Online Promotional Communication Attributes and Company Competitiveness of a Malaysian Fast Fashion Clothing Company

The purpose of this research is to study the impact of online promotional communication attributes on the company's competitiveness in a Malaysian fast fashion clothing context, where the independent variables comprised online interactive communication, online content communication, online word-of-mouth communication, and online emotional communication. The Resource Based-View Theory guided the study, and the research design is based on the quantitative method. The sample size of this research was targeted 300 customers who have experienced purchasing the X Fast Fashion Retailing products through purposive sampling. The findings of this research have determined that online interactive communication, online content communication, and online emotional communication have a significant relationship with company competitiveness. In contrast, online word-of-mouth communication is not a predictor of company competitiveness. Conclusion, implications, and suggestions for future study were also discussed.

Competitiveness; Fast fashion clothing industry; Marketing communication strategies; Online promotional communication

    Clothing and fashion are one the worldwide sectors, and it continues to play an essential part in economic and commercial development (Abdallah, 2021). Many sectors, especially the fashion industry, have lately seen an increase in competition. The Internet of Things (IoT), Big Data, 3D printing, smart sensors, artificial intelligence (AI), and cloud computing are all emerging technologies for economic recovery after the Covid-19 pandemic (Berawi, 2021; Zhang, 2020). For the clothing industry, Covid-19 has been regarded as a turbulent situation as many clothing brands have yet to invest in the technology which allows the processes to be digitalized (Berg et al., 2020). This has led to a decline in the sales of these clothing brands leading to many clothing-related businesses winding down.

       In Malaysia, revenue in the apparel market amounts to US$4.70 in 2022. The market is expected to grow annually by 7.21% (CAGR 2022-2026). In addition, the apparel

market is expected to grow 8.2% in 2023 (Statista, 2022) However, there are still many challenges faced by the fast-fashion clothing industry in the Malaysian setting, for example, issues with the sustainability/ environmental impact (Rosli, 2018), digitalization and consumer shifts (Fibre2fashion, 2019), underpaid labor as the clothing industry required many workforces to manufacture the products (Sofiyyah, 2021), brand store closures due to many competitors and the unstable economy as the consequences of Covid-19 pandemic  (Kaur, 2020), where all these affect the competitiveness of the companies.
    Although numerous past research has examined digital marketing/ promotional communication in a different setting, such as Indonesia (Dwityas et al., 2020), Slovakia (Krizanova et al., 2019), South Africa (Duffett, 2017), Thailand (Yongvongphaiboon & Chantamas, 2021), which focused on various industries, such as a hotel (Krizanova et al., 2019), and fashion industry focused on the Malaysian setting have also been found (Ali et al., 2020). However, it required further investigation into Malaysian scene as Covid-19 has sparked online marketing and promotion for business-to-customers companies, which gauges a new insight for the marketing communication scholarship.
     In addition, studies on online integrated marketing communication focused on online advertising, online public relations, and online sales promotion (Al-Khattab et al., 2015). Besides, Zhang and Lin (2015) argued that businesses are adopting a consumer-centric approach in designing and executing marketing communication messages to achieve interactivity. Bowden and Mirzaei (2021) focused on digital content marketing initiatives and customer engagement, while Huete-Alcocer (2017) examined electronic word-of-mouth and its implications for consumer behavior and emotion in marketing communication has also been found (Chan-Olmsted & Wolter, 2018; Kim & Sullivan, 2019). However, those studies were examined independently, whereas studies that applied the combination of online promotional communication attributes were scared and required further investigation. This has further supported the notion of optimal integration of marketing communications, which focuses on digital and e-commerce (Agus et al., 2021; Batra & Keller, 2016; Dwivedi et al., 2021; Zagloel et al., 2021).
    This study contributed in two folds, first, this study integrated the attributes of online promotional communication that were previously tested independently and was scared to be integrated. Second, the study applies the Resource Based Theory, which views the online promotional communication attributes as the company’s resource to be competitive during the pandemic period. Therefore, the current study aims to find the impact of online promotional communication attributes on the company's competitiveness, by focusing on a Malaysian fast fashion clothing company.

2.    Literature Review

2.1. Resource-Based Theory (RBT)
    The underpinning theory used to guide the study is Resources Based Theory (RBT) (Barney, 1991; Barney et al., 2011). According to Barney (1991), the RBT focuses on the internal resources that firms require to perform specific actions and achieve sustained competitive advantages. These resources comprise “all assets, capabilities, organizational processes, firm attributes, information, knowledge, etc. controlled by a firm that enable the firm to conceive of and implement strategies that improve its efficiency and effectiveness. Thus, this research posits that a firm’s social media/ online marketing and promotional communication as a resource enhances its competitive advantage and performance (Aswani et al., 2017; Kwayu et al., 2018; Marchand et al., 2021), which can boost the marketing capability.

