Academy of Marketing Studies Journal (Print ISSN: 1095-6298; Online ISSN: 1528-2678)

Research Article: 2021 Vol: 25 Issue: 4

Customer Intention To Complain Online Retail- Scale Development And Validation

George K J, BML Munjal University, Gurgaon


The online purchase platform, as an interactive channel provides the customer an instant and easily comprehensible path not only to facilitate purchase but also to broadcast their feelings and opinions about products and services. Why are some customers so reluctant to complain? Customer makes a mental assessment of the worthiness of their complaint, the likelihood of success, and the effort. What is important is not just the customer�??s likelihood of complain but the repurchase in future, customers who are dissatisfied with the services should be encouraged to voice their complain and seek redress for the same because this will provide the service provider with an opportunity both to make amends with the customer and also develop loyalty and long-term profitability. The current research contributes insights into the following areas, the factors that encourage complaint behavior, a reliable scale, validated that exhibits the multi-dimensional conceptualization of the construct �??Customer Intention to Complain�?�.


Customer Service, Customer Complaint, Complaint Intention, Loyalty, Repurchase, Online Retail, Purchase, Complaint Behavior, Attitude, Service Failure Scale Development.


Online shoppers in India are projected to get to around 300 million by 2025, as per research by Bain & Co, Chandorkar & Khambhayata (2021). A Red seer report signifies an 85% rise in online shoppers in 2020 alone as cited in the study conducted by Kumar, et al (2020), shoppers are attracted by huge discounts and deals plus the convenience and safety of remote shopping. It has come up as one of the most popular activities which happened in 2019/20. The purchasing platform provided by online retailers as a communication channel provides the customer with an instantaneous and effortlessly coherent path to enable the purchase and air their thoughts and views about products and services as per Dellarocas (2003). Even though this retail channel is growing exponentially still it is not without its challenges. Like all businesses, the online business also has its fair share of challenges. Right from finding the right product assortment that is more appealing to customers cited in the study conducted by Caruana & Ewing (2010), generating traffic towards the website which was the maim crux of the study done by Pitta, et al (2006), conversion rates and retention of the existing customers which was cited in Nemzow (1999), 50% of visitors browse the image gallery but only 20% check the detailed description given as per Unnikrishnan (2020). Despite organisations being cautious in minimalising customer complaints, there may be fallout, and dissatisfied consumers can vent their adversative encounters with their purchases online, where no cost is involved, where time and distance do not impact the complaining behaviour, contrary to the brick-and-mortar stores. This will lead to a "Complaining behaviour" either Direct to the organisation or indirect. Customers' complaining behaviour can be termed as a set of probable customer reactions to dissatisfying purchase encounters. Complaint behaviour comprises seeking redress, engaging in negative word of mouth, or even exiting. If an organisation manages these three possibilities both successfully and competently, the prospect of a complaint would automatically reduce, and the repurchase and loyalty intention would increase manifold.

So, what if the customer does not complain? It will get complicated for an organisation to quantify unsatisfied customers as enumerated in the research by Hirshman (1970) and Singh & Wilkes (1996). Customer complaints are a prized source of market intelligence, which organisations should use to comprehend and evaluate the root reason for customer dissatisfaction. Customer exiting without complaining is not only a blind spot with regards to the understanding level of satisfaction, but it also may, in the long run, impact profitability. Having a high degree of loyalty or customer satisfaction also has a surging influence on the marketing cost. Bendle, et al (2019) study stated about how developing new customers could cost almost five times more than maintaining the current ones Research shows that increasing customer retention by 5% can increase profitability from 25% to almost 95%. Further, the success rate of selling to an existing customer is as high as 60-70%, while to a new customer, it ranges from 5-20% this research done by Reichheld (2014) is highly relevant to understand why it is imperative to map unsