Keywords

Mosaics, Jordan, Marketing Intelligence, Self-Congruity, Brand Engagement, Category Involvement

JEL Classifications

M21, O16

Introduction

Madaba-Jordan, where the worlds' oldest mosaic map is located, is the worlds' mosaic capital and the only mosaic institute accredited by the (UNESCO - United Nations Education, Cultural and Scientific Organization) to document mosaic and Byzantine sites in the world. Here we have n=400 mosaic panels distributed over different churches (Mustafa, 2011). Tourists visit it to enjoy the beauty of the rare mosaic and take distinctive handicraft souvenirs for their families and friends (Arinat, 2016). In the light of the economic crisis that Jordan is experiencing with the whole world due to the current epidemic (-the coronavirus-), organizations must develop robust solutions to confront these economic challenges especially marketing intelligence and brand co-creation. Marketing intelligence represents one of the contemporary developments in the information systems environment to support the competitiveness of companies by obtaining comprehensive information about the customer, market, competitors, products, and environmental variables of all kinds surrounding the company, and working on updating this information continuously to improve the efficiency of these companies' performance in terms of making their strategical decisions and the ability to stay in the market and avoid the threats that could face the company, and thus marketing intelligence activities have become necessary so that companies can stay in the market for the longest possible period (Boekelder, 2018). Marketing intelligence system helps managers in the company's application of a specialized information system that carries advanced thinking and in-depth, conscious analysis of all variables to make a decision right on time, which in turn extends to achieve the essential goal that business companies seek for, which is the profit that is associated with the trust in the company. Marketing intelligence works to closely understand customers' needs, as customers are a primary source of information that companies rely on when developing or innovating new products. The role that customers play in providing companies with information and ideas has increased their importance for companies’ performance, so creating a brand with the customer's participation is one of the areas worthy of attention (Zamil, 2011).

However, despite adoption of Marketing Intelligence, the Jordanian tourism sector is confronting many marketing problems and challenges, further increased by the several environmental changes and various risks. In Jordan, the local handicraft industry reflects many barriers and declination due to decreased tourism during the current crisis, demanding severe consideration. (Ali et al., 2021; Harahsheh et al., 2019) The research problem revolves around the ongoing question of the extent to which those in charge of the Jordanian mosaics production sector realize the importance of preserving existing customers and at the same time attracting new customers, as the customer is considered capital. Furthermore, a study was conducted by (Alghizzawi et al., 2018), which highlighted the importance of marketing intelligence in creating a brand through customer participation to achieve a competitive advantage. Whereas creating a brand with the customer's participation needs up-to-date and available information for companies to be in line with the requirements and developments of the market, which can be achieved with marketing intelligence. Therefore, the study aims to explore the impact of marketing intelligence on customer brand co-creation of geographically identified products through a field study on consumers of Jordanian mosaics. Thus, the current study seeks to identify the effect of marketing intelligence in creating a brand for geographically indication products through a field study on Jordanian mosaics from the customers' point of view. Moreover, this study's theoretical importance is evidenced by considering the five dimensions of marketing intelligence represented by (customer intelligence, market intelligence, product intelligence, environmental intelligence, competitor intelligence) that may affect the creation of a brand for geographically indication products of the Jordanian mosaics.

Thus, this research will also contribute significantly to previous studies due to the shortage of Arab investigations dealing with marketing intelligence and creating a brand for geographically indicated products. On the other hand, since the intense competition in the market constitutes a solid motive to search for brand creation through marketing intelligence, it would be worth examining this problem in the context of the Jordanian mosaics producers. Therefore, this research has practical value in helping mosaics producers in Jordan study the effect of marketing intelligence in creating a brand for geographically created products. The value of this study is also increased by the recommendations that we have made to mosaics producers in Jordan by presenting an experimental model.

Literature Review & Hypotheses Development

The Relationship between Marketing Intelligence & Brand Self-Congruity

In todays’ more challenging business arenas, self-congruity is a fundamental factor that attracts customers, create brand awareness, loyalty, marketing effectiveness, brand choice and product attributes in a better possible way. In the existing literature of marketing, scholars mutually agree that customers’ buying behavior and loyalty both depend on their self-congruity. When customers are well-aware about the product image, its quality, reputation, perceived benefits, and compatibility, they prefer making the relevant buying decision (Lahoz Marco, 2017). In simple terms, customers’ own perceptions, observations, and attitudes, all are strongly aligned with their buying behavior. However, we cannot deny the importance of brand image, which is the chief component of creating brand image, attracting the customers, and help them to make favorable buying decision. To complement the findings of the previous research, it is important to search for the question that why consumers make favorable buying decisions. These studies indicate a strong, substantial role of marketers in creating self-congruity among the customers by using different tactics such as marketing intelligence (Islam et al., 2019). A study conducted by (Lu & Xu, 2015) also validated the relationship between marketing intelligence and brand self-congruity among the sports brands customers in the China. Data gathered from the intercept survey method indicated that, due to several social media marketing tactics, customers have self-congruity about these sports brands and their quality. As a result, customers prefer to stay loyal to particular brands, and continue buying from them. Here, Marketing intelligence and brand self-congruity work hand in hand as when customers have a self-concept, they evaluate the certain brand on their own. Consequently, it helps them to decide according to their demands, and the designated criteria (Kim et al., 2005). As noted by (Govoni, 2012), marketing intelligence is a powerful tool that enhance brand image, awareness, and acceptance among the customers. By using marketing intelligence, marketers can create brand awareness among the customers, positively affect their opinion, and help them to select the relevant brand.

H1: There is a statically significant impact of marketing intelligence on brand co-creation of geographical indication products of Jordanian mosaics.

The Relationship between Marketing Intelligence & Category Involvement

According to (Michaelidou & Dibb, 2008), category involvement i9s one of the leading concepts in marketing research that is accompanied by several decision-making process. Due to its profound characteristics and importance, category involvement is widely linked with other concepts such as advertising, brand switching, opinion leadership, diffusion of a product or idea, segmentation and others. Here one of the most prominent, and significant relationship is considered between brand category involvement and marketing intelligence. As marketing intelligence is a major determinant of customer behavior, reinforcing category involvement is its one of the primary objectives. Different components of marketing intelligence are focused towards persuasion, and motivating the customers to make favorable decision. For this purpose, marketing intelligence provides with different tactics, that further create category involvement among the potential customers (Kim et al., 2005; Study, 2007).

To further validate this, (Doole et al., 2006) cited an example of the United Kingdom based Small and Medium Enterprises that resort to marketing intelligence for reinforcing brand involvement among their international customers. As stated that, despite much national level customers and much efforts from the SMEs in Bradford, Yorkshire, Manchester, and London, it was difficult to create brand awareness and involvement among the international customers, to cope with this, SMEs in the relevant cities, adopted several marketing strategies which were based on marketing intelligence. After three years of struggles and efforts, nor SMEs in the United Kingdom are successfully reinforcing category involvements in their international customers. Here marketing intelligence is just not a concept or a term, instead it is comprised of several components or strategies that distinguish a product or brand from the other competitors. These strategies may involve: determining the customers’ demands, strengths and weakness of the competitors, product characteristics, and others (Douglas, 2006). A survey also resumed witnessing this notion as the researchers examined the impacts of marketing intelligence on tourism in Austria. Results revealed that, in order to keep pace with the increased challenges, Austrian tourism companies such as Kitzbühel Tourism is much resorting to social media based marketing intelligence. Through social media marketing they directly access to customers, keep an eye on their competitors marketing strategies, keep their products updated according to the customers’ criteria, and determine environmental factors that can affect the product quality, that further help Kitzbühel Tourism to upgrade their services, leading them to attain a distinguished position among their competitive organizations (Pühringer & Taylor, 2008).

H2: There is a statically significant impact of marketing intelligence on category involvement of Jordanian mosaics.