2.2. Hypotheses Development

Relationship between online interactive communication and company competitiveness

       According to Scorrano et al. (2019), online interactive communication is a branding strategy that engages with customers via online platforms, resulting in a long-term relationship. According to Grewal et al. (2021), online interactive communication positively impacts company competitiveness because it provides a platform for brand representatives to interact with customers, and it cognitively influences customers to be supportive of the brand in the future. Varadarajan et al. (2010), highlighted that online interactive communication technologies have a favorable impact on company competitiveness, leading to increased company competitiveness. Based on the discussion above, therefore, the study hypothesized that:

H1: There is a positive relationship between online interactive communication and company competitiveness.

Relationship between online content communication and company competitiveness

Wiktor and Sanak-Kosmowska (2021) have discovered a considerable linkage between online content communication and company competitiveness. Online content communication is a factor that can help a brand develop consumer loyalty in the market, allowing the brand to achieve competitiveness. According to Dwivedi et al. (2021), online content communication impacts company competitiveness since it can improve a company’s relationships with market consumers by raising the consumers’ awareness. Momen et al. (2020) found that online content communication is part of web-based marketing that builds brand equity and contributes to the company's competitiveness. Hence, the current study hypothesized that:

H2: There is a positive relationship between online content communication and company competitiveness.

Relationship between online word-of-mouth communication and company competitiveness

According to Konstantopoulou et al. (2018), online word-of-mouth communication positively impacts the company's competitiveness. Online word-of-mouth communication enables the actual experiences to be shared among consumers, allowing for the accurate representation of the brand. According to Siddiqui et al. (2021), online word-of-mouth communication can influence company competitiveness by influencing their online purchase intention. Bhattacharya (2016) discovered that online word-of-mouth communication is linked to influencing company competitiveness in the organization. Online word-of-mouth communication is a platform for people to express their favorable or unfavorable feelings about a brand, which affects its competitiveness. Based on the above discussion, the study hypothesized:

H3: There is a positive relationship between online word-of-mouth communication and company competitiveness.

Relationship between online emotional communication and company competitiveness

According to Alvarado-Karste and Kidwell (2021), online emotional communication is a significant perspective for developing the brand-customers relationship and increasing company competitiveness. Kim and Sullivan (2019) discovered that emotional branding allows fashion brands to form connections through storytelling that increase the company's competitiveness by winning consumers' trust and loyalty. According to Potdar et al. (2018), online emotional communication significantly impacts company competitiveness because it has a long-term relationship with the consumers. Thus, the study hypothesized that:

H4: There is a positive relationship between online emotional communication and company competitiveness.

3.1. Research Design

        The quantitative research design was utilized where, the researcher collects and analyzes the numerical data to determine the characteristics, identify the correlations, and test the hypotheses that have been established (Mohajan, 2020). The purpose of quantitative research is to identify the predictions and determine the cause-effect relationship between the variables that are being studied (Disman et al., 2017).

3.2.  Sampling Procedure

        The sampling that the researchers had conducted is purposive sampling, which allows the researchers to collect the data from the respondents (customers) who have purchased the products of X fast fashion retail clothing either online or offline. Hence, researchers include two screening questions, “Do you bought any X fast fashion products before?” and “Do you know the online platforms used by X?” to help filter out the invalid responses.

        Based on Faber and Fonseca (2014) recommendations on five factors, the minimum number of respondents would be 60 for this research. A sample size of more than 120 responders is required for quantitative analysis (Krejcie & Morgan, 1970). To comply with the research of (Krejcie & Morgan, 1970; Faber & Fonseca, 2014), the sample size for this study has 300 respondents, hence, it is deemed sufficient for statistical analysis.

3.3. Measurement

        In this research, there are five variables, which the items for company competitiveness were adapted from (Gupta et al., 2020), online interactive communication items were adapted from (Alawamleh et al., 2020), whereas for online content communication, the items were adapted from (Cortado & Chalmeta, 2016), online word-of-mouth communication from (Erkan & Evans, 2016), and contents for online emotional communication were adapted from (Annisty and Agustina, 2020). For each of the variables, a five-point Likert-type scale was used to quantify the degrees of agreement, with 1 representing strongly disagree, 2 representing disagree, 3 representing neutral, 4 representing agree, and 5 representing strongly agree.

