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INTRODUCTION

The use of digital marketing communication (DMC) and customer relationship management (CRM) has brought revolutionary new ways of cooperating and interacting between the company and the seller, which has resulted in benefits and value in terms of efficiency, convenience, and cost reduction in consumer purchasing decisions (Jermsittiparsert et al.: 2018, pp. 1257-1273; Jermsittiparsert: 2020, pp. 338-346). DMC and CRM are the technology solution based on the personal database and salesperson's automation and marketing tools to meet the objectives (Xu et al.: 2002). This is because of the ability of DMC and CRM that can deliver customer data and increased customer retention and loyalty, reduce workflows, and distribute customer knowledge at a lower price than older network technology (Chen & Popovich: 2003). Based on past studies, communication is essential, and it has mainly positive effects on relationships.

However, the construction steel industry in Thailand has only the proportion of communication that can occur in attending business events, personal relationships, telemarketing, and face-to-face sales, which is an ineffective tool for delivering marketing objectives. Industrial companies have low interest and are holding back from adopting digital marketing compared to other companies working in the B2C market (Hazlett: 2010). Therefore, the adaption of digital marketing communication and CRM in sales and marketing departments has drawn a lot of attention among different disciplines up to the present. There is minimal information regarding ways how industrial marketing is affected by communications’ digitalization. Digital marketing communication is being used to achieve efficiencies in cost in handling customers, create social trust and brand relationships (Drèze & Hussherr: 2003, pp. 8-23).

Using modern digital channels like social media has been largely covered under consumer marketing. However, the changes that have resulted from them include ways that industrial firms can manage their channels of sales, serve their customers, establish marketing campaigns or keep in touch with their current customers remain comparatively under-researched (Vipaporn et al.: 2019, pp. 352-361; Jermsittiparsert et al.: 2019, pp. 1272-1289). The range to which industrial firms have effectively exploited the progress in digital marketing communication and CRM to improve a firm’s overall performance, such as increased sales, increased customer satisfaction, improve financial performance build customer loyalty, and reduction of firm’s activities cost is still unclear. This study aims to examine the impact of digital marketing communication and CRM on the firm and customer performance effectiveness in the B2B industrial sector. This research focuses on the B2B construction steel sector in Thailand, which is common in a typical supply chain, as companies import products such as raw materials from suppliers overseas for manufacturing processes, and then finished products can then be sold to steel distributors via B2B transactions.

Research Objectives

The study's research objectives are as follows:

Objective 1: To determine the relative importance of digital marketing communication in influencing different concepts of trust.

Objective 2: To determine the relative importance of customer relationship management in influencing different concepts of trust.

Objective 3: To understand the moderating effect of customer engagement on the effects of digital marketing and CRM on different concepts of trust

Objective 4: To investigate the mediating role of customer trust on customer and firm performance effectiveness

LITERATURE REVIEW

This study can contribute useful practice in terms of theoretical contribution to study how the input of customer relationship management (CRM) and digital marketing communication, which is amplified through the engagement process leading to different concepts of trust (social trust and custom trust), have an impact on thecustomer and firm performance. This study is among the few of its kind to include performance in both areas (firm and customer). The significance of this study is to better understand the impact of digital marketing communication and CRM technologies adoption on B2B industrial companies in Thailand. This is because when digital marketing communication combines with CRM, it will effectively help the B2B industrial companies in generating, storing, representing, translating, and reproducing information. The CRM system is implemented to help in managing customers’ relationships as well as improving how consumers understand company profiles (Gupte: 2011). Besides, CRM potentials depict the required skills and techniques that maintain, establish, and enhance meaningful relationships, and it was found through research that CRM and business performance have a direct relationship. Deductively, companies should base on existing resources to build a reliable CRM system that match expected objectives. Proper CRM techniques create opportunities through service providers hence customer satisfaction (Salem: 2010).

