; ; ; ;
3 | 0 | | 0.761 | | 0.743 | 0.502 | DCM1: DCM under MR environment provides enough details and information about the Product/Service (e.g., the materials of the product/the functions of the product/some ideas to better utilize the product). | | | 0.765 | | 0.58 | | |
DCM2: The Product/Service described in DCM under MR environment is attractive. | | 0.751 | 0.56 |
DCM3: DCM under MR environment is relatively less intrusive than the paid-advertisement marketing campaign. | | 0.821 | 0.27 |
Perceived value of the Product/Service | 3 | 0 | | 0.863 | | 0.749 | 0.503 |
V1: I can perceive a great value of the Product/Service described in DCM. | | | 0.835 | | 0.72 | | |
V2: It is worth the price to have the Product/Service described in DCM under MR environment. | | 0.812 | 0.66 |
V3: The description in DCM under MR environment let me realized that the Product/Service can cater to my needs. | | 0.873 | 0.76 |
Immediate OPI ; ; | 5 | 0 | | 0.834 | | 0.833 | 0.621 |
IPI 1: I want to buy the Product/Service because I found it has powerful features. | | | 0.731 | | 0.53 | | |
IPI 2: The more I know the Product/Service, the more OPI on it. | | 0.671 | 0.44 |
IPI 3: I want to buy the Product/Service because I believe I can make good use of it to improve my living quality. | | 0.702 | 0.49 |
IPI 4: I want to buy the Product/Service because the excellent quality described in DCM. | | 0.773 | 0.59 |
IPI 5: I want to buy the Product/Service because I believe it can create great value. | | 0.773 | 0.59 |
Customer engagement | 3 | 0 | | 0.862 | | 0.717 | 0.499 |
CE1: DCM under MR environment is interactive that the communication between me and the company is bilateral. | | | 0.861 | | 0.73 | | |
CE2: I have different ways to contact the companies/sellers, which adopted DCM under MR environment, either like, comment, direct message, story interaction, or hashtags in social media. | | 0.892 | 0.77 |
CE3: I have positive customer experiences as I can get assistance in time. | | 0.791 | 0.83 |
Trust on seller | 4 | 0 | | 0.840 | | 0.803 | 0.505 |
T1: More communication with the editor can leverage the trust on the company. | | | 0.761 | | 0.58 | | |
T2: I can gain more Brand Trust by reviewing the comments from other users. | | 0.753 | 0.56 |
T3: The continuous interaction makes me believe the company is trustworthy and reliable. | | 0.774 | 0.59 |
T4: I believe that more customer engagement interprets the company cares what its customer wants so that they can offer a better and suitable Product/Service. | | 0.771 | 0.55 |
Long-term OPI ; | 4 | 0 | | 0.829 | | 0.798 | 0.501 |
LPI 1: I will be at ease if the company cares about their followers, for example: gives a response to any enquires in time. | | | 0.852 | | 0.73 | | |
LPI 2: The company is reliable if the company tackles the customer’s problem reasonably. | | | 0.811 | | 0.65 | | |
LPI 3: I will shop online if the seller gains positive comments from other users. | | | 0.753 | | 0.58 | | |
LPI 4: The reliable seller can leverage my OPI. | | | 0.632 | | 0.39 | | |
Discriminant validity (correlations between constructs).
Latent constructs | DCM | Perceived value | Customer engagement | Brand trust | Immediate OPI | Long-term OPI |
---|
DCM | 0.709 | | | | | |
Perceived value | 0.620 | 0.709 | | | | |
Customer engagement | 0.486 | 0.629 | 0.788 | | | |
Brand trust | 0.414 | 0.604 | 0.635 | 0.706 | | |
Immediate OPI | 0.458 | 0.535 | 0.778 | 0.628 | 0.710 | |
Long-term OPI | 0.527 | 0.528 | 0.538 | 0.632 | 0.578 | 0.708 |
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 fit | GFI | AGFI | SRMR | RMSEA |
---|
| 0.901 | 0.912 | 0.042 | 0.031 |
Model comparison fit measures.
Model fit | NFI | NNFI | RFI | CFI |
---|
| 0.907 | 0.905 | 0.911 | 0.921 |
Model parsimonious fit measures.
Model fit | /df | PGFI | PNFI |
---|
| 1.777 | 0.676 | 0.741 |
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.
Hypothesis | Path | | Sign. | -value | Result |
---|
H1 | DCM → Perceived value | 0.97 | <0.01 | 14.842 | |
H2 | Perceived value → Immediate OPI | 0.87 | <0.01 | 9.503 | |
H3 | DCM → Customer engagement | 0.89 | <0.01 | 13.146 | |
H4 | DCM → Brand trust | 0.17 | 0.35 | 0.935 | Rejected |
H5 | Customer engagement → Brand trust | 0.59 | <0.01 | 3.128 | |
H6 | Customer engagement → Long-term OPI | 0.12 | 0.252 | 1.146 | Rejected |
H7 | Brand trust → Long-term OPI | 0.66 | <0.01 | 5.685 | |
The mediation impact.