3.4. Reliability, Validity & Normality of the variables

        According to Van-Teijlingen and Hundley (2002), the pilot test can be completed with a sample size of at least 30 respondents. Therefore, 30 people were included in this research to execute the pilot test. The outcome for the reliability is being determined based on the reference conducted by Cronbach (1951), in which he stated that the cut-off point for the variable to be regarded as reliable is more than 0.7 (Taber, 2018). As illustrated in Table 1, all the values exceeded 0.7. Hence, the data is regarded as reliable.

        The validity of the variables has been determined based on the reference to the Kaiser-Meyer-Olkin (KMO) and Bartlett’s Test of Sphericity significant value. According to Kitagawa (2015), a KMO value above 0.6 is regarded as valid, wherein the closeness to the value of 1.0 is regarded as perfect. Therefore, based on Table 1, all KMO values are above 0.6 with significant values of 0.000. Thus, this indicates that each of the variables in this research can accurately measure its intended purpose.

        The normality of the data has also been determined in Table 1 by referring to the range of skewness and kurtosis values. According to Withers and Nadarajah (2011), the data is normally distributed when the skewness is between the degree of -2 to +2 while the kurtosis value is between -7 to +7. Therefore, it indicated that the data is typically distributed for this research.

Table 1 Reliability, Validity, and Normality of the variables

3.5.   Linearity and Homoscedasticity Assumptions

        The linearity testing is vital as it provides the correct representation of the variable’s linear correlations (Hair et al., 2018). Therefore, the relationship between two metric variables can be obtained using a P-P plot. Moreover, the p-p plot has to show a linear line (Figure 2). The assumption of normality related to the supposition that the dependent variable has equal variance throughout several independent variables is referred to as homoscedasticity (Hair et al., 2018). The homoscedasticity test was conducted using the standardized residual’s P-P plot, the results showed the absence of homoscedasticity (Figure 2).

Figure 2 Linearity and Homoscedasticity Assumptions

3.6.   Data Collection Procedure 
      The data was collected by sending out questionnaires to the targeted respondents using Google Forms and using applications such as WhatsApp and WeChat as it is regarded to be a convenient way to collect the data from the respondents during the pandemic period. The confidentiality and anonymity of the respondents have been ensured by not disclosing their details and responses to any third-party sources.

4.1. Demographic Profile of the Respondents

       More than half of the respondents are female (60.3%) compared to male respondents (39.7%). On the contrary, more than half of the respondents (52.3%) are from the age group of 29 to 39. This has reflected that the respondents are primarily young adults and have purchasing power.  The time spent on social media allows the researcher to understand the behavior and browsing patterns of the customers who have purchased the X fast fashion clothing products on the social media channels. The time spent on social media between 1 to 3 hours is the highest, with 163 respondents or 54.3%. Therefore, most customers who have purchased fast fashion clothing have spent 1 to 3 hours on social media platforms daily.

4.2. Multiple Linear Regression

     The value for the Durbin-Watson in this research was 1.209, which is between the values of 1 to 3, indicating no auto-correlations arose from the generated statistical regression analysis. The value of R2 in Table 2 shows the total predictors able to explain 75.8% of the variation in the company competitiveness.

     The outcomes in Table 2 displayed that online interactive communication, online content communication, and online emotional communication have a significant relationship with company competitiveness. However, online word-of-mouth communication has no significant association with company competitiveness. Based on the findings from the coefficients table, it has indicated that H1, H2, and H4 were accepted, while H3 was rejected.

Table 2 Multiple regression analysis of competitiveness with predictor variables

    In conclusion, it has been indicated that online interactive communication, online content communication, and online emotional communication have a significant relationship with company competitiveness. However, online word-of-mouth communication has no substantial relationship with company competitiveness. Based on this research that has been conducted, a few recommendations are being suggested for future research. One of the recommendations is to further expand the variables being studied in this research, which needs to include more variables such as customers’ attitudes towards online communication, brand reputation, customer satisfaction, and customer loyalty, to name a few could be incorporated into the current model to make the conceptual model more robust. In addition, to increase the number of respondents to more than 300 that have been included in this research. This would allow for future research to gain a better representation of the sample that can impact the outcome of the study as there is a higher representation of the population. The following recommendation is to expand on the fast fashion clothing company such as Zara, Padini, and H&M and make a comparison study between those fast fashion retail clothing companies regarding their competitiveness, which will contribute significantly to the outcome of this research.

    The early version of the work has been presented at the Digital Futures International Congress (DIFCON 2022) under the International Conference on Communication, Language, Education, and Social Sciences (CLESS) 2022; at Multimedia University. The authors would also like to thank Multimedia University for providing financial support to publish this article.

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