This study can contribute useful practice in terms of managerial contribution to create values by developing quality communication and better customer engagement through digital marketing communication and CRM in order to positively influence customer trust and lead to both firm and customer performance effective for B2B industrial. Digital marketing communication is an essential tool for industrial companies ‘development if utilized and implemented correctly. One of the most common explanations for customer behaviors to switch to rival businesses is the lack of effective communication with them. The study also focuses on the barriers of digital communication in B2B industrial companies that encounter its implementation. B2B marketers can use the findings of this study to plan their digital marketing operational processes. B2B marketers can better strategize their operational procedures, focusing on closing specified gaps and disclosing the meaningful insights for the B2B marketers, such as digital channels that customers prefer.

Hypothesis of the Study

H1: Customer relationship management (CRM) has a positive influence on customer trust

H2: Digital Marketing Communication has a positive influence on social trust

H3.1: Customer Engagement moderates the relationship between CRM and customer trust

H3.2: Customer Engagement moderates the relationship between digital marketing communication and social trust

H4: Social trust has a positive influence on customer trust

H5: Customer trust has a positive influence on customer performance effectiveness H6: Customer trust has a positive influence on firm performance effectiveness

METHODOLOGY

In this study, exploratory sequential mixed methods design has been employed in order to gather deeper insights into the influence of digital marketing communication and CRM on the firm and customer performance effectiveness. It is a method with initial qualitative procedures for data collection and analysis, followed by process of quantitative data collection and analysis that includes the final stages of the integration from the two separate lines of data (Creswell & Plano Clark: 2017). The study aims to analyze the impact of digital marketing communication and CRM on social trust, customer trust, engagement, and firm performance effectiveness. The questionnaire items were adapted from previous literature, and a pretest was conducted before the actual distribution in order to test the reliability of the questionnaire. The survey technique helps to provide researchers with sufficient data from samples for generalization of results. The statistical analysis software will be used for data analyses such as reliability tests, confirmatory factor analysis of the measurement mode, structural equation modeling analysis, and test the impact between variables as explained and hypothesized.

Sampling Procedures

This section provides the details of the sampling procedures. The unit of analysis is the client of the Permsin Steel company, which are steel producers and are also a trading company. According to the Iron and Steel Institute of Thailand report (November 11, 2019), steel industry companies in Thailand are mostly small and medium enterprises (SME) and approximately 60% of the total number of steel entrepreneurs. The remaining 40% is made up of both steel producers and suppliers that produce and distribute construction steel products such as roof, pipe, conduit, bar rebar and including various types of related secondary steel such as scrap, billet, slab, and bloom beam in order to spread the risk and save economies of scale with details as follows.:

Steel Producers: Most steel manufacturers produce many types of steel but are always focusing on producing steel products that they have considerable expertise with, and they are divided into four groups: first is the group that focuses on producing long-steel billet and iron flat bars group (a large part of which is usually extended by the production of steel pipes), second, is the group that focuses on producing steel bars/structural steel, third, is the groups that focus on producing hot rolled/cold rolled steel and forth, is the group that focuses on producing steel pipe.

Steel Traders: steel trading companies usually buy a high production volume of steel products from steel manufacturers or producers in Thailand and sell them to end-users, Government projects, and export to different countries where it has its own distribution network. Steel traders are divided into three subgroups: first, traders in scrap metal; second, traders in finished steels, such as roofs, pipes, hot and cold rolled steel; and third, traders in steel products such as construction hardware.

Prior to conducting the survey, interviews were conducted with the director manager of the steel sector from Kasikorn Bank. Two relevant questions were asked during the interviews and focused on the total number of registered steel industrial companies in Thailand and where in Thailand these companies are located. The information received revealed that the total number of all registered steel companies in Thailand is 5102 companies, of which are located in Samut Sakhon, Samut Prakan, Bangkok, Thonburi, and Rayong as these areas that state enterprises under the control of the Ministry of Industry of Thailand. The number 3313 is used as the size of the population.

RESULTS

The results of the principal component analysis indicated that all observable variables demonstrated correlation coefficients are greater than 0.4. The results of Standardized Factor Loading of all constructs varied from 0.63 to 0.89, which exceeded the recommended level of 0.4. The exploratory factors of digital marketing communication, CRM, social trust, customer trust, firm performance effectiveness, and customer performance effectiveness accounted for a combined 76.18% of the total variance. The Cronbach's alpha coefficients of digital marketing communication, CRM, customer engagement, social trust, customer trust, firm performance effectiveness, and customer performance effectiveness varied from 0.936 to 0.967, which exceeded the recommended level of 0.7. This section describes the reliability test for the observed variables. The Cronbach’s alpha results from conducting the statistical process are equivalent or higher than the acceptable level of 0.6 to0.7. the Cronbach’s alpha results from previous studies conducted in western countries compared with the results from this study.

To examine the model fit, the seven important criteria, x2/df, NFI, RFI, IFI, TLI, CFI, and RMSEA, were used to test the model fit measurement. The chi-square goodness-of-fit test, x2/df = 1.953, p <.05, p-value showed the model did not fit the data well. Given the known sensitivity of the chi-square value to the sample size, this study complements the chi-square measure with the baseline comparisons fit measures. The baseline comparisons fit indices model showed a marginal acceptable fit for the hypothesized model (x2/df = 1.924; NFI= 0.878; RFI=0.869; IFI=0.937; TLI=0.933; CFI=0.937; RMSEA=0.057).

Construct Validity

The composite reliability of factors in the measurement model of factorial structure for digital marketing communication and CRM in construction steel industries ranged from 0.817 to 0.886, which were greater than0.7. Therefore, composite reliability of measurement model of digital marketing communication and CRM in construction steel industries was provided.

According to Table 1, the variance extracted estimates of factors in the measurement model of factorial structure for digital marketing communication and CRM in construction steel industries ranged from 66% to 78%, all of which exceeded the recommended 50 percent. Therefore, the square root of the AVE of each construct was greater than the correlations between constructs. Therefore, this finding indicates that compared with other constructs, each construct shared more variance with its measurement items and that discriminant validity was good. In sum, the measurement model of factorial structure for digital marketing communication and CRM in construction steel industries indicated that the NFI, IFI, TLI, NFI, CFI values were close to 0.9 and supported its discriminant validity, convergent validity, construct reliability and construct validity

Structural Model Fitting

The configurations of the study of the structural equation model are similar to the CFA. The first process is to determine the overall model fit by means of incremental and absolute fit indicators. The next process includes evaluating the hypothesis test using the multiple square correlations and the maximum probability estimates given by the SEM results. Seven criteria, x2/df, NFI, RFI, IFI, TLI, CFI, and RMSEA, we're used to in order to examine the fit of the structural models for both firm and customer performance effectiveness as shown in Figure 2 and 3. The results of Figure 3 revealed that the chi-square goodness-of-fit test, x2/df = 1.761, p < 0.000, p-value showed the model fits the data well. The baseline comparisons fit indices model showed a marginal acceptable fit for the hypothesized model (x2/df = 1.761; NFI= 0.887; RFI=0.879; IFI=0.948; TLI=0.944; CFI=0.947; RMSEA=0.052). Also, the results of Figure 3 showed that the chi-square goodness-of-fit test, x2/df= 1.707, p < 0.000, p-value showed the model fits the data well. The baseline comparisons fit indices model showed a marginal acceptable fit for the hypothesized model (x2/df = 1.761; NFI= 0.887; RFI=0.879; IFI=0.948; TLI=0.944; CFI=0.947; RMSEA=0.052).The comparisons fit indices are close to the recommended level of 0.90, and this indicated an acceptable estimate of the proposed relationship among the constructs.

Notes: x2/df = 1.761; NFI= 0.887; RFI=0.879; IFI=0.948; TLI=0.944; CFI=0.947; RMSEA=0.052Fit Indices of Structural Equation Model for Firm Performance Effectiveness, the result of chi-square value was 1.707, and the NFI, RFI, IFI, TLI, CFI, RMSEA values raged from 0.894, 0.887, 0.953, 0.950, 0.953, and 0.050, respectively. Due to the sensitivity of the chi-square value to the large sample size, which is more than 200, this study completes the chi-square measures with the baseline comparison measures. All the baseline fit indices of the structural equation model with item parcels are close to the recommended level of 0.90. Therefore, item parcels can be used to improve the model fit of SEM for firm performance.

Notes: x2/df = 1.761; NFI= 0.887; RFI=0.879; IFI=0.948; TLI=0.944; CFI=0.947; RMSEA=0.052

The six hypotheses were tested with the structural equation model, and all relationships were presented in Table 2 and Table 3. Both structural equation models present a marginally acceptable fit for the hypothesized models that is close to 0.9.

According to Tables 4 and 5, the moderation is significant since the difference in Chi-Square value between the constrained and unconstrained model is more than 3.84, while the difference in Degrees of Freedom is 1. The results of the moderation test found that the moderator engagement level does moderate the causal effects of digital marketing communication on social trust and CRM on customer trust. The results show that engagement level is full moderation since the standardized estimate for low engagement is not significant while high engagement is significant.

DISCUSSION

The results of demographic characteristics of the respondents and their experiences using digital marketing communication and CRM show that most of them use social media (e.g., LINE, Facebook) as a main online channel to communicate and interact with their customers. In addition, according to the findings, contact management, auto data collection, and sales support are the most commonly used functionality of CRM implemented in construction steel industries. The results of this study have found support for all the proposed hypotheses. The important findings related to the hypothesized relationships are discussed and divided into five sections.

Section 1: Relative Importance of Digital Marketing Communication in Influencing Different Concepts of Trust

The results found that there is a significant relationship between digital marketing communication and social trust. Kennedy et al. affirm that when digital marketing communication strategies were enhanced, social trust also improved (Kennedy et al.: 2001, pp. 73-86). As an outcome, improving their digital marketing communication helps to enhance customer-to-company interactions and customer-to-company relationships through the exchange of information digitally (Lin et al.: 2020). Digital marketing communication, such as social media, in which businesses provided the best medium for marketing purposes, affected social trust positively (Albarran: 2013). Social media also plays a very important role in maintaining, attaining, and engaging the company's present and new customers. Social media platforms such as Facebook or LINE may not directly foster trust, but the active firm’s sales representative and marketers can increase the quantity and quality of their social ties when using these platforms by increasing social interaction with the customer, which can be achieved by pictures or sharing relevant data with customer. This can effectively create a social event resulting in increasing customer’s feelings of intimacy and connectedness (Mathew & Coye: 2017). Recent research has shown that social media marketing communication influences overall relations, and thus, has an indirect positive effect on social trust (Cavanaugh: 2017).

Section 2: Relative Importance of Customer Relationship Management in Influencing Different Concepts of Trust

The study supports the hypothesis; customer relationship management has a positive influence oncustomer trust. The correlation showcases that when customer relationship management is improved, there is a rise in customer trust in companies (Kennedy et al.: 2001, pp. 73-86) This is because the CRM system plays in vital in managing customers’ relationships as well as improving how consumers understand company profiles (Gupte: 2011). According to Thakur and Workman, CRM can be used as a comprehensive strategy that entails the process of acquiring, maintaining, and fostering cooperation with specific customers to create trust and a unique value in their interaction with the firm (Thakur & Workman: 2016, pp. 4095-4102). Furthermore, the improved customer trust contributes to increased productivity and performance of those organizations, and social media platforms play a major role. Other studies that support this view have shown that customer relationship management influences customer confidence and loyalty (Wali et al.: 2015, pp. 45-58).