Hypothesis (indirect effect) path | Path coefficient | Result |
---|
DCM → Perceived value → Immediate OPI | 0.621 | Partial mediation |
DCM → Customer engagement → Long-term OPI | 0.462 | / |
DCM → Brand trust → Long-term OPI | 0.567 | / |
Customer engagement → Brand trust → Long-term OPI | 0.564 | Partial mediation |
***< 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.
- Abbas A. F., Jusoh A., Mas’od A., Alsharif A. H., Ali J. (2022). Bibliometrix analysis of information sharing in social media . Cogent Bus. Manag. 9 :2016556. doi: 10.1080/23311975.2021.2016556 [ CrossRef ] [ Google Scholar ]
- Ahmad N. S., Musa R., Harun M. H. M. (2016). The impact of social media content marketing (SMCM) towards brand health . Procedia Econ. Finance 37 , 331–336. doi: 10.1016/S2212-5671(16)30133-2 [ CrossRef ] [ Google Scholar ]
- Alcañiz M., Bigné E., Guixeres J. (2019). Virtual reality in marketing: a framework, review, and research agenda . Front. Psychol. 10 :1530. doi: 10.3389/fpsyg.2019.01530, PMID: [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Ali M. A., Ting D. H., Ahmad-Ur-Rahman M., Ali S., Shear F., Mazhar M. (2021). Effect of online reviews and crowd cues on restaurant choice of customer: moderating role of gender and perceived crowding . Front. Psychol. 12 :780863. doi: 10.3389/fpsyg.2021.780863, PMID: [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Amblee N., Bui T. (2011). Harnessing the influence of social proof in online shopping: the effect of electronic word of mouth on sales of digital microproducts . Int. J. Electron. Commer. 16 , 91–114. doi: 10.2753/JEC1086-4415160205 [ CrossRef ] [ Google Scholar ]
- Areeba T., Mudassir H., Talha H. (2017). The impact of social network marketing on consumer purchase intention in Pakistan: consumer engagement as a mediator . Asian J. Bus. Account. 10 , 167–199. [ Google Scholar ]
- Azer J., Alexander M. (2020). Negative customer engagement behaviour: the interplay of intensity and valence in online networks . J. Mark. Manag. 36 , 361–383. doi: 10.1080/0267257X.2020.1735488 [ CrossRef ] [ Google Scholar ]
- Bagozzi R. P., Yi Y. (2011). Specification, evaluation, and interpretation of structural equation models . J. Acad. Mark. Sci. 40 , 8–34. doi: 10.1007/s11747-011-0278-x [ CrossRef ] [ Google Scholar ]
- Bakker P. (2012). Aggregation, content farms and huffinization . J. Pract. 6 , 627–637. doi: 10.1080/17512786.2012.667266 [ CrossRef ] [ Google Scholar ]
- Barreto A. M. (2013). Do users look at banner ads on Facebook?: an international journal . J. Res. Interact. Mark. 7 , 119–139. doi: 10.1108/JRIM-Mar-2012-0013 [ CrossRef ] [ Google Scholar ]
- Bentler P. M. (1982). Confirmatory factor analysis via noniterative estimation: a fast, inexpensive method . J. Mark. Res. 19 , 417–424. doi: 10.1177/002224378201900403 [ CrossRef ] [ Google Scholar ]
- Bentler P. M. (1990). Comparative fit indexes in structural models . Psychol. Bull. 107 , 238–246. doi: 10.1037/0033-2909.107.2.238, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Bentler P. M., Bonett D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures . Psychol. Bull. 88 , 588–606. doi: 10.1037/0033-2909.88.3.588 [ CrossRef ] [ Google Scholar ]
- Bergström T., Bäckman L. (2013). Marketing and PR in Social Media: How the Utilization of Instagram Builds and Maintains Customer Relationships [Bachelors’ Thesis, Stockholms University].
- Bolat E., O’sullivan H. (2017). Radicalising the marketing of higher education: learning from student-generated social media data . J. Mark. Manag. 33 , 742–763. doi: 10.1080/0267257X.2017.1328458 [ CrossRef ] [ Google Scholar ]
- Boomsma A., Hoogland J. J. (2001). “ The robustness of LISREL modeling revisited, ” in Structural Equation Models: Present and Future. A Festschrift in Honor of Karl Jöreskog. Vol. 2 . eds. R. Cudeck, S. du Toit and D. So ¨rbom (Chicago, IL: Scientific Software International; ). 139–168. [ Google Scholar ]
- Bowden J., Mirzaei A. (2021). Consumer engagement within retail communication channels: an examination of online brand communities and digital content marketing initiatives . Eur. J. Mark. 55 , 1411–1439. doi: 10.1108/EJM-01-2018-0007 [ CrossRef ] [ Google Scholar ]
- Canhoto A. I., Vom Lehn D., Kerrigan F., Yalkin C., Braun M., Steinmetz N. (2015). Fall and redemption: monitoring and engaging in social media conversations during a crisis . Cogent Bus. Manag. 2 :1084978. doi: 10.1080/23311975.2015.1084978 [ CrossRef ] [ Google Scholar ]
- Castillo M. J., Bigne E. (2021). A model of adoption of AR-based self-service technologies: a two country comparison . Int. J. Retail Distrib. Manag. 49 , 875–898. doi: 10.1108/IJRDM-09-2020-0380 [ CrossRef ] [ Google Scholar ]
- Chen Y., Fay S., Wang Q. (2011). The role of Marketing in social media: how online consumer reviews evolve . J. Interact. Mark. 25 , 85–94. doi: 10.1016/j.intmar.2011.01.003 [ CrossRef ] [ Google Scholar ]
- Chinomona R., Okoumba L., Pooe D. (2013). The impact of product quality on perceived value, trust and students’ intention to purchase electronic gadgets . Mediterr. J. Soc. Sci. 4 :462. doi: 10.5901/mjss.2013.v4n14p463 [ CrossRef ] [ Google Scholar ]
- Consumer Council (2016). Online Retail—A Study on Hong Kong Consumer Attitudes, Business Practices and Legal Protection. ed. Consumer Council (HKSAR, China: Consumer Council; ). [ Google Scholar ]
- Corrêa S. C. H., Soares J. L., Christino J. M. M., Gosling M. D. S., Gonçalves C. A. (2020). The influence of YouTubers on followers’ use intention . J. Res. Interact. Mark. 14 , 173–194. doi: 10.1108/JRIM-09-2019-0154 [ CrossRef ] [ Google Scholar ]
- Dawes J., Nenycz-Thiel M. (2014). Comparing retailer purchase patterns and brand metrics for in-store and online grocery purchasing . J. Mark. Manag. 30 , 364–382. doi: 10.1080/0267257X.2013.813576 [ CrossRef ] [ Google Scholar ]
- Dessart L. (2017). Social media engagement: a model of antecedents and relational outcomes . J. Mark. Manag. 33 , 375–399. doi: 10.1080/0267257X.2017.1302975 [ CrossRef ] [ Google Scholar ]
- Doll W. J., Xia W., Torkzadeh G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument . MIS Q. 18 , 453–461. [ Google Scholar ]
- Erik E. V. (2019). Effects of enduring involvement and perceived content vividness on digital engagement . J. Res. Interact. Mark. 14 , 1–16. doi: 10.1108/JRIM-05-2018-0071 [ CrossRef ] [ Google Scholar ]
- Fan J., Zheng P., Li S. (2022). Vision-based holistic scene understanding towards proactive human–robot collaboration . Robot. Comput. Integr. Manuf. 75 :102304. doi: 10.1016/j.rcim.2021.102304 [ CrossRef ] [ Google Scholar ]
- Flavián C., Ibáñez-Sánchez S., Orús C. (2019). The impact of virtual, augmented and mixed reality technologies on the customer experience . J. Bus. Res. 100 , 547–560. doi: 10.1016/j.jbusres.2018.10.050 [ CrossRef ] [ Google Scholar ]
- Freeze R., Raschke R. L. (2007). “ An Assessment of Formative and Reflective Constructs in IS Research”, in ECIS 2007 Proceedings , 1481–1492.
