. As mentioned, the seller, or the content provider, is the expert in the specific area related to the content. Excellent content can build trust between the seller and customer, as the customer will perceive it worthwhile and reliable if they can absorb valuable knowledge during online shopping. Ahmad et al. (2016) found that sites will lose their customer’s interest if they only deliver simple responses or quick answers to their customer’s inquiries. Differing from that, social media sites, which provide unique content with plentiful customer engagement, can gain more customer attention and trust.

Either cognitive, emotional, or behavioral customer engagement acts as the primary effect (first-tier) of DCM, and intra-interaction, respectively, fosters the brand-related sense-marketing, citizenship behavior, and identification (second-tier) through different customer engagement. The third tier consequence of DCM is trust, either on credibility or benevolence and brand attitude. DCM will finally affect consumer-based brand equity and firm-based brand equity ( Hollebeek and Macky, 2019 ).

Functional motive can also integrate with the hedonic motive (for example, entertaining and interesting content) to drive behavioral engagement, which means customers are willing to spend time, effort, and energy interacting with the brand. Moreover, functional motive integrates with authenticity motive (integrity and credibility content) and can cultivate cognitive engagement, and authenticity motive combines with hedonic involvement to achieve emotional engagement. Besides spending effort on interaction with the brand, people are triggered into brand identification and sense of belonging and further achieve trust on either credibility or benevolence ( Hollebeek and Macky, 2019 ).

H5 : Customer engagement is positively related to brand trust.

Besides attracting more leads, detailed and trusted content can obtain higher customer retention ( Pulizzi and Barrett, 2009 ), which means the seller will be the priority choice. Moreover, social media have been proven to play a significant role in OPI, as trust can be cultivated and accumulated through quality product/service, customer engagement, and trust ( Mohamad et al., 2018 ). Areeba et al. (2017) has pointed out that customer engagement becomes a primary concern for the online retailer as the accumulated emotional ties between customers and companies help to convince their consumers to make the right buying decision ( Rose and Samouel, 2009 ; Hollebeek, 2011 ; Leckie et al., 2016 ; Dessart, 2017 ).

Hollebeek and Macky (2019) illustrated three incentives that can drive customers to make purchase decisions through interaction with DCM communications. For the functional motive, customers are willing to seek valuable information. Regarding the hedonic motive, customers found that they can entertain themselves, relax, and absorb the knowledge if they are enjoying the content. Regarding the authenticity motive, the ultimate desires of consumers are achieved through the brand-related connection, integrity, credibility, and customer relationship from DCM ( Hollebeek and Macky, 2019 ). Rather than persuading potential customers to purchase the product directly, DCM is designed to develop and reinforce consumer engagement, awareness, trust, and the relationship between both parties ( Ahmad et al., 2016 ; Hollebeek and Macky, 2019 ). Therefore, DCM can increase long-term sales and lead to repeat sales by accumulating relationships and trust. Figure 2 shows the conceptual model of DCM.

Conceptual model: digital content marketing (DCM).

H6 : Customer engagement is positively related to long-term OPI.

H7 : Brand trust is positively related to long-term OPI.

Methodology

A survey was conducted to obtain the opinion and perception of the effect of DCM under an MR-based training platform environment among Hong Kong residents. The effect of DCM on OPI was analyzed by SEM. Around 1000 questionnaires were distributed to the participants through Google Form from November 2021 to January 2022. Only the digital surveying method has been adopted in the research due to the outbreak of COVID-2019.

Mobile MR-apps enhance retail visits, including online shopping adoptability, by providing multiple product demonstration capabilities ( Alcañiz et al., 2019 ; Wedel et al., 2020 ; Castillo and Bigne, 2021 ). The questionnaire presented the DCM simulating the online purchase intention through Instagram’s social media. The DCM is designed for multiple scenarios, including online travel agencies, fashion, beauty, and electronic products, shown in an MR on mobile devices. The research had two sections. In the first part, the participants used the mobile device to conduct three template scenarios, including online travel agencies, fashion, beauty, and electronic products with Instagram. Those contents are designed under the MR-based mobile environment. All the participants also conducted MR-based digital content through social media as examples for simulating the effects of the real-world DCM through social media. The second part consisted of an online questionnaire that assessed six constructs based on literature.