Section 3: Moderating Effect of Customer Engagement on the Effects of Digital Marketing and Customer Relationship Management on Different Concepts of Trust

The study supports the hypothesis; customer engagement moderates the relationship between customer relationship management and customer trust. Therefore, when the level of engagement was enhanced and increased through the use of CRM and digital marketing communication, there was a considerable increase in customer trust. The impact of this is increased operational and organizational productivity and performance. Previous evidence supports this view and has put forward that customer engagement by customers influences the trust they build in that product (Harmeling et al.: 2017, pp. 312-335). Through this research, customer engagement was projected to lead to better brand identity, thus, more brand trust. It should always be mentioned that organizations must establish customer confidence for a business to ensure economic viability. Such a company, nevertheless, should be able to present to customers its capabilities and goals through an effective CRM approach (Kennedy et al.: 2001, pp. 73-86).

CONCLUSION

The study supports the hypothesis; customer trust has a positive influence on firm performance effectiveness. As such, organizations that focus on improving their customer trust levels using customer relationship management and digital marketing communication realize improved productivity and performance (Kennedy et al.: 2001, pp. 73-86). Customer trust is becoming essential not only for business structures but also in expanding customer expectations, and the intense rivalry requires a product distinguishing factor for performance improvement (Sarwar et al.: 2012). Companies recognize that customer confidence can promote the enhancement of the institution's sales volume. In the digital marketplace, customers are exposed to various choices in which they must determine which product to select (Harris et al.: 2017, pp. 139-158). Therefore, to achieve long- and short-term company goals, businesses need to build and retain customer trust to gain additional loyal partnerships (Leonidou et al.: 2008, pp. 92-103). The study also identified that there is a significant relationship between customer trust and customer performance effectiveness. Hence, the study supports the hypothesis; customer trust has a positive influence on customer performance effectiveness. Thus, the study established that when organizations improved their customer trust levels, their customer performance effectiveness would improve as well. Trust should be used as a mediating variable as per this theoretical research framework to impact customer loyalty and relationship development (Sarwar et al.: 2012). In other studies that support this view, it was found that businesses relied on trust and loyalty to build and maintain a long-lasting engaged client relationship. Also, from past studies, commitment and trust is a factor that retains customers in the market. In addition, in certain firms, the psychological edge of trusting customers of businesses is more important to retain existing customers than the social value (Iglesias et al.: 2020, pp. 151-166).

BIODATA

P. YONGVONGPHAIBOON: holds Ph.D. in Business Administration (major in Marketing) from Assumption University, Thailand. He is currently a managing director of Atlantic Pipe Company Limited, a part time lecturer at Siam University, Assumption University and Huachiew Chalermprakiet University, Thailand. His areas of expertise are E-Commerce, International Supply Chain Management, and Investment in Stocks.

M. CHANTAMAS: holds a Ph.D. in Business Administration (Marketing) from Assumption University, Thailand. She is the Program Faculty Member of the Ph.D. in Business Administration and lecturer in the Department of Marketing, MSME, Assumption University. Her areas of expertise are marketing, branding, gaming, social media, sustainability, and luxury branding.

BIBLIOGRAPHY

ALBARRAN, A. B (2013). “The Social Media Industries”. Routledge.

CAVANAUGH (2017). “The Role of Digital Marketing in Brand Experience”. Forbes.

CHEN, I. J, & POPOVICH, K (2003). Understanding Customer Relationship Management (CRM). Business Process Management Journal.

CRESWELL, J. W, & CLARK, V. L. P (2017). “Designing and Conducting Mixed Methods Research”. SagePublications.

DRÈZE, X, & HUSSHERR, F. X (2003). “Internet Advertising: Is Anybody Watching”? Journal of InteractiveMarketing, 17(4), pp. 8-23.

GUPTE, M (2011). “Mapping of Information Flow in Customer Relationship Management Tool”. State University ofNew York at Binghamton.

HARMELING, C. M, MOFFETT, J. W, ARNOLD, M. J, & CARLSON, B. D (2017). “Toward a Theory of CustomerEngagement Marketing”. Journal of the Academy of Marketing Science, 45(3), pp. 312-335.