The Relationship between Marketing Intelligence & Brand Engagement

A rapidly growing business challenges demand for an instant attention towards brand engagement. Previous studies also suggest that, an increased consideration on brand engagement is also beneficial to determine customers’ perceptions, behavior, characteristics of our products, and the demands of the targeted consumers. However, much efforts in the marketing research, brand engagement needs strong attention to find out the underlying facts that can accelerate this engagement process (Hollebeek, 2011). Here the role of marketing intelligence, which reinforce customers’ engagement marketing is of greater prominence. As marketing intelligence e aims to empower, improve, accelerate the existing marketing practices, and to develop new tactics, it provides a pathway to increase the brand engagement (Housden, 2016).

As noted by (Harmeling et al., 2017), marketing intelligence helps to persuade the customers while decision-making process. If marketing intelligence is utilized and executed properly, it further facilitates the brand engagement process through two primary mechanisms: self-ownership and psychological transformation. For example, today the concept of social media marketing is of greater consideration in the United States. Brands adopting customers-centric approaches and market their products accordingly. For this purpose, they keep five contextual dimension under consideration: consumer, brand, advertising tactics, media, and customer. As a result, the more these brands keep an eye on the increased business challenges and consumer demands, the more efficiently they accelerate and sustain the brand engagement among the customers (Koot, 2016). In this regard, (Khan et al., 2019) examined the extent to which marketing intelligence indirectly influences the brand engagement in the Kingdom of Saudi Arabia. Results gathered from case study method also validated that, strategic marketing plays a key role in increasing brand engagement. Here by determining different marketing factors and challenges, organizations successfully cope with them. Consequently, boost brand quality and brand image, leading to a long-term brand engagement and loyalty among their customers. Therefore, marketing intelligence provides a pathway to achieve the customer loyalty. Here, the assumption is, if the customers are aware about the brand, they will consider trying it, once satisfied they will stay loyalty with it (Meirani & Abror, 2019). (Figure 1)

H3: There is a statically significant impact of marketing intelligence on the brand engagement of Jordanian mosaics.

Research Methods

This study relied on primary sources to collect the required information. We assessed the Validity and reliability of the data gathering tool by conducting the convergent and discriminant validity assessments using the manual calculation method, IBM Statistical Package for Social Sciences, and Microsoft Excel 2016 (Alghizzawi & Habes, 2020; Levin, 2006). Later, to test the proposed study hypotheses, we used IBM AMOS Ver 23 to select the Structural Equation Modelling as highly suitable for the current investigation (Stein et al., 2012).

Accordingly, we obtained the data through n=450 questionnaires distributed using a convenience sampling technique to mosaics customers. However, after carefully examining all the filled questionnaires, we shortlisted n=422 as n=28 of the questionnaires were incompletely or wrongly filled. Therefore, the total response rate was 93 % and the same percentage of questionnaires were further utilized for the data analysis. It is also notable that there were three sections in the questionnaire; the first section consisted of demographic information, followed by a section on marketing intelligence. In comparison, the last section represented the constructs of brand co-creation.

Respondents’ Demographics & Analysis of Variance

To calculate the respondents' primary demographics, we resorted to descriptive statistics that further facilitated the frequency and percentage calculations. As shown in Table 1 below, most of the participants were females (n=323 or 76.5%), and n=99 or 23.4% were males. Regarding the age of the participants, n=228 or 43.9% of respondents were 31-35 years old, n=132 or 25.4% were 36-40 years old, n=42 or 8.1% were 41 years old or above, and n=20 or 3.9% of them were 21-30 years of age. Similarly, the qualification of participants indicated that n=186 or 35.8% had Graduation, n=125 or 24.1% were having Post-graduation, n=101 or 19.5% were undergraduate and only n=10 or 1.9% of respondents had skilled diplomas or certification.

Table 1 Calculation of Demographical Data & Analysis of Variance
Variables	Constructs	f	%	f	Levene Statistics	Sign.
Gender	Male	99	23.40%	32.075	8.551	0
	Female	323	76.50%
Age	21-30	20	3.90%	0.776	3.433	0.608
	31-35	228	43.90%
	36-40	132	25.40%
	41 or Above	42	8.10%
Educational Level	Undergraduate	101	19.50%	14.35	12.556	0
	Graduate	186	35.80%
	Post-graduate	125	24.10%
	Diploma/Certification	10	1.90%

Here we also conducted the Analysis of Variance (ANOVA) to examine any mean discrepancies based on the respondents’ demographical characteristics (Patel, 2015). In this context, One-Way Analysis of Variance (ANOVA) did not indicate any mean differences based on the gender and qualification of the participants (p ≥ 0.000). However, with the significance value of p ≤ 0.608, we found apparent differences based on the study participants'' age groups.