There are five reasons for choosing Instagram rather than another social media and digital platform. First, the user base of Instagram has been observed a significant growth trend, which is more evident than others. Second, the target groups of online retail in Hong Kong are mainly teenagers, young people, and middle-aged people, which entirely match the majority of user groups of Instagram. Third, as social media marketing simultaneously, DCM on Instagram can seize its benefits, for instance, customer engagement, user-generated content, and electronic word-of-mouth. Moreover, Instagram has many functions for corporations to communicate with audiences, and the graphics-based peculiarities allow them to deliver engaging and valuable content to their fans efficiently. Last but not least, there are many e-commerce and boutiques on Instagram. Sellers can market their customer and audiences simultaneously. Therefore, the search is to determine the effectiveness of DCM on Instagram. The experimental subjects are active users of social media in Hong Kong.

The questionnaire was designed based on Confirmatory Factor Analysis and a multi-item measurement scale. A seven-point Likert-type scale, where one indicates “strongly disagree” and seven interprets “strongly agree,” was adopted to evaluate the perception on different dimensions related to DCM ( Park et al., 2004 ; Leong et al., 2015 ). The questionnaire items are summarized and modified based on the literature whereas the hypothesis settings are. Hence, the questionnaire included six constructs, measured on various scales adapted from previous studies.

The online survey was designed for Hong Kong citizens who use social media frequently. The participants were voluntary anonymous, and the results were confidential. The survey was first developed in English based on the literature and previous studies and translated into Chinese by a bilingual researcher. Three questions have been asked to indicate the Chinese and English proficiency level and any language-related difficulties.

Two screening questions developed that the potential participants were regular social media users and online shopping customers. Only participants who were regular social media users and online shopping customers were considered. Five incomplete responses answered by people who do not use any social media were found, nor non-online shopping users, and 25 invalid responses failed to answer Neutral in either one or both verification questions. There were 374 questionnaires in total qualified for the data analysis in this study. The model fit indices are affected by the sample size significantly. Therefore, Boomsma and Hoogland (2001) recommended that CFA have more than 400 samples and at least a sample size of N  > 200 ( Lee et al., 2018a ).

Data Analysis and Results

The SEM was analyzed with IBM SPSS Statistics 25 and IBM SPSS AMOS 25, ensuring reliability, confidentiality, and significance level. As a method for the covariance-based SEM, the AMOS provides more flexibility for data requirements. The benefit of AMOS-SEM is that it offers a parameter estimation model assessment and is fit for use in reflective indicators and parameter estimation modeling. Hence, AMOS-SEM performs well even when the sample size is large compared to, e.g., PLS-SEM.

Respondents’ Characteristics

Respondents’ characteristics are reported in Table 1 . The gender distribution of the qualified surveys is 178 Male (47.59%) and 196 (52.40%) Female. Nearly half of the participants fell into the 19–24 age group, followed by the 25–34 age group (35.56%) and the 35–44 age group (8.56%). Around 357 out of 374 participants had or were pursuing an Associate Degree/Higher Diploma or Bachelor’s Degree or above. The respondents’ characteristics also include the times of online shopping in the past 12 months, using social media habits, adopting different social media platforms, and reasons for online shopping. More than 75% of the respondents use social media for more than 2 h per day. Facebook, Instagram, and YouTube are the top priority social media usage in Hong Kong, of which nearly 90% of the respondents have been using. More than 75% of respondents have been shopping online during the past 12 months. The significant reasons for shopping online are convenience and a wide selection of choices. Therefore, the questionnaire results can show the opinions of significant online consumers in Hong Kong.

Respondents’ characteristics.

Measurement Model

Given that the results were similar, only the sample results as a whole are presented. Hair (2010) suggested convergent validity and the measurement reliability of data should be assessed by Standardized factor loading K of each measurement items, Cronbach’s alpha α , Composite reliability (CR), and Average Variance Extracted (AVE). Convergent validity measures of constructs that theoretically are related to each other are, in fact, observed to be related to each other. The value criteria, which indicates that the data are reliable and valid, are shown as follows: K is excellent when greater than 0.7, good between 0.5–0.7; α should be greater than 0.7 ( Freeze and Raschke, 2007 ; Corrêa et al., 2020 ). Bagozzi and Yi (2011) suggested C.R. should be higher than the acceptable levels of 0.700; AVE should be greater than 0.500, or 0.400 in the cases of exploratory research ( Corrêa et al., 2020 ). The majority of factors show sufficient internal consistency. Most of the measurement items’ K were above 0.7, and at least over 0.6. The α of the constructs were above 0.7, and ranged between 0.761 and 0.863. The C.R. varied between 0.717 and 0.833 and AVE loaded between 0.499 and 0.621. Therefore, the majority of measurements had significant internal consistency, and a few measures had relatively low reliability but also supported the convergent validity. Table 2 summarizes the confirmatory factor analysis, which includes K , α , C.R., and AVE on the constructs and measurement items. Table 2 also lists the questionnaire items which are based on a certain of literatures. Discriminant validity was tested using item cross-loadings, which indicates that a construct should share more variance with its indicators than with other constructs ( Castillo and Bigne, 2021 ) shown in Table 3 . The value of the correlations was significant value of p  < 0.01, except for brand trust and customer engagement.