HARRIS, P, KLUPPEL-STROBEL, C, & SHAKHIRY, R (2017). “An Exploration of Brand Experience Development and Management”. In Academy of Marketing Science World Marketing Congress, pp. 209-216.

HAZLETT, K (2010). “Marketing to the Social Web: How Digital Customer Communities Build Your Business”.Journal of Consumer Marketing.

IGLESIAS, O, MARKOVIC, S, BAGHERZADEH, M, & SINGH, J. J (2020). “Co-creation: A Key Link BetweenCorporate Social Responsibility, Customer Trust, and Customer Loyalty”. Journal of Business Ethics, 163(1), pp.151-166.

JERMSITTIPARSERT, K (2020). “The Moderation Effect of Supply Chain Information Technology Capabilities on the Relationship between Customer Relationship Management with Organizational Performance of Thai Restaurants and Hotels”. In Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning, pp. 338-346.

JERMSITTIPARSERT, K, SUTDUEAN, J, & SRIYAKUL, T (2018). “Social Customer Relationship Management Capabilities and Customer Relationship Performance: Moderating Role of Social Media (Face-book) Usage among Indonesian Firms”. Opcion, 34(86), pp. 1257-1273.

JERMSITTIPARSERT, K, SUTDUEAN, J, & SRIYAKUL, T (2019). “Determinants of Social Media Usage (Face- book) to Create Brand Awareness among Indonesian Consumers”. International Journal of Innovation, Creativity and Change, 5(2), pp. 1272-1289.

KENNEDY, M. S, FERRELL, L. K, & LECLAIR, D. T (2001). “Consumers' Trust of Salesperson and Manufacturer: an Empirical Study”. Journal of Business Research, 51(1), pp. 73-86.

LEONIDOU, L. C, TALIAS, M. A, & LEONIDOU, C. N (2008).” Exercised Power as a Driver of Trust and Commitmentin Cross-border Industrial Buyer–seller Relationships”. Industrial Marketing Management, 37(1), pp. 92-103.

LIN, H, GURSOY, D, & ZHANG, M (2020). “Impact of Customer-to-Customer Interactions on Overall ServiceExperience: A Social Servicescape Perspective”. International Journal of Hospitality Management, 87.

MATHEW, A, & CHESHIRE, C (2017). “Risky Business: Social Trust and Community in the Practice of Cybersecurity for Internet Infrastructure”. In Proceedings of the 50th Hawaii International Conference on System Sciences.

SALEM, S. A. S (2010). “The Effects of Customer Relationship Management (CRM) Practices in the Egyptian Mobile Telecommunications Market on Customer Satisfaction, Loyalty and Corporate Image”. Arabic Journal of Management, 30(1).

SARWAR, M. Z, ABBASI, K. S, & PERVAIZ, S (2012). “The Effect of Customer Trust on Customer Loyalty and Customer Retention: A Moderating Role of Cause Related Marketing”. Global Journal of Management and Business Research, 12(6).

THAKUR, R, & WORKMAN, L (2016). “Customer Portfolio Management (CPM) for Improved Customer Relationship Management (CRM): Are your Customers Platinum, Gold, Silver, or Bronze”? Journal of Business Research, 69(10), pp. 4095-4102.

VIPAPORN, T, PAKVICHAP, V, & JERMSITTIPARSERT, K (2019). “Analyzing the Usage of Internet of Things among Indonesian pharmaceutical Customers: Role of Social Media related Determinants”. Systematic Reviews in Pharmacy, 10(2), pp. 352-361.

WALI, A. F, WRIGHT, L. T, & UDUMA, I. A (2015). “Customer Relationship Management for Brand Commitmentand Brand Loyalty”. British Journal of Marketing Studies, 3(4), pp. 45-58.

XU, Y, YEN, D. C, LIN, B, & CHOU, D. C (2002). “Adopting Customer Relationship Management Technology".Industrial Management & Data Systems.

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