Data Analysis & Results

Convergent & Divergent Validity

To strengthen the postulations and support the conceptual model, Convergent and Divergent Validity assessment provides an important pathway in Structural Equation Modelling (Eudy, 2000). For this purpose, we also conducted both convergent and discriminant reliability analyses by calculating both on SPSS and manually. Table 2 summarizes the results of convergent reliability. As shown, assessing the construct reliability indicates the Cronbach Alpha values range from 0.802 to 0.869. Composite Reliability values are also ranging from 0.798 to 0.969, surpassing the threshold value of 0.7; our research instrument is intensely reliable. Likewise, each value of Factor Loading and Average Variance Extracted (AVE) also surpassed the threshold value of 0.5, our convergent Validity is successfully established (Alnaser et al., 2020).

Table 2 Convergent & Discriminant Validity Analysis of The Research Instrument
Variables	Items	Factor Loading	AVE	CA	CR
Marketing Intelligence	MI1	0.963
	MI2	0.968
	MI3	0.912
	MI4	0.949	0.95	0.8	0.97
	MI5	0.945
Brand Self-congruity	BSC1	0.438
	BSC2	0.589
	BSC3	0.827	0.71	0.81	0.95
	BSC4	0.829
	BSC5	0.856
Category Involvement	CI1	0.872
	CV2	0.736
	CV3	0.872	0.86	0.87	0.81
	CV4	0.926
	CV5	0.873
Brand Engagement	BE1	0.934
	BE2	0.967
	BE3	0.864	0.83	0.86	0.8
	BE4	0.893
	BE5	0.701

Moreover, to affirm the discriminant validity, we also conduct Heterotrait-Monotrait and Forner-Larcker Analyses. Tables 3 & Table 4 summarized the results of discriminant validity analysis. Here we can observe that the calculated square of Average Variance Extracted values are higher than the correlation values of the other items; here, the discriminant reliability is partially established. Similarly, we also calculated the Heterotrait-Monotrait value to validate further the discriminant validity (Zaiţ & Bertea, 2011). Here we can see that the calculated HTMT value of 0.6062 is lower than the threshold value of 0.85, so the discriminant validity is fully established

Table 3 Fornell-Larcker Scale (FLS)
	MI	BSC	CI	BE
MI	0.9
BSC	0.325	0.66
CI	-0.3	0.389	0.76
BE	0.59	0.629	0.226	0.74

Table 4 Heterotrait-Monotrait Ratio Scale (HTMT)
	MI	BSC	CI	BE
MI
BSC	0.283
CI	-0.294	0.406
BE	0.59	0.63	0.23

Hypotheses Testing: Coefficient of Determination R² & Path Analysis

Analyzing the Coefficients of Determination R² is an important step in conducting Structural Equation Modelling. It facilitates the pathway to conduct the analysis and examines the predictive power of the study model (Filho et al., 2011). Thus, we also conducted the Coefficients of Determination R² in this investigation. As shown in Table 5 below that Brand Self-Congruity constitutes the R² value of 0.908, indicating that each of the variable contain approximately 9% of the other variance, strongly indicating the linearity of the research model as it is near to the threshold value of 1.0.

Table 5 Coefficients of Determination R2
Variables	R2	Strength
BSC	0.91	Strong
CI	0.89	Strong
BE	0.85	Strong

Similarly, with the R² value of the 0.886, each variable in the Category Involvement also contains approximately 80.0% of the other variance. Hus, with the total 80.8% of variance, Category Involvement also indicates a strong predictive power. Finally, with the R² value of the 0.848, we can observe that each variable in the Brand Engagement also constitutes 84.8% of variance, leading to moderately strong significant predictive power of the Brand Engagement. Therefore, as the values are ranging from 0.848-0.948; overall our conceptual model contains strong predictive capabilities (Zhang, 2017).

Later, we conducted the path analysis to determine the path values and test the evidence-based research hypotheses (Bevan, 2013). As visible in Table 6, the output of all the path values are significant, along with the significance values of the proposed hypothesis. Here we can observe that, the path value between Marketing Intelligence and Brand Self-congruity is 0.922 with the t-value of 6.039, and significance values of 0.000, the presumed relationship between Marketing Intelligence and Brand Self-congruity is strongly validated. Moreover, the path value between Marketing Intelligence and Category Involvement is 0.173, along-with the t-value of -6.294, and significance values of 0.000, the presumed relationship between Marketing Intelligence and Category Involvement is also validated.

Table 6 Path Analysis, Linear Regression Analysis
S/R	Relation	path	t-value	f-value	Sign.	Status
H1	MI≤BSC	0.922***	6.039	36.473	0	Strongly Significant
H2	MI≤CI	0.819***	-6.294	39.617	0	Strongly Significant
H5	MI≤BE	0.173***	14.477	209.585	0	Strongly Significant

Finally, the path value between Marketing Intelligence and Brand Engagement is 0.819, along-with the t-value of 14.477, and significance values of 0.000, the presumed relationship between Marketing Intelligence and Brand Engagement is also validated. Thus, we have found that there is a robust and significant relationship between Marketing Intelligence, Brand Self-congruity (B=0.172, p ≥ 0.000), Category Involvement (B=-0.187, p ≥ 0.000), and Brand Engagement (B=0.341, p ≥ 0.000). Therefore, we conclude that all of our study hypotheses are firmly accepted.

Discussion on Results & Conclusion

Creating a brand with the customer's participation is one of the obscure concepts that need clarification and experimental study. Likewise, placing customers at the heart of the equation for creating the brand will determine the success of brands between the competitors. Also, enhancing the competitive advantage by creating the brand with the customer's participation is an indicator of the success of the brand's management (Boekelder, 2018). Achieving the creation of a brand with the participation of the customer results from digital communications that started from the nineties until the present time, which enabled customers to communicate with others on networks via the Internet, share ideas, recommend improvements and modify products, and also through the exposure of the concept of "intangible exchange" based on the relationship between the client and the organization in the form of collaborative work. Then companies benefit from mutual/cooperation with customers while listening to their complaints, recommendations, and opinions about the performance of brands, furthermore, when customers cooperate and participate effectively, they can be a valuable source of specific skills and competencies whose participation may significantly enhance the performance of the company as a whole (Boekelder, 2018).

In the current article, we examined the potential relationship between marketing intelligence, self-congruity, category involvement, and brand engagement in the context of Jordanian handicrafts that primarily reliance on the tourism and marketing of the relevant products to attract more international customers (Mustafa, 2011). As in this research, N=422 study participants also indicated a significant solid relationship between the Marketing Intelligence and brand self-congruity components. These results are strongly compatible with the study conducted by (Aguirre-Rodriguez et al., 2012; Lu & Xu, 2015). They also highlighted the role of self-congruity in creating brand creation, awareness, and persuasive purchase decisions. As stated that, customers prefer those products, services and brands that they find relevant according to their needs. However, the selection process largely depends on the brand awareness and image (Islam et al., 2019). In this regard, marketing intelligence is a leading factor that is designed to persistently produce knowledge about a product, by observation, skills, experiences and implementation of different policies, that further leads to unincreased brand self-congruity among the potential customers (Jamil, 2013).

Moreover, we again found a significant solid relationship regarding the correlation between Marketing Intelligence and category involvement. These results also indicated a strong consistency with the research carried out by (Douglas, 2006; Pühringer & Taylor, 2008). Here the researchers argued that Marketing Intelligence enables n organization to gather all the essential information that can boost brand co-creation, recognition, leading to increased sales. Marketing Intelligence gathers information from the database, customers, employees, or online communities, platforms, and social networking sites. Thus resorting to Marketing Intelligence essentially facilities category involvement as it helps to maximize the depth and breadth of marketing information resources in general (Vishnoi & Bagga, 2020).

