Tuesday, September 25, 2018

Basic Concept of PLS


What is PLS? 

PLS is an acronym for Partial Least Squares. There are actually many alternatives for students to measure the reliability and validity of a model. For example, they can use either IBM software for SPSS, SmartPLS software for PLS, SEM, AMOS or other statistical software. However, during my study, I have employed PLS to measure the research model.

The process of analysing the data can be done by using the statistical software SmartPLS (Smart PLS 2.0) for Windows or PLS. In an investigation, a structural equation modelling approach using PLS technique is employed to evaluate the proposed hypotheses through the research model.

SmartPLS performs a Confirmatory Factor Analysis (CFA) while evaluating the partial least squares (PLS). The examination on CFA is done to confirm the subscales to be in the right group. The application of CFA is particularly appropriate in an investigation if there is an argument about the dimensionality or factor structure of a scale or measure (Kelloway, 1998). Hence, conducting CFA in an investigation would assist the researcher to address the issues arising in the construct validity that is related to the measurement of scale. The investigation conducts a Cronbach alpha test to solve the issues related to the scale measurement reliability. As for the last analysis, Partial Least Square Path Modeling is measured with an attempt to examine the bond between latent variables.

According to Hair et al. (2014), PLS-SEM is a prediction-oriented, variance-based approach to SEM that is based on limited predictions about the distribution of the variables. PLS-SEM denoted as PLS path modelling (PLS-PM) is primarily used to extend theories in exploratory researches by focusing on explaining the variance in the dependent variables as the researchers measured the model (Hair et al., 2013). PLS is justified to have assumptions on non-normal data, small sample sizes and formatively measured constructs (Hair et al., 2014).

Hair et al. (2013) insisted that PLS-SEM can extensively estimate the path relationships in the model using the available data as it minimizes the error terms, which is the residual variance of the endogenous constructs. The researchers further listed the data and model characteristics of PLS-SEM which included: (i) Sample size is small and or the data are non-normally distributed; (ii) Achieve a high level of statistical power with small sample sizes; (iii) Larger sample sizes increase the precision of PLS-SEM estimations; (iv) No distributional assumptions; (v) Handle extremely non-normal data; (vi) Handle construct measured with single and multi-item measures; (vii) Handle both formative and reflective measurement models equally well; (viii) Handle complex models with many structural model relations; (ix) Larger numbers of indicators are helpful in reducing the PLS-SEM bias.

Moreover, researchers also benefit from high efficiency in parameter estimation when applying PLS-SEM instead of CB-SEM (i.e. Covariance-based SEM). To sum up, the several reasons why we use PLS to calculate the measurement model are:
  • PLS can be used for theory confirmation or theory development
  • PLS makes fewer demands regarding sample size (e.g. <20)
  • PLS better than CBSEM when data were normally distributed, with a small sample size and correlated exogenous variables
  • PLS does not require normal-distributed data (CBSEM use the usual maximum likelihood estimation method, which assumes multivariate normality)
  • PLS can be applied to more complex structural equation model
  • PLS able to handle both reflective and formative construct
  • PLS is better suited for theory development than for theory testing
  • PLS is especially useful for prediction (prediction intention - prediction is more important than parameter estimation)
  • PLS has the ability to handle multicollinearity among IVs and small samples

The calculation of reliability and validity using PLS method use can be further viewed in these articles published. The influence of a moderator on the relationship also been measured in these articles:




That's all for now. 
I will update on how to measure PLS model in the next post. 


Wassalam.


Love
Dr SAA





Monday, September 24, 2018

The moderating role of technology anxiety on brand service quality, brand image and their relation to brand loyalty


Assalamu'alaikum and good day. How are you? Today I have got a chance to write a post on my blog. So, I decided to upload my latest article which has been published under Scopus-indexed journal. I'm so surprised when one of the editors from IJIMA emailed me regarding my publication. I"ve got it! Yatta! It's a Scopus-indexed journal! All bless goes to Allah Azza Wa Jalla. ^_^

In the published article, I measured how technology anxiety moderates the relationship between brand service quality and brand image on brand loyalty. I used the SmartPLS software to calculate the relationship between exogenous (independent) variables and endogenous (dependent) variable. There are two steps in measuring the moderating variable in a relationship especially when using PLS in which I'll explain it in the next post.

Some people have asked me before: 'Why I measure technology anxiety in my study? Anxiety is not in the marketing field of study but it is from the psychology field of study. How can you managed to do a research on two different fields of study?' Hmm... Like I have said before, I really interested in psychology but I majored in Marketing during my bachelor degree. So I decided to combine it (marketing + psychology) together. Basically, I want to understand the customers' emotion and feeling before we can decide which strategy suit them better when we introduce and market our products. If we fail to understand our customers, automatically our strategies planned would fail since it does not solve the problems arise.

Okay, this is the abstract of the article published.

Abstract
The increasing importance of technology in our daily lives leads companies to integrate the latest technology into their products before bringing them to the market. Since technologically advanced cars have attracted a great deal of attention, marketers use this as a means of increasing customers' level of loyalty by assuaging concerns that create a level of anxiety about the system installed in their cars. This study investigates the direct effects of brand service quality and brand image on brand loyalty as moderated by technology anxiety. It analyses 206 samples in Malaysia. Since the moderator variables are rarely tested in the context of the Partial Least Square (PLS) model, the authors analysed the data using PLS by measuring the moderating effect of technology anxiety in brand loyalty relationships. The results illustrate that technology anxiety moderates the relationship between brand image and brand loyalty.

Introduction
In this fast-growing era of technology, energy efficient vehicles have attracted a great deal of attention with a rapidly increasing customer base in the automotive industry. People look forward to products that offer advanced technological systems which can improve the ways in which they do things (i.e. while driving). The rapid growth of technology systems in the automotive industry has forced automakers to embed advanced technological systems into their manufacturing of cars in order to gain competitive advantages. This could increase the anxiety level among the automotive consumers (i.e. drivers). In the context of this investigation, understanding customers’ feelings (i.e. anxiety) towards the technology installed in their cars is an important part of understanding their intention to repeat their purchase. Osswald et al. (2012) noted that there is a high level of anxiety in the public towards technologically advanced cars that manifests in poor customer behaviour. Several studies have criticised technology anxiety from the context of mobile usage, yet few have addressed such concerns in the automotive sector. As such, this study addresses this lacuna in the literature by examining technology anxiety from the context of automotive consumers.

Industrialised countries like North America, Europe, and Japan have slow population growth, which means that customer loss can be disastrous to companies. This is due to a smaller number of available new customers to replace those who leave (Blackwell et al., 2012). From the context of this study, a slow-growing population in a developing country like Malaysia has made it difficult for automotive companies to gain new customers (MIDA, 2012). Even though the population in Malaysia increased slightly by 4.1 million between 2001 to 2009, the average annual population growth rate in Malaysia decreased from 2.3% in 2001 to 1.8% in 2009 (Department of Statistics, 2014). Therefore, retaining existing customers is the preferred and most efficient means to increase market share and profitability.

Malaysia has been overlooked due to its political instability but its markets have started to gain more and more international attention. A highly competitive business environment has urged companies to establish close and long-term relationships with their customers. In Malaysia, enhancing and maintaining brand loyalty is not easily achieved as the services offered to customers are unsatisfactory and the delivery slow, irrespective of product quality (Es, 2012). Surprisingly, a survey of the automotive consumers found that less than a third of consumers love their car’s brand while others prefer to choose a different brand in their next purchase (Harris, 2016). To overcome this problem, marketers need to focus on brand service quality to achieve a sustainable competitive advantage and customer brand loyalty (Yarimoglu, 2014). Nevertheless, improving only the qualities of a service does not guarantee the loyalty of customers as other factors such as brand image also play an important role in purchase behaviour (Fianto et al., 2014). Companies nowadays are forced to create a strong brand image for their products due to being fundamental to making a profit. Significant funds are spent on marketing a strong brand image to capture the customers’ attention and loyalty. Thus, companies need to understand the determinants of brand loyalty among existing and potential customers.

This topic is a primary concern in building a brand, especially in fast-growing and emerging markets (Meyer, 2014). It is at least five times more cost efficient to retain existing customers compared to attracting new customers (Oladele and Akeke, 2012). Brand loyalty is, however, a much used and abused term. Although it is widely utilised, many scholars investigate different determinants of customer brand loyalty, resulting in a lack of consistency in their findings (Es, 2012; Thompson et al., 2010; Sugiati et al., 2013; Kassim et al., 2014). The frequent assumption is that a satisfied customer will repeat a purchase from the same supplier (Alex and Thomas, 2011; Chinomona and Sandada, 2013; Goel, 2014). However, many other factors could influence a customer’s repeat purchase. This investigation aims to bridge the research gap by examining key factors that influence brand loyalty among the automotive consumers, as well as the moderating role of technology anxiety in strengthening the relationship between brand service quality and brand image towards brand loyalty.

Marketing researchers have studied branding since the 1950s (Bastos and Levy, 2012). They have established that it is a crucial component for increasing sales (Li and Green, 2011). Historically, brand loyalty was explained only in terms of customer behaviour (i.e. repeat purchase) and since 1969, Day offered two dimensions to explain brand loyalty, namely attitudinal and behavioural aspects. However, the insufficient findings regarding the two dimensions of customer loyalty has led researchers to study brands and branding through a composite of attitudinal and behavioural aspects (Kaur and Soch, 2013; Tabaku and Kushi, 2013). Hence, the attitudinal and behavioural aspects and their composite are necessary to understand and measure the level of brand loyalty (Chuah et al., 2014).