- Goodrich K., Schiller S. Z., Galletta D. (2015). Consumer reactions to intrusiveness of online-video advertisements . J. Advert. Res. 55 , 37–50. doi: 10.2501/JAR-55-1-037-050 [ CrossRef ] [ Google Scholar ]
- Grant R., Clarke R. J., Kyriazis E. (2007). A review of factors affecting online consumer search behaviour from an information value perspective . J. Mark. Manag. 23 , 519–533. doi: 10.1362/026725707X212801 [ CrossRef ] [ Google Scholar ]
- Hair J. F. (2010). Multivariate Data Analysis. Upper Saddle River, NJ: Prentice Hall. [ Google Scholar ]
- Hajli M. (2014). A study of the impact of social media on consumers . Int. J. Mark. Res. 56 , 387–404. doi: 10.2501/IJMR-2014-025 [ CrossRef ] [ Google Scholar ]
- Hartiwi P., Ridho B., Yuniarty (2020). Student purchase intention in higher education sector: the role of social network marketing and student engagement . Manag. Sci. Lett. 10 , 103–110. [ Google Scholar ]
- Hollebeek L. D. (2011). Demystifying customer brand engagement: exploring the loyalty nexus . J. Mark. Manag. 27 , 785–807. doi: 10.1080/0267257X.2010.500132 [ CrossRef ] [ Google Scholar ]
- Hollebeek L. D., Conduit J., Brodie R. J. (2016). Strategic drivers, anticipated and unanticipated outcomes of customer engagement . J. Mark. Manag. 32 , 393–398. doi: 10.1080/0267257X.2016.1144360 [ CrossRef ] [ Google Scholar ]
- Hollebeek L. D., Macky K. (2019). Digital content marketing’s role in fostering consumer engagement, trust, and value: framework, fundamental propositions, and implications . J. Interact. Mark. 45 , 27–41. doi: 10.1016/j.intmar.2018.07.003 [ CrossRef ] [ Google Scholar ]
- Holliman G. (2014). Business to business digital content marketing: marketers’ perceptions of best practice . J. Res. Interact. Mark. 8 , 269–293. doi: 10.1108/JRIM-02-2014-0013 [ CrossRef ] [ Google Scholar ]
- Hoyle R. H. (1995). Structural Equation Modeling: Concepts, Issues, and Applications. Thousand Oaks, California: Sage Publications. [ Google Scholar ]
- Hwang K., Zhang Q. (2018). Influence of parasocial relationship between digital celebrities and their followers on followers’ purchase and electronic word-of-mouth intentions, and persuasion knowledge . Comput. Hum. Behav. 87 , 155–173. doi: 10.1016/j.chb.2018.05.029 [ CrossRef ] [ Google Scholar ]
- Jacoby J., Nelson M. C., Hoyer W. D. (1982). Corrective advertising and affirmative disclosure statements: their potential for confusing and misleading the consumer . J. Mark. 46 , 61–72. [ Google Scholar ]
- Jamil K., Dunnan L., Gul R. F., Shehzad M. U., Gillani S. H. M., Awan F. H. (2022). Role of social media marketing activities in influencing customer intentions: a perspective of a new emerging era . Front. Psychol. 12 :808525. doi: 10.3389/fpsyg.2021.808525, PMID: [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Jöreskog K. G., Sörbom D. (1989). LISREL 7: A Guide to the Program and Applications. Spss Inc., 2nd Chicago. [ Google Scholar ]
- Kareem J. A. H., Rashid W. N., Abdulla D. F., Mahmood O. K. (2016). Social media and consumer awareness toward manufactured food . Cogent Bus. Manag. 3 :1266786. doi: 10.1080/23311975.2016.1266786 [ CrossRef ] [ Google Scholar ]
- Kemp S. (2019). Digital 2019: Hong Kong. Datareportal.
- Keung K. L., Chan Y. Y., Ng K. K. H., Mak S. L., Li C. H., Qin Y., et al.. (2022a). Edge intelligence and agnostic robotic paradigm in resource synchronisation and sharing in flexible robotic and facility control system . Adv. Eng. Inform. 52 :101530. doi: 10.1016/j.aei.2022.101530 [ CrossRef ] [ Google Scholar ]
- Keung K. L., Lee C. K. M., Ji P. (2021). Data-driven order correlation pattern and storage location assignment in robotic mobile fulfillment and process automation system . Adv. Eng. Inform. 50 :101369. doi: 10.1016/j.aei.2021.101369 [ CrossRef ] [ Google Scholar ]
- Keung K. L., Lee C. K. M., Ji P. (2022b). Industrial internet of things-driven storage location assignment and order picking in a resource synchronization and sharing-based robotic mobile fulfillment system . Adv. Eng. Inform. 52 :101540. doi: 10.1016/j.aei.2022.101540 [ CrossRef ] [ Google Scholar ]
- Keung K. L., Lee C. K. M., Ji P., Ng K. K. H. (2020). Cloud-based cyber-physical robotic mobile fulfillment systems: a case study of collision avoidance . IEEE Access 8 , 89318–89336. doi: 10.1109/ACCESS.2020.2992475 [ CrossRef ] [ Google Scholar ]
- Keung K. L., Lee C. K. M., Ng K. K. H., Yeung C. K. (2018). “Smart city application and analysis: real-time urban drainage monitoring by IoT sensors: a case study of Hong Kong,” in 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) , December 16–19, 2018; 521–525.