Finally, the strong and significant association between the Marketing Intelligence and brand engagement was also found. This significant relationship is found compatible with the previously conducted studies such as (Goldsmith & Emmert, 1991; Khan et al., 2019; Koot, 2016; Meirani & Abror, 2019). As noted by (Koot, 2016), customer engagement is a fundamental objective of marketing in any organization. Evidence also suggests that, an increased customer engagement with positive marketing results. Especially, in terms of marketing intelligence when customers start showing affiliation, emotional attachments and cognitive activity towards a brand, their behavior moves beyond transaction; instead their interests converts into a desire to prefer the multiple products from the same organization (Meirani & Abror, 2019). In simple terms as marketing helps to create brand recognition, which further helps to increase brand engagement. Here, Marketing Intelligence is considered as one of the four major strategies to improve brand engagement with guaranteed sustainability in the future (Kozinets, 2014). (Figure 2)

Today, tourism is the largest and fastest source of improving the Jordanian economy. Jordanian top several economic challenges (Habes, 2020), sustaining tourism and export of these products is highly questionable. As noted by (Haruhiko, 2020). The global pandemic is adversely affecting the economic condition all over the world. Especially underdeveloped countries are facing much more challenges to cope with the ever-existing economic crisis Government is much concerned about revitalizing the tourism activities by using several marketing and strategically tactics (Arinat 2016). To attract tourists and improve the local artists' living standards, Jordanian handicrafts are diverse yet require much more attention. The importance of mosaics can be estimated by the fact that handicraft production helps to sustain the national economy; also helps to support the national identity of a country (GREENER, 2008). However, during the current healthcare emergency, when the country confronts several restrictions on tourism, sustain the marketing and export of mosaics is questionable, demanding for serious considerations.

Therefore, the components of Marketing Intelligence are significantly linked with increasing brand self-congruity, category involvement, and brand engagement. This research is witnessing how these components can accelerate the marketing of Jordanian handicrafts. Notably, during the Covid-19 crisis, when tourism is facing several restrictions, and sales of these products is almost hindered (Zhang et al., 2020), further leading to the financial declination of the Jordanian economy and livelihood opportunities for the local handicraft workers.

Practical Implications

The current investigation also contains some practical implications for the policymakers and the stakeholders. First, the Government of Jordan should pay a special consideration to independent opportunities to the handcraft makers to approach the international customers without any physical barriers. Second, accessing the international customers will be accompanied by first adopting the intelligent marketing approaches. For this purpose, apart from traditional, new media resources should be taken under consideration. Third, the expansion and adoption of new techniques in handicraft making is another major phenomenon, that can accelerate the export of mosaics. Fourth, marketers should improve the customers’ brand awareness with a positive impression through advertising campaign. As people know good Jordanian mosaics, the will more likely to increase their direct involvement. Finally, to provide direct involvement, facilitating the direct communication between mosaic sellers and the buyers, can further accelerate this buying and selling process. However, all the above mentioned implications are only possible if, despite the healthcare challenges the local government may encourage the handicraft workers and sustain the marketing process especially by using the marketing intelligence techniques (Govoni, 2012).

Recommendations & Limitations

Despite the ongoing healthcare challenges, sustaining the living standard of local handicraft workers provides a two-way benefit. First, mosaics can provide employment opportunities to the local artists; second, they also help the government generate more revenue. This study also highlighted this debate and gathered the opinion of potential customers. However, this investigation also contains two primary limitations. (i) We employed convince sampling method; (ii), we did not focus on any particular handicraft, which could have narrow down this topic. Yet this article will be of greater significance for the readers, policymakers, and Jordanian government officials to take more steps for sustaining the mosaic business to attain enormous benefits even during crises. Here we recommend more studies on the relevant topic, and for this purpose, a qualitative interview-based investigation directly from the workers will bring out even more significant outcomes.

References

Rodriguez, A., Bosnjak, M., & Sirgy, M.J. (2012). Moderators of the self-congruity effect on consumer decision-making: A meta-analysis. Journal of Business Research, 65(8), 1179–1188. https://doi.org/10.1016/j.jbusres.2011.07.031

Alghizzawi, M., & Habes, M. (2020). Impacts of social media and demographical characteristics on university admissions : Case of Jordanian private universities impacts of social media and demographical characteristics on university admissions : Case of. December.

Alghizzawi, M., Salloum, S.A., & Habes, M. (2018). The role of social media in tourism marketing in Jordan. 2, 59–70.

Ali, S., Qamar, A., Derindag, O.F., Habes, M., & Youssef, E. (2021). The Mediating role of gender in ict acceptance & its impacts on students’ academic performance during Covid -19. International Journal of Advanced Trends in Computer Science and Engineering, 10(2).

Alnaser, A.S., Habes, M., Alghizzawi, M., & Ali, S. (2020). The relation among marketing ads, via digital media and mitigate (COVID-19) pandemic in Jordan. the relationship between social media and academic performance: Facebook perspective view project healthcare challenges during COVID-19 pandemic View project. Dspace.Urbe.University, July. https://www.researchgate.net/publication/342866347

Arinat, M. (2016). The status of handicrafts in Jordan: Challenges and prospects. Dirasat: Human and Social Sciences, 43, 2273–2284. https://doi.org/10.12816/0035121

Bevan, A. (2013). Chapter 8: Hypothesis testing. Statistical Data Analysis for the Physical Sciences, 114–127.

Boekelder, S. (2018). The impact of marketing intelligence on SMEs. https://essay.utwente.nl/76620/

Doole, I., Grimes, T., & Demack, S. (2006). An exploration of the management practices and processes most closely associated with high levels of export capability in SMEs. Marketing Intelligence and Planning, 24(6), 632–647. https://doi.org/10.1108/02634500610701690

Douglas, N. (2006). An examination of how product involvement affects brand loyalty a thesis submitted to auckland university of technology in partial fulfilment of the degree of master of business. December, 1–112.

Filho, D.B., Júnior, J.A., & Rocha, E.C. (2011). What is R2 all about? Leviathan (São Paulo), 3, 60. https://doi.org/10.11606/issn.2237-4485.lev.2011.132282

Goldsmith, R.E., & Emmert, J. (1991). Measuring product category involvement: A multitrait-multimethod study. Journal of Business Research, 23(4), 363–371. https://doi.org/10.1016/0148-2963(91)90021-O

Govoni, N.A. (2012). Marketing intelligence. Dictionary of Marketing Communications, January 2014. https://doi.org/10.4135/9781452229669.n2033

GREENER, M. (2008). The economic and social significance of the analysis. Between the Lines of the Balance Sheet, I, 122–134. https://doi.org/10.1016/b978-0-08-024071-8.50017-0

Habes, M. (2020). The influence of youtube videos on ELA during the COVID-19 outbreaks in Jordan. December.

Harahsheh, S., Haddad, R., & Alshorman, M. (2019). Implications of marketing Jordan as a Halal tourism destination. Journal of Islamic Marketing, 11(1), 97–116. https://doi.org/10.1108/JIMA-02-2018-0036

Harmeling, C.M., Moffett, J.W., Arnold, M.J., & Carlson, B.D. (2017). Toward a theory of customer engagement marketing. Journal of the Academy of Marketing Science, 45(3), 312–335. https://doi.org/10.1007/s11747-016-0509-2

Haruhiko, K. (2020). COVID-19 and the global economy: Impact and challenges -- from asia’s perspective -- speech at the 62nd annual meeting of the national association for business economics KURODA Haruhiko Governor of the Bank of Japan. 9.

Hollebeek, L. (2011). Exploring customer brand engagement: Definition and themes. Journal of Strategic Marketing, 19(7), 555–573. https://doi.org/10.1080/0965254X.2011.599493

Housden, M. (2016). Marketing Research and Market Intelligence. 1–82.