The measurement model (convergent validity).

Discriminant validity (correlations between constructs).

These two formulas calculate the C.R. and AVE:

e  = residual/error

To examine the common method bias, Podsakoff et al. (2003) proposed and summarized for the confirmatory factor analysis was estimated, restricting all the indicators in the model to load on a single factor. Table 4 shows the model absolute fit measures. The Goodness-of-fit index (GFI) is adequate when larger than 0.9, and a perfect fit with the value near 1.0 ( Bentler, 1990 ). GFI scores in the range of 0.8–0.9 represent a good fit as they are quite affected by the sample size ( Doll et al., 1994 ). Adjusted Goodness-of-fit index (AGFI) is further analysis from GFI considering the degree of freedom which is adequate when larger than 0.9 ( Bentler, 1982 ). Standardized root means square residual (SRMR) scores less than 0.05 represent a reasonable ( Jöreskog and Sörbom, 1989 ). Root Mean Square Error of Approximation (RMSEA) is recommended to be equal to/below 0.08 ( Hair, 2010 ). Table 5 shows the model comparison fit measures. Normed fit index (NFI) values range between 0 and 1, and the higher value indicates a better fit ( Ullman, 2001 ). NFI should be greater than 0.95, which is reasonable. Bentler (1990) and Schumacker and Lomax (2004) proposed that the value of NFI over 0.8 is acceptable, as it will be under loaded when analyzing with the small sample size. The non-normed fit index (NNFI) or The Tucker-Lewis Index (TLI) should be greater than 0.9 ( Bentler and Bonett, 1980 ; Hoyle, 1995 ). Relative fix index (RFI) is the extension from NFI and should be greater than 0.9 ( Bentler and Bonett, 1980 ). The comparative fit index (CFI) is similar to NFI but considers penalties. The value is typically greater than 0.9 ( Bentler and Bonett, 1980 ). Table 6 shows the model parsimonious fit measures. Hair (2010) mentioned that X 2 distribution should be less than 3 but greater than 1 would be the best scenario. Parsimonious goodness-fit-index (PGFI) and Parsimonious normed fit index (PNFI) should be greater than 0.5 ( Bentler and Bonett, 1980 ). The results showed that the computed fit indices provided strong support for the hypothesis (GFI = 0.901; AGFI = 0.912; SRMR = 0.042; RMSEA = 0.031; NFI = 0.907; NNFI = 0.905; RFI = 0.911; CFI = 0.921; X 2 /df = 1.777; PGFI = 0.676; and PNFI = 0.741.).

Model absolute fit measures.

Model comparison fit measures.

Model parsimonious fit measures.

The proposed model was evaluated, and the estimated path coefficient and p -value are presented in Figure 3 . Table 7 summarizes the hypothesis results of each measure. According to the result, Hypotheses H1, H2, H3, H5, and H7 are accepted, while H4 and H6 are rejected in the proposed model. DCM in social media is strictly related to both the perceived value of the product/service H1: β  = 0.97, p  < 0.01 and customer engagement H3: β  = 0.89, p  < 0.01. The perceived value of the product/service stipulated a significant positive relationship with immediate OPI (H2: β  = 0.87, p  < 0.01). Customer engagement indicated a strictly positive relationship with brand trust H5: β  = 0.59, p  < 0.01. And brand trust significantly affects long-term OPI H7: β  = 0.66, p  < 0.01. Although the result does not point to a direct positive relationship between DCM and brand trust, exceptional customer engagement can reinforce brand trust. The result illustrates that customer engagement has no significant direct effect on long-term OPI, while customer engagement still affects OPI through increasing brand trust. Table 8 shows the mediating effects which standardized indirect effects of mediators. As a result, perceived value partially mediated the relationship between DCM and immediate OPI. Brand trust has partially mediated the relationship between customer engagement and the long-term OPI.

Structural Equation Model (SEM) result. ***< 0.01.

Summary of the hypothesis testing results.

The mediation impact.

***< 0.01.

The findings confirm the assumption that good use of MR-based DCM could bring positive effect on both long-term and immediate OPI through mediating factors.

Immediate OPI

Digital Content Marketing delivers practical, engaging, and correct content to its leads. Therefore, potential customers who have been receiving enough details about the product/service are willing to search for more details about the product. They can capture the characteristics and quality of the product, and it can obtain a high perceived value from the DCM under the MR environment marketing description (H1). People claim that they can realize whether the product/service can fulfill their demand, and whether the product/service is worth the price. One of the most critical online shopping intentions is the product’s quality and features. Consequently, more perceived value by customers will bring more behavioral OPI (H2) as they have an excellent perception of the product and perceive less risk of online shopping. Therefore, the immediate OPI can be cultivated, consistent with the SEM results from Chinomona et al. (2013) .

Long-Term OPI

Besides achieving immediate OPI (in terms of product/service) by delivering helpful content to the leads, DCM under MR environment can cultivate trust and customer loyalty by customer engagement, finally affecting long-term OPI positively. DCM under MR environment in social media can seize the benefits, for instance, customer engagement (H3), user-generated content, and electronic word-of-mouth. The respondents acknowledge that DCM in social media is interactive and can promote positive customer experiences. In this study, a strict relationship between DCM in social media with customer engagement was found, which is in line with the previous studies conducted by Areeba et al. (2017) , Mohamad et al. (2018) , Rozina et al. (2019) , and Hartiwi et al. (2020) .

Digital Content Marketing on social media was found to have no strictly positive effect on brand trust, while it affects customer engagement, which can finally boost brand trust (H5). Companies engage their leads with continuous interaction, and this presence helps them in times of trouble. In addition, leads will be provided with customized service and offered suitable and better product choices, as firms are more familiar with their leads and are able to recognize their desires. Thus, companies are recommended to develop brand trust through excellent customer experience and other users’ positive actions (likes or shares), as customers are perceived less risk and uncertainty ( Laroche et al., 2012 ; Erik, 2019 ). The significant result between customer engagement and brand trust is in line with the conceptual framework developed by Hollebeek and Macky (2019) and the observation of Ahmad et al. (2016) . As predicted, brand trust has a positive relationship with long-term OPI (H7), which is aligned with the previous study ( Hwang and Zhang, 2018 ; Mohamad et al., 2018 ; Hollebeek and Macky, 2019 ). The participants claimed that they would have more OPI if the seller was reliable or gained positive comments from other users.

The absence of a direct positive relationship between DCM under MR environment and brand trust can be explained by the conceptual model of Hollebeek and Macky (2019) . Under the model, customer engagement is the first-tier consequence of DCM, while brand trust is the third-tier consequence. A progressive relationship exists between DCM, customer engagement, and brand trust. Therefore, DCM can enforce brand trust, mediated by customer engagement. Although the result does not indicate the significant relationship between customer engagement and long-term OPI, which has been proved in various relevant studies, customer engagement accumulated brand trust and positively affected long-term OPI. The potential customer-generated per $1,000 spent by content marketing or paid search campaign was compared in the research of Le (2013) . A paid search campaign can grasp the advantages as the company paid for the leads in the first one and a half years. However, leads from paid search campaigns are constant, but content marketing will have more rapid growth in the future because of the accumulated trust and loyalty. Content marketing can produce three times more than the paid search campaign in the last month of the third year ( Le, 2013 ). The long-tail effect of DCM under an MR-based training platform will surprise everyone, as it requires time to acquire trust between both parties and occurs rampant growth. Therefore, there is no significant direct effect between customer engagement and long-term OPI, but a strict positive relationship between brand trust and long-term OPI exists. Thus, SMEs should not give up developing DCM, even if they cannot observe powerful results initially.

Theoretical and Managerial Implications

It is no doubt that paid advertisements can reach many digital users who access the Internet through search engines, websites, social media advertisements, and video commercials on YouTube. However, they are intrusive and hard-selling and may result in annoying and negative impressions from leads, as they disturb the endless entertainment of the leads. Therefore, the viewers usually ignore the paid advertisements and close the paid advertisement page; some people even pay for the external blocker or subscribe to premium membership to avoid them ( Truong and Simmons, 2010 ). Thus, paid advertisements are an expensive investment and lack effectiveness in recent years. With MR-enabled DCM, even SMEs can achieve extraordinary sales performance from their marketing campaigns and access their targeted customers with great content through two-way communication.

Social media networks are the most popular way people can grasp information. Launching the DCM in social media can present selling messages to their targeted customers effectively and avoid the issues of the traditional paid advertisements, for instance, intrusive marketing and misleading ads. With the feature of MR, customers may get to know more about the characteristics of a product or a service. The process itself also stimulates customers’ engagement with a brand. Li et al. (2002) pointed out that reducing intrusiveness has a significant positive impact on advertising effectiveness and customer engagement. E-commerce can capture customers’ preferences and massive data during the conversation and provide customized products ( Pulizzi and Barrett, 2009 ). The longer the investment period of DCM, the more the substantial long-tail effect can be acquired ( Le, 2013 ). Therefore, the Return on Investment goes up if the companies apply successful DCM, as they no longer need to spend on useless advertisements and related rent ( Truong and Simmons, 2010 ).

Digitalization is a worldwide trend. Digital users in Hong Kong spend nearly 2.5 h on their mobile devices, an hour longer than they do on the TV. Last but not least, each person in Hong Kong had 2.3 devices and was enjoying 129.5 MB/s connection speed on average in 2017. 5G network technology has been launched, the access speed of digital content can be shortened to instant, and people can access various content more readily ( Li et al., 2020 , 2021a ). With better bandwidth and lower latency, the MR scenarios that customers can experience would be attractive in further detail. All these figures showed that digital marketing in Hong Kong has tremendous potential to grow.

According to the marketing expenditure in Hong Kong, traditional advertising has been replaced by digital marketing since 2012 ( Wong and Wei, 2018 ). The spending on the online advertisement has increased from 9% in 2012 to 32% in 2019. The total digital advertising value is now around 5.5 billion HKD ( STATISTA, 2020 ). In the same period, TV advertising, which used to have the most market share, fell to 14% in 2019. The expected budget on digital marketing would reach 34% and be more than double TV ad spending by 2021. Digital marketing involves many varieties. Following the Hong Kong Digital Marketing Statistics, leads discover an unfamiliar brand through search engines the most (35%), followed by eWOM (29%), social media ads (24%), and recommendations on social media (21%; Kemp, 2019 ). To provide the best experience over the Internet, companies should put more resources into developing digital content marketing under the MR environment. This provides valuable and engaging content to raise immediate purchase intention and build trust and long-term purchase intention. Regarding the data analysis of SEM, the effectiveness of DCM on purchase intention in terms of both immediate effect and long-tail effect were proved either through familiarity with the product/service or customer engagement. The recommendations will focus on the three most popular industries in e-commerce: Fashion and Beauty, Airline and Travel, and Electronic Products. According to the study on online retail, more than 90% of respondents, who are frequent online shoppers, sometimes or always purchase clothes online, and over 50 and 35% of respondents buy books/toys and air ticket/travel online, respectively, from the Hong Kong Consumer Council Report.

Other frequently online purchase sectors are clothing and beauty, as the products are quickly replaced by trending items in the fast fashion industry. People are confident enough to purchase branded clothing even if they cannot physically inspect or try the items. They believe branded goods have passed quality assurance and they are comforted by the fact that they can exchange unwanted items. However, customers have low confidence toward unknown brands, which usually are SMEs. Regarding the reasons for never and rarely online shopping, around 50% of respondents claimed a lack of confidence in online shopping because they have had bad experiences before and could not physically inspect the product. A questionnaire has done by the consumer council shows that 22% of interviewees are afraid of online shopping and no confidence in the product quality. Lack of confidence will cause the purchase intention of the potential customer to collapse. However, online paid ads can reach many audiences but cannot cultivate their trust in the brand. Moreover, the companies should continuously invest a relatively large amount in promotion, as they have to pay for the marketing rent. Therefore, DCM on Instagram is a better approach for SMEs to achieve promotion goals with an affordable budget.

More and more people look for flight tickets, hotel booking, and travel tours through Online Travel Agent (OTA) rather than visit the physical travel agency. OTAs provide services 24/7 from anywhere, and users can compare the prices with several OTAs simultaneously rather than visit different physical stores. Expedia, Trip.com , Trivago, and Skyscanner are examples of famous OTAs. It is no longer attractive to promote tours only through paid advertisements on the search engine. Intrepid Travel is a travel agency, which mainly offers small groups, big adventures, and responsible travel. They have currently adopted DCM, showcasing aspirational travel images posted on Instagram and Facebook taken by real travelers, Intrepid Travel, is interspersing with its content. It also allows real travelers to share their experiences, which helps the company connect more with its core audience. Last but not least, Intrepid Travel shows its enthusiasm for travel by replying to comments, which can draw the connections with the viewers as both of them have share the same passion on the adventurous travel.

Moreover, the DCM approach offers solutions for companies to reach the target audience precisely, which means the companies can reach their ideal customers through social media. Although approaching a smaller group of leads, DCM allows sellers to focus on targeted customers, easily perceive the product’s value and have greater OPI. With the DCM assisted with MR, sellers have more valuable data collected by sufficient customer engagement to improve marketing insights. For example, the number of “likes” indicates how many people are interested in a product, and their comments may involve inquiries and attitudes to the product. Thus, the sellers can strengthen their marketing tactics according to online data. In addition, future fabrications can be adjusted following the trend and the preference of potential customers. Deeper interaction with the ideal customers can improve the behavior brand attitude and result in repeat purchases ( Hollebeek and Macky, 2019 ). In particular, MR-based DCM has enormous potential to grasp a significant market share in the Hong Kong digital advertising market.

Conclusion, Limitations, and Future Research

Regarding the result of the study, both the immediate and long-term OPI has been proved. The immediate impact comes from the perceived value toward the product or service described exhaustively in the DCM under the MR-based training platform environment. Furthermore, customer engagement can cultivate brand trust and enlarge the long-term OPI due to behavioral loyalty. The effectiveness of DCM under the MR environment has been introduced segmentally. However, it may take time to see the long-tail effect of DCM under the MR-based training platform, as the companies have to accumulate leads by continuously providing unique content. An effective marketing tactic for SMEs, DCM, a section of social media marketing, is suggested to take a significant component, supported by the paid advertising on either search engines or social media. MR can be further used and extended to enhance the customers’ experience and satisfaction. Online shopping in Hong Kong is most common among young and middle-aged adults and highly educated people, perfectly fitting the respondents’ characteristics. Therefore, the results can indicate the preferences and opinions on DCM for the above group of residents. However, online shopping market and e-commerce are proliferating, and people in other age groups and education levels may also be willing to accept and adopt the digital method of purchasing. The result will no longer be sufficient to represent all online shoppers. The findings fill the gaps in the literature by providing empirical evidence for OPI boosted by DCM via social media. Therefore, future research can be extended to broader respondents, who may have different responses and preferences on DCM. Future research could extend customer engagement and trust constructs with other individual difference variables and extend to the mediating effect on the antecedents. MR’s adaptability and effectiveness to different marketing channels could be further considered. The technology acceptance model and the theory of planned behavior model could be further analyzed for new model development. The multi-group analysis considering different countries could be considered. Consumer behavior under the MR-based platform for DCM could be a new construct to analyze further and consider.

Data Availability Statement

Author contributions.

CL, OC, and KK contributed to conceptualization. OC, YC, and KK performed data curation. YC and KK carried out formal analysis, performed investigation, and contributed to project administration. CL contributed to funding acquisition. OC, PT, and KK provided methodology. CL, OC, YC, and KK provided resources. PT and KK provided software. OC performed supervision. XZ, PT, and KK carried out validation. CL, OC, YC, XZ, PT, SL, HN, and KK helped with visualization. OC, YC, PT, and KK performed writing—original draft. CL, OC, YC, PT, and KK performed writing—review and editing. All authors have read and agreed to the published version of the manuscript. All the authors contributed to the article and approved the submitted version.

The research was supported in part by the School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, China and in part by the Division of Business and Hospitality Management, College of Professional and Continuing Education, The Hong Kong Polytechnic University, Hong Kong SAR, China. The work described in this paper was partially supported by the grant from the Research Grants Council of the Hong Kong Special Administrative Region, China and Hong Kong Metropolitan University (Project No. R7016, Reference code: 2020/3003).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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