- Khan F., Siddiqui D. K. (2013). The importance of digital marketing. An exploratory study to find the perception and effectiveness of digital marketing amongst the marketing professionals in Pakistan . J. Inf. Syst. Operat. Manag. 7 , 221–228. [ Google Scholar ]
- Koiso-Kanttila N. (2004). Digital content marketing: a literature synthesis . J. Mark. Manag. 20 , 45–65. doi: 10.1362/026725704773041122 [ CrossRef ] [ Google Scholar ]
- Kumar S. P., Gunaseelan D. (2016). Misleading advertisements and issues . Int. J. Manag. 7 , 475–483. [ Google Scholar ]
- Laroche M., Habibi M. R., Richard M.-O., Sankaranarayanan R. (2012). The effects of social media based brand communities on brand community markers, value creation practices, brand trust and brand loyalty . Comput. Hum. Behav. 28 , 1755–1767. doi: 10.1016/j.chb.2012.04.016 [ CrossRef ] [ Google Scholar ]
- Le D. (2013). Content Marketing. [Bachelors’ Thesis, Haaga-Helia University of Applied Sciences].
- Leckie C., Nyadzayo M. W., Johnson L. W. (2016). Antecedents of consumer brand engagement and brand loyalty . J. Mark. Manag. 32 , 558–578. doi: 10.1080/0267257X.2015.1131735 [ CrossRef ] [ Google Scholar ]
- Lee C. K. M., Lv Y., Ng K. K. H., Ho W., Choy K. L. (2018b). Design and application of internet of things-based warehouse management system for smart logistics . Int. J. Prod. Res. 56 , 2753–2768. doi: 10.1080/00207543.2017.1394592 [ CrossRef ] [ Google Scholar ]
- Lee C. K., Ng K. K. H., Chan H. K., Choy K. L., Tai W., Choi L. (2018a). A multi-group analysis of social media engagement and loyalty constructs between full-service and low-cost carriers in Hong Kong . J. Air Transp. Manag. 73 , 46–57. doi: 10.1016/j.jairtraman.2018.08.009 [ CrossRef ] [ Google Scholar ]
- Leong L.-Y., Hew T.-S., Lee V.-H., Ooi K.-B. (2015). An SEM–artificial-neural-network analysis of the relationships between SERVPERF, customer satisfaction and loyalty among low-cost and full-service airline . Expert Syst. Appl. 42 , 6620–6634. doi: 10.1016/j.eswa.2015.04.043 [ CrossRef ] [ Google Scholar ]
- Li H., Edwards S. M., Lee J.-H. (2002). Measuring the intrusiveness of advertisements: scale development and validation . J. Advert. 31 , 37–47. doi: 10.1080/00913367.2002.10673665 [ CrossRef ] [ Google Scholar ]
- Li S., Fan J., Zheng P., Wang L. (2021b). Transfer learning-enabled action recognition for human-robot collaborative assembly . Procedia CIRP 104 , 1795–1800. doi: 10.1016/j.procir.2021.11.303 [ CrossRef ] [ Google Scholar ]
- Li Q., Feng J., Guo J., Wang Z., Li P., Liu H., et al.. (2020). Effects of the multisensory rehabilitation product for home-based hand training after stroke on cortical activation by using NIRS methods . Neurosci. Lett. 717 :134682. doi: 10.1016/j.neulet.2019.134682, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Li Q., Ng K. K. H., Fan Z., Yuan X., Liu H., Bu L. (2021a). A human-centered approach based on functional near-infrared spectroscopy for adaptive decision-making in the air traffic control environment: a case study . Adv. Eng. Inform. 49 :101325. doi: 10.1016/j.aei.2021.101325 [ CrossRef ] [ Google Scholar ]
- Li S., Zheng P., Fan J., Wang L. (2021c). Towards proactive human robot collaborative assembly: a multimodal transfer learning-enabled action prediction approach . IEEE Trans. Ind. Electron. 69 , 8579–8588. doi: 10.1109/TIE.2021.3105977 [ CrossRef ] [ Google Scholar ]
- Li S., Zheng P., Zheng L. (2021d). An AR-assisted deep learning-based approach for automatic inspection of aviation connectors . IEEE Trans. Ind. Informat. 17 , 1721–1731. doi: 10.1109/TII.2020.3000870 [ CrossRef ] [ Google Scholar ]
- Liu C., Cao S., Tse W., Xu X. (2017). Augmented reality-assisted intelligent window for cyber-physical machine tools . J. Manuf. Syst. 44 , 280–286. doi: 10.1016/j.jmsy.2017.04.008 [ CrossRef ] [ Google Scholar ]
- Liu Y., Liu X., Wang M., Wen D. (2021). How to catch customers’ attention? A study on the effectiveness of brand social media strategies in digital customer engagement . Front. Psychol. 12 :800766. doi: 10.3389/fpsyg.2021.800766, PMID: [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Liu B., Zhang Y., Zhang G., Zheng P. (2019). Edge-cloud orchestration driven industrial smart product-service systems solution design based on CPS and IIoT . Adv. Eng. Inform. 42 :100984. doi: 10.1016/j.aei.2019.100984 [ CrossRef ] [ Google Scholar ]
- Madsen D. Ø., Slåtten K. (2015). Social media and management fashions . Cogent Bus. Manag. 2 :1122256. doi: 10.1080/23311975.2015.1122256 [ CrossRef ] [ Google Scholar ]
- Malthouse E. C., Calder B. J., Kim S. J., Vandenbosch M. (2016). Evidence that user-generated content that produces engagement increases purchase behaviours . J. Mark. Manag. 32 , 427–444. doi: 10.1080/0267257X.2016.1148066 [ CrossRef ] [ Google Scholar ]
- Marbach J., Lages C. R., Nunan D. (2016). Who are you and what do you value? Investigating the role of personality traits and customer-perceived value in online customer engagement . J. Mark. Manag. 32 , 502–525. doi: 10.1080/0267257X.2015.1128472 [ CrossRef ] [ Google Scholar ]
- Mason A. N., Brown M., Mason K., Narcum J. (2021a). Pandemic effects on social media marketing behaviors in India . Cogent Bus. Manag. 8 :1943243. doi: 10.1080/23311975.2021.1943243 [ CrossRef ] [ Google Scholar ]
- Mason A. N., Narcum J., Mason K. (2021b). Social media marketing gains importance after COVID-19 . Cogent Bus. Manag. 8 :1870797. doi: 10.1080/23311975.2020.1870797 [ CrossRef ] [ Google Scholar ]
- Mathew V., Soliman M. (2021). Does digital content marketing affect tourism consumer behavior? An extension of technology acceptance model . J. Consum. Behav. 20 , 61–75. doi: 10.1002/cb.1854 [ CrossRef ] [ Google Scholar ]
- Meyer-Waarden L., Benavent C. (2006). The impact of loyalty programmes on repeat purchase behaviour . J. Mark. Manag. 22 , 61–88. doi: 10.1362/026725706776022308 [ CrossRef ] [ Google Scholar ]
- Mohamad M., Zawawi Z. A., Hanafi W. N. W. (2018). The influences of social network marketing on student purchase intention in the digital era: the mediating role of consumer engagement . Glob. Bus. Manag. Res. 10 , 938–947. [ Google Scholar ]
- Nabec L., Pras B., Laurent G. (2016). Temporary brand–retailer alliance model: the routes to purchase intentions for selective brands and mass retailers . J. Mark. Manag. 32 , 595–627. doi: 10.1080/0267257X.2015.1111923 [ CrossRef ] [ Google Scholar ]
- Naidoo V., Hollebeek L. D. (2016). Higher education brand alliances: investigating consumers’ dual-degree purchase intentions . J. Bus. Res. 69 , 3113–3121. doi: 10.1016/j.jbusres.2016.01.027 [ CrossRef ] [ Google Scholar ]
- Papagiannidis S., Pantano E., Bourlakis M. (2013). Modelling the determinants of a simulated experience in a virtual retail store and users’ product purchasing intentions . J. Mark. Manag. 29 , 1462–1492. doi: 10.1080/0267257X.2013.821150 [ CrossRef ] [ Google Scholar ]
- Park J.-W., Robertson R., Wu C.-L. (2004). The effect of airline service quality on passengers’ behavioural intentions: a Korean case study . J. Air Transp. Manag. 10 , 435–439. doi: 10.1016/j.jairtraman.2004.06.001 [ CrossRef ] [ Google Scholar ]
- Podsakoff P. M., Mackenzie S. B., Lee J.-Y., Podsakoff N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies . J. Appl. Psychol. 88 , 879–903. doi: 10.1037/0021-9010.88.5.879, PMID: [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Pulizzi J., Barrett N. (2009). Get Content, Get Customers: Turn Prospects into Buyers with Content Marketing. New York: McGraw-Hill. [ Google Scholar ]
- Rose S., Samouel P. (2009). Internal psychological versus external market-driven determinants of the amount of consumer information search amongst online shoppers . J. Mark. Manag. 25 , 171–190. doi: 10.1362/026725709X410089 [ CrossRef ] [ Google Scholar ]
- Rowley J. (2008). Understanding digital content marketing . J. Mark. Manag. 24 , 517–540. doi: 10.1362/026725708X325977 [ CrossRef ] [ Google Scholar ]
- Rozina I., Syeda K., Maheen A., Atif A. (2019). The impact of social network marketing on consumer purchase intention in Pakistan: a study on female apparel . Manag. Sci. Lett. 9 , 1093–1104. doi: 10.5267/j.msl.2019.3.015 [ CrossRef ] [ Google Scholar ]
- Schumacker R. E., Lomax R. G. (2004). A Beginner’s Guide to Structural Equation Modeling. 2nd Edn. Mahwah, N.J.: Lawrence Erlbaum Associates. [ Google Scholar ]
- Sharma R. R., Chander S. (2011). What’s wrong with misleading advertising?—an empirical investigation . Asia Pac. Bus. Rev. 7 , 191–205. doi: 10.1177/097324701100700116 [ CrossRef ] [ Google Scholar ]
- Shiyong Z., Jiaying L., Haijian W., Dukhaykh S., Lei W., Biqing L., et al.. (2022). Do product characteristics affect customers’ participation in virtual brand communities? An empirical study . Front. Psychol. 12 :792706. doi: 10.3389/fpsyg.2021.792706, PMID: [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Siddique J., Shamim A., Nawaz M., Faye I., Rehman M. (2021). Co-creation or co-destruction: a perspective of online customer engagement valence . Front. Psychol. 11 :591753. doi: 10.3389/fpsyg.2020.591753, PMID: [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- STATISTA (2020). Digital Advertising Report 2019. STATISTA.
- Stone M. D., Woodcock N. D. (2014). Interactive, direct and digital marketing: a future that depends on better use of business intelligence . J. Res. Interact. Mark. 8 , 4–17. doi: 10.1108/JRIM-07-2013-0046 [ CrossRef ] [ Google Scholar ]
- Stouthuysen K., Teunis I., Reusen E., Slabbinck H. (2018). Initial trust and intentions to buy: the effect of vendor-specific guarantees, customer reviews and the role of online shopping experience . Electron. Commer. Res. Appl. 27 , 23–38. doi: 10.1016/j.elerap.2017.11.002 [ CrossRef ] [ Google Scholar ]
- Tafesse W., Wien A. (2018). Implementing social media marketing strategically: an empirical assessment . J. Mark. Manag. 34 , 732–749. doi: 10.1080/0267257X.2018.1482365 [ CrossRef ] [ Google Scholar ]
- Thomson E. S., Laing A. W. (2003). “The net generation”: children and young people, the internet and online shopping . J. Mark. Manag. 19 , 491–512. doi: 10.1362/026725703321663764 [ CrossRef ] [ Google Scholar ]
- Truong Y., Simmons G. (2010). Perceived intrusiveness in digital advertising: strategic marketing implications . J. Strateg. Mark. 18 , 239–256. doi: 10.1080/09652540903511308 [ CrossRef ] [ Google Scholar ]
- Tudoran A. A. (2019). Why do internet consumers block ads? New evidence from consumer opinion mining and sentiment analysis . Internet Res. 29 , 144–166. doi: 10.1108/IntR-06-2017-0221 [ CrossRef ] [ Google Scholar ]
- Ullman J. (2001). Structural Equation Modeling—Using Multivariate Statistics. Needham Heights: Allyn & Bacon. [ Google Scholar ]
- Valos M. J., Maplestone V. L., Polonsky M. J., Ewing M. (2017). Integrating social media within an integrated marketing communication decision-making framework . J. Mark. Manag. 33 , 1522–1558. doi: 10.1080/0267257X.2017.1410211 [ CrossRef ] [ Google Scholar ]
- Wang P., Mccarthy B. (2020). What do people “like” on Facebook? Content marketing strategies used by retail bank brands in Australia and Singapore . Australas. Mark. J. 29 , 155–176. doi: 10.1016/j.ausmj.2020.04.008 [ CrossRef ] [ Google Scholar ]
- Wang S., Ye Y., Ning B., Cheah J.-H., Lim X.-J. (2022). Why do some consumers still prefer in-store shopping? An exploration of online shopping cart abandonment behavior . Front. Psychol. 12 :829696. doi: 10.3389/fpsyg.2021.829696, PMID: [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Wedel M., Bigné E., Zhang J. (2020). Virtual and augmented reality: advancing research in consumer marketing . Int. J. Res. Mark. 37 , 443–465. doi: 10.1016/j.ijresmar.2020.04.004 [ CrossRef ] [ Google Scholar ]
- Wertalik D. (2017). Social media and building a connected college . Cogent Bus. Manag. 4 :1320836. doi: 10.1080/23311975.2017.1320836 [ CrossRef ] [ Google Scholar ]
- Wong E., Wei Y. (2018). Customer online shopping experience data analytics . Int. J. Retail Distrib. Manag. 46 , 406–420. doi: 10.1108/IJRDM-06-2017-0130 [ CrossRef ] [ Google Scholar ]
- Wu W.-P., Chan T. S., Lau H. H. (2008). Does consumers’ personal reciprocity affect future purchase intentions? J. Mark. Manag. 24 , 345–360. doi: 10.1362/026725708X306130 [ CrossRef ] [ Google Scholar ]
- Xia L., Zheng P., Huang X., Liu C. (2021). A novel hypergraph convolution network-based approach for predicting the material removal rate in chemical mechanical planarization. J. Intell. Manufact. doi: 10.1007/s10845-021-01784-1 [Epub ahead of print] [ CrossRef ]
- Xu Y., Chen Z., Peng M. Y.-P., Anser M. K. (2020). Enhancing consumer online purchase intention through gamification in China: perspective of cognitive evaluation theory . Front. Psychol. 11 :581200. doi: 10.3389/fpsyg.2020.581200, PMID: [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Yaghtin S., Safarzadeh H., Karimizand M. (2021). B2B digital content marketing in uncertain situations: a systematic review. J. Bus. Ind. Mark. doi: 10.1108/JBIM-03-2021-0174 [Epub ahead of print] [ CrossRef ]
- Yang S., Lin S., Carlson J. R., Ross W. T. (2016). Brand engagement on social media: will firms’ social media efforts influence search engine advertising effectiveness? J. Mark. Manag. 32 , 526–557. doi: 10.1080/0267257X.2016.1143863 [ CrossRef ] [ Google Scholar ]
- Ying L., Korneliussen T., Grønhaug K. (2009). The effect of ad value, ad placement and ad execution on the perceived intrusiveness of web advertisements . Int. J. Advert. 28 , 623–638. doi: 10.2501/S0265048709200795 [ CrossRef ] [ Google Scholar ]
- Zhang X., Zheng P., Peng T., He Q., Lee C. K. M., Tang R. (2022). Promoting employee health in smart office: a survey . Adv. Eng. Inform. 51 :101518. doi: 10.1016/j.aei.2021.101518 [ CrossRef ] [ Google Scholar ]
- Zheng P., Xia L., Li C., Li X., Liu B. (2021). Towards self-X cognitive manufacturing network: an industrial knowledge graph-based multi-agent reinforcement learning approach . J. Manuf. Syst. 61 , 16–26. doi: 10.1016/j.jmsy.2021.08.002 [ CrossRef ] [ Google Scholar ]
- DOI: 10.24182/2073-9885-2023-17-3-96-104
- Corpus ID: 271448985
Key performance indicators of marketing mix effectiveness in the pharmaceutical business
- N. A. Lagoda
- Published in Entrepreneur's Guide 22 July 2024
- Business, Medicine
2 References
Complex of digital marketing-mix: search and substantiation of the optimal theoretical construction, challenges of creating a client-oriented strategy in an online pharmaceutical organization, related papers.
Showing 1 through 3 of 0 Related Papers
- Español (Spanish)
- Français (French)
- Bahasa Indonesia (Indonesian)
- Brasil (Portuguese)
- India (English)
- हिंदी (Hindi)
- Feature Stories
- Explore All
- Subscribe page
- Submissions
- Privacy Policy
- Terms of Use
- Advertising
- Wild Madagascar
- Selva tropicales
- Mongabay.org
- Tropical Forest Network
Reforestation to capture carbon could be done much more cheaply, study says
Share this article
If you liked this story, share it with other people.
- New research shows that a mix of natural forest regrowth and tree planting could remove up to 10 times more carbon at $20 per metric ton than previously estimated by the IPCC, the U.N.’s climate science panel.
- The study found that natural regeneration is more cost-effective in 46% of suitable areas, while tree planting is better in 54%, suggesting a tailored approach could maximize carbon capture.
- Researchers estimate that using the most cost-effective method in each location could remove 31.4 billion metric tons of CO2 over 30 years at less than $50 per metric ton.
- While the findings are promising, experts caution that reforestation alone can’t solve the climate crisis and emphasize the need to consider biodiversity and other ecological factors alongside cost-effectiveness.
Trees are allies in the struggle against climate change, and regrowing forests to capture carbon may be cheaper than we thought. According to new research published in Nature Climate Change , a strategic mix of natural regrowth and tree planting could be the most cost-effective way to capture carbon.