Islam, T., Attiq, S., Hameed, Z., Khokhar, M.N., & Sheikh, Z. (2019). The impact of self-congruity (symbolic and functional) on the brand hate: A study based on self-congruity theory. British Food Journal, 121(1), 71–88. https://doi.org/10.1108/BFJ-03-2018-0206

Jamil, G.L. (2013). Approaching market intelligence concept through a case analysis: Continuous knowledge for marketing strategic management and its complementarity to competitive intelligence. Procedia Technology, 9(Mi), 463–472. https://doi.org/10.1016/j.protcy.2013.12.051

Khan, I., Hollebeek, L.D., Fatma, M., Islam, J.U., & Rahman, Z. (2019). Brand engagement and experience in online services. Journal of Services Marketing, 34(2), 163–175. https://doi.org/10.1108/JSM-03-2019-0106

Kim, H.R., Lee, M., & Ulgado, F.M. (2005). Brand personality, self-congruity and the consumer-brand relationship. ACR Asia-Pacific Advances, AP-06. https://www.acrwebsite.org/volumes/11876/volumes/ap06/AP-06/full

Koot, J. (2016). Customer engagement. Australian Journal of Pharmacy, 97(1147), 35. https://doi.org/10.4337/9781786430281.00023

Marco, M., & Del, C. (2017). Co-creation of brand value. The new source for competitive advantage. TDX (Tesis Doctorals En Xarxa), 1–3. https://www.tesisenred.net/handle/10803/404740

Levin, K.A. (2006). Study design III: Cross-sectional studies. Evidence-Based Dentistry, 7(1), 24–25. https://doi.org/10.1038/sj.ebd.6400375

Lu, J., & Xu, Y. (2015). Chinese young consumers’ brand loyalty toward sportswear products: A perspective of self-congruity. Journal of Product and Brand Management, 24(4), 365–376. https://doi.org/10.1108/JPBM-05-2014-0593

Meirani, M., & Abror. (2019). The effect of customer brand engagement, customer satisfaction and brand love, on honda customer-based brand equity. Advances in Economics, Business and Management Research, 97(Piceeba), 341–352.

Michaelidou, N., & Dibb, S. (2008). Consumer involvement: A new perspective. The Marketing Review, 8(1), 83–99. https://doi.org/10.1362/146934708x290403

Mustafa, M. (2011). Potential of sustaining handicrafts as a tourism product in Jordan. International Journal of Business and Social Science, 2(2), 145–152.

Patel, P.A. (2015). ONE-WAY ANOVA.

Pühringer, S., & Taylor, A. (2008). A practitioner’s report on blogs as a potential source of destination marketing intelligence. Journal of Vacation Marketing, 14(2), 177–187. https://doi.org/10.1177/1356766707087524

Eudy, R. (2000). Using structural equation modeling to test for differential reliability and validity: An empirical demonstration. Structural Equation Modeling, 7(1), 124–141. https://doi.org/10.1207/S15328007SEM0701_07

Stein, C.M., Morris, N.J., Nock, N.L., & Abstract. (2012). Statistical Human Genetics, 850, Issue November 2014, 411–421. https://doi.org/10.1007/978-1-61779-555-8

Study, G. (2007). Market intelligence in large companies. Intelligence.

Zaiţ, A., & Bertea, P.E. (2011). Methods for testing discriminant validity. Management and Marketing Journal, IX(2), 217–224.

Zamil, A. (2011). The role of jordanian local community in marketing tourism. Journal of Business Studies Quarterly, 2(3), 42–49. https://search.proquest.com/docview/1017601899?accountid=14511%0Ahttps://ucl-new-primo.hosted.exlibrisgroup.com/openurl/UCL/UCL_VU2?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&genre=article&sid=ProQ:ProQ%3Aabiglobal&atitle=The+Role+of+Jor

Zhang, D. (2017). A coefficient of determination for generalized linear models. American Statistician, 71(4), 310–316. https://doi.org/10.1080/00031305.2016.1256839

Zhang, M., Fang, E., Wang, R.J.H., & Han, Y. (2020). The effects of Covid-19 on mobile app usage. 1–37. https://ssrn.com/abstract=3740433.