In an increasingly innovative and aggressive business environment, firms compete fiercely to secure an advantage. One of the key success factors is how customers perceive the quality of service (Auka et al., 2013), as it determines their level of satisfaction (IvanauskienÄ— and VolungÄ—naitÄ—, 2014). This is because a company’s profits and sales depend on the behaviour of customers (Rahman, 2014). Therefore, it is important for firms to not only improve the quality of their products to create an intention to purchase, but also to improve the quality of their services. In the past, little effort has been spent in maintaining a relationship with customers after they purchased goods in the retail business even though the brand service quality was found to encourage customers to perform repeat purchases and remain loyal to the brand (Auka et al., 2013). In evaluating the quality of the service provider, customers compare their expectations with what they received (Gilbert & Wong, 2003). However, offering high-quality service is not the only approach to increasing the level of brand loyalty among customers; anxiety towards the technology in cars also plays a vital role in influencing buyers’ brand loyalty.

In today’s business world, every company tries to catch their potential customers’ eye by displaying a strong brand image. Marketers need to focus on their customer’s feeling and emotion and how to increase their willingness to pay more. It is essential for marketers to concentrate on customers’ feelings as the customers would want to buy something that allows them to identify their identity, signal their status and manifest a sense of belonging (Marazza and Saviolo, 2012). If customers are only interested in design and price, luxury cars that offer intriguing high-tech features such as Mercedes, Lexus, Audi, BMW, etc. would record poor sales. Fortunately for those car manufacturers, millions of people feel happy and proud when driving Audi and BMW cars. The need to build a brand image that creates a relationship with their customers can be done via several communication strategies (Yilmaz & Enginkaya, 2015). This allows firms to maintain a long-term relationship with customers as a trusted brand and product (Thaichon et al., 2013) which will ultimately result in increased brand loyalty (Sivarajah and Sritharan, 2014).

This study aims to add to the scant body of knowledge by including technology anxiety as a variable to test the level of brand loyalty among automotive consumers. Similar to other industries, the use of electronic components in the automotive industry has rapidly increased as multiple aspects of driving a modern automobile is controlled by advanced technological electronics such as acceleration, braking, security, and navigation (Osswald et al., 2012). Furthermore, the automotive industry is highly competitive. Malaysian automotive marketers could benefit from the awareness of how relational variables influence the brand loyalty among customers. This study measured the levels of anxiety among automotive consumers towards the technological system installed in a car. This undertaking is motivated by Chen and Chen’s (2009) study of navigation systems whereby drivers reported facing problems managing two tasks simultaneously (i.e. following the route guidance system and driving), and that multitasking distracts drivers and creates anxiety (Rakotonirainy & Steinhardt, 2009). Meanwhile, a survey of customers who use the self-service technology revealed that customers consider the human interaction, convenience, speed of transaction, perceived accuracy, satisfaction, and trust when using the technology. The researchers, however, found stark contrasts in three areas, namely a customer’s need for employee interaction, the convenience of the self-service technology and desire for speed in the transaction (Kimes and Collier, 2015). This means that there is a high level of anxiety due to a poorly implemented self-service technology that frustrates customers. This reduces a brand’s trustworthiness and causes customers to be reluctant to repeat purchase.

Additionally, with the latest technology, auto manufacturers produce numerous fuel-efficient cars believed to be able to protect the environment. This initiative is partly in response to reports that transportation is responsible for about 20% of the global greenhouse gas emissions released into the air (Benthem and Reynaert, 2015)Consumers prefer to own a safer car which includes additional safety features such as airbags, antilock brake systems, and anti-theft alarm systems. More recently, researchers demonstrated the benefits of technology in the automotive industry, especially in providing safety in terms of information, safe environment and driving tasks assistance (Osswald et al., 2012). The message here is clear: the lower anxiety of technology increases trust in a brand, while high anxiety reduces trust in a brand. Once the customers place their trust in a brand, they intend to remain loyal to that brand. In relation to customer behaviour in technology-related industries, the relationship between the infrastructure of technology and customer intention is strengthened by technology anxiety (Yang and Forney, 2013). Hence, this investigation aims to measure the moderating role of technology anxiety on the relationship between brand service quality and brand image towards brand loyalty among automotive consumers.

Previous studies employed the Technology Acceptance Model (TAM) and Car Technology Acceptance Model (CTAM) to measure the level of anxiety among users towards technology (Osswald et al., 2012; Gelbrich and Sattler, 2014). CTAM acts as the foundation of technology anxiety with an extension of the Unified Theory of Acceptance and Use of Technology (UTAUT). The theory of UTAUT was primarily developed to explain and predict users’ acceptance towards technology from the context of the organisation. Since the UTAUT model has only been used to measure anxiety in the context of computers (Yang and Forney, 2013) and not from the context of other technological system such as technology usage in car (Osswald et al., 2012), Venkatesh et al. (2012) introduced CTAM to improve the explanatory power of the model. Hence, to predict technology anxiety in the context of customers regarding the technology system installed in the cars, this investigation revisits the predicting factors postulated by CTAM by introducing brand service quality and brand image to measure and analyse the level of technology anxiety among drivers.

The conceptual framework, research methodology, results, discussion and conclusion of this research will appear HERE.



This is the end of this post. Till we meet again in the next post. Enjoy your life and take care!

Love,
Dr SAA