Researchers analyzed reforestation projects in 138 low- and middle-income countries to compare the costs of different reforestation approaches. They found it’s possible to remove 10 times more carbon at $20 per metric ton, and almost three times more at $50, compared to what the Intergovernmental Panel on Climate Change (IPCC) had previously estimated .
Neither natural regeneration nor tree planting consistently outperforms the other. Instead, the most cost-effective method varies depending on local conditions. Natural regeneration, which involves letting forests regrow on their own, is cheaper in about 46% of suitable areas. Tree planting, on the other hand, is more cost-effective in 54% of areas.
“Natural regeneration is more cost-effective in areas where tree planting is expensive, regrowing forests accumulate carbon more quickly, or timber infrastructure is distant,” said lead author Jonah Busch, who conducted the study while working for Conservation International. “On the other hand, plantations outperform in areas far from natural seed sources, or where more of the carbon from harvested wood is stored in long-lasting products.”
The research team estimates that by using the cheapest method in each location, we could remove a staggering 31.4 billion metric tons of carbon dioxide from the atmosphere over 30 years, at a cost of less than $50 per metric ton. This is about 40% more carbon removal than if only one method was used universally.
“It’s exciting that the opportunity for low-cost reforestation appears much more plentiful than previously thought; this suggests reforestation projects are worth a second look by communities that might have prejudged them to be cost prohibitive,” said Busch. “While reforestation can’t be the only solution to climate change, our findings suggest it should be a bigger piece of the puzzle than previously thought.”
To reach these conclusions, the research team gathered data from hundreds of reforestation projects and used machine-learning techniques to map costs across different areas at a 1-kilometer (0.6-mile) resolution. This detailed approach allowed them to consider crucial factors such as tree growth rates and potential species in different regions.
Ecologist Robin Chazdon, who wasn’t involved in the research, praised the comprehensive approach but highlighted important considerations beyond cost-effectiveness.
“These eye-opening findings add nuance and complexity to our understanding of the net costs of carbon storage for naturally regenerating forests and monoculture plantations,” Chazdon said. However, she emphasized that “the relative costs of carbon storage should not be the only factor to consider regarding spatial planning of reforestation.”
Chazdon pointed out some of the ecological trade-offs involved in different reforestation methods. Monoculture tree plantations, while potentially cost-effective in certain areas, often create excessive water demand and provide poor opportunities for native biodiversity conservation. In contrast, naturally regenerating forests typically offer a wider range of ecosystem services and better support local biodiversity.
“Ultimately, these environmental costs and benefits — which can be difficult to monetize — need to be incorporated in decisions regarding how and where to grow plantations or foster natural regeneration,” Chazdon said.
The study’s authors acknowledge these limitations and suggest several directions for future research. They propose extending the analysis to high-income countries and exploring other forms of reforestation, such as agroforestry or planting patches of trees and allowing the rest of an area to regrow naturally.
Additionally, the researchers emphasize the need to integrate their findings on cost-effectiveness with data on biodiversity, livelihoods and other societal needs to guide reforestation efforts in different contexts.
While the study’s findings are promising, the researchers caution that reforestation alone won’t solve the climate crisis. Even at its maximum potential, reforestation would only remove as much carbon dioxide in 30 years as eight months of current global emissions.
Reforestation is very important, but it won’t solve climate change on its own, Busch said. Ultimately, “we still need to reduce emissions from fossil fuels.”
Banner image of two men planting trees in the Yokadouma Council Forest, Cameroon. Image courtesy WWF.
Liz Kimbrough is a staff writer for Mongabay and holds a Ph.D. in ecology and evolutionary biology from Tulane University, where she studied the microbiomes of trees. View more of her reporting here .
How to pick a tree-planting project? Mongabay launches transparency tool to help supporters decide
Busch, J., Bukoski, J. J., Cook-Patton, S. C., Griscom, B., Kaczan, D., Potts, M. D., … Vincent, J. R. (2024). Cost-effectiveness of natural forest regeneration and plantations for climate mitigation. Nature Climate Change , 1-7. doi: 10.1038/s41558-024-02068-1
FEEDBACK : Use this form to send a message directly to the author of this post. If you want to post a public comment, you can do that at the bottom of the page.
To wipe or to wash? That is the question
Toilet paper: Environmentally impactful, but alternatives are rolling out
Rolling towards circularity? Tracking the trace of tires
Getting the bread: What’s the environmental impact of wheat?
Consumed traces the life cycle of a variety of common consumer products from their origins, across supply chains, and waste streams. The circular economy is an attempt to lessen the pace and impact of consumption through efforts to reduce demand for raw materials by recycling wastes, improve the reusability/durability of products to limit pollution, and […]
Free and open access to credible information
Latest articles.
Biodiversity’s Tower of Babel: The confusion & disorientation of Convention on Biological Diversity Decision 15/9 (commentary)
Nepal’s buffalo-kills-tiger story reveals deeper pains in compensation system
China accepts U.N. recommendations to improve environmental conflicts in Latin America
Time to highlight South Asia’s less-studied vultures: Interview with Krishna Bhusal
As southern African freshwater fish & fisheries struggle, collaboration is key (commentary)
Logging done sustainably doesn’t have to harm ecosystem services, study finds
How a fun women’s gathering led to small wildcat conservation in Peru’s Andes
The indelible traces of oil and gas in the Peruvian, Ecuadorian and Colombian Amazon
you're currently offline
A Study of “The Growth of AI in Digital Marketing Platform”
- International Journal for Research in Applied Science and Engineering Technology 12(5):4934-4944
- 12(5):4934-4944
- This person is not on ResearchGate, or hasn't claimed this research yet.
Discover the world's research
- 25+ million members
- 160+ million publication pages
- 2.3+ billion citations
No full-text available
To read the full-text of this research, you can request a copy directly from the author.
- Dominika Kaczorowska-Spychalska
- J MED INTERNET RES
- J RETAILING
- Ming-Hui Huang
- Roland T. Rust
- J Serv Market
- Recruit researchers
- Join for free
- Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up
IMAGES
COMMENTS
Traditional marketing refers to the employment of non-digital methods to promote a corporate entity's products and services. Digital marketing refers to the promotion and sale of products and ...
A Systematic Review. Mohammed T. Nuseir , Ghaleb A. El Refae, Ahmad Aljumah, Muhammad Alshurideh , Sarah Urabi, and Barween Al Kurdi. Abstract The aim of this study is to explore the contemporary ...
This research paper describes a framework for research in digital marketing that highlights the strategies in the marketing process as well as the effectiveness of the marketing process and impact ...
Our objectives for this paper are three-fold. First, we develop and describe a framework for research in digital marketing that highlights the touchpoints in the marketing process as well as in the marketing strategy process where digital technologies are having and/or will have a significant impact. Next, we organize the developments and ...
While not a mature field yet, the body of knowledge of content marketing has grown over the last 12 years since the first scholarly paper about content marketing by Rowley (2008).Although some confusion about content marketing remains, more recent studies across different disciplines have focused on how content marketing influences online consumer behavior and the mechanisms used to achieve ...
This section synthesizes the existing literature focusing on digital and social media marketing and discusses each theme listed in Table 1 from a review of the extant literature. Studies included in this section were identified using the Scopus database by using the following combination of keywords "Social media", "digital marketing" and "social media marketing".
Research methods and scales will be presented in detail in section 4 of the paper. Research results will be presented in section 5. And an in-depth discussion in the 6 sections. Finally, the conclusion with a brief assessment of the limitations of the study as well as suggestions for future research. ... Develop an effective digital marketing ...
Despite particular research conducted on the issues related to digital marketing and marketing analytics, additional attention is needed to study the revolution and potentially disruptive nature of these domains (Petrescu and Krishen 2021, 2022).Considering the substantial impact of digital marketing and marketing analytics in the current competitive and demanding business landscape, the ...
SUBMIT PAPER. Journal of Interactive Marketing. ... Create email alert. Restricted access. Research article. First published online February 15, 2022. A Framework for Digital Marketing Research: Investigating the Four Cultural Eras of Digital Marketing ... An Empirical Investigation of Web Page Effectiveness European Journal of Marketing 32 7/8 ...
Research in this area could explore how algorithm changes impact the reach and effectiveness of digital marketing strategies. The intersection of sustainability and digital marketing is an emerging area that offers rich potential for future exploration (Thangam & Chavadi, Citation 2023 ).
Advertising through digital channels—known as Digital Marketing—is recognized for its transformative impact on companies and for its immense effect on brand-consumer relationships, as it enables interactions with customers at any time and in any place.
Design/methodology/approach. A systematic literature review has been conducted on digital marketing, and its implementation in SMEs. The impact of digital marketing on SMEs performance is observed over the past 12 years through the resources which are undertaken for the study, namely, Science Direct, Scopus, Springer, IEEE Explorer, ACM Digital Library, Engineering Village, ISI Web of ...
This paper offers views on some current and future trends in marketing. The content is based on recent literature and on what is happening in the business world ... Digital marketing is cost effective and having a great commercial impact on the business. Based on this study, it can further be argued that knowing which social media sites a ...
stomers, leads, content consumption, and brand awareness. Digital branding increases brand awareness and reaches through sharing, ial interaction.2.11.1 Previous research learning pointsDigital marketing enhances performance for Jordanian mobile operators through effective customer connection management.
Efficacy of Digital Channels: Another dominant theme is evaluating the effectiveness of various digital marketing channels like email, social media, and search engine marketing. Research compares their performance and explores strategies to optimize campaigns for each channel.
Additionally the differences between traditional marketing and digital marketing in this paper are presented. This study has described various forms of digital marketing, effectiveness of it and the impact it has on firm's sales. The examined sample consists of one hundred fifty firms and fifty executives which have been randomly selected to ...
mail to the existing or potential consumer, it is defined as email marketing. Direct digital marketing is used to send ads, to build br. nd and customer loyalty, to build customer trust and to make brand awareness. Company can promot. its products and services by using this element of digital marketing easily. It is.
ISSN (O) 2278-1021, IS SN (P) 2319- 5940. A study on effectiveness of digital marketing for. Small Business Units. Dr. Hiren Harsora1*, Dr. Anil Sharma2. 1 Assistant Professor, St. Kabir Institute ...
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 ...
This study aims to determine the level of effectiveness of digital marketing strategies used by coffee shops in Tagaytay during the Covid-19 pandemic. Specifically, the study seeks to answer the following questions: ... The research study on digital marketing strategies focused on the measurement of the level of effectiveness of different
Different tools and techniques are used to influence the purchasing decision of consumers. This case study on online marketing, research through survey and analysis of data received from respondents is still in its embryonic stage, and it is conducted to find the effectiveness of tools and techniques—online chat assistance, email ...
RESEARCH-PAPER-FINAL-FORMAT - Free download as Word Doc (.doc / .docx), PDF File (.pdf), Text File (.txt) or read online for free. This document summarizes an empirical study on the effectiveness of digital marketing as a business strategy among students at Faith Fidelis. The study aims to assess student perceptions of digital marketing's effectiveness in terms of engagement metrics like likes ...
Based on the literature review, the researchers identified the following themes: digitalization and digital marketing, digital and traditional modes of marketing, social media as a digital ...
A system of key performance indicators of the marketing mix effectiveness in the pharmaceutical business is identified and the expediency of following the «5Ps» and «6Ps» marketing mix is justified. The article is devoted to the study of determining key performance indicators of the marketing mix effectiveness in the pharmaceutical business. The problems and contradictions in ensuring the ...
According to new research published in Nature Climate Change, a strategic mix of natural regrowth and tree planting could be the most cost-effective way to capture carbon. Researchers analyzed ...
ICTCON19. STUDY OF THE EFFECTIVENESS OF DIGITAL MARKETING. Dr.C.K.Gomathy, Assistan t Professo r,Department of CSE,SC SVMV University ,Tamilnadu,India. Abstract: The media consumption in India has ...
Make informed decisions and gather insights by building effective dashboards with user-friendly, visual tools. Add, edit, and manage the types of report widgets you want to add to dashboards to quickly build datasets. Export your Workfront data into data lakes or other business intelligence (BI) tools. Learn how to measure and report
The current research paper followed a library research method in that 26 research papers related to the topic were reviewed. All the reviewed research papers were published from 2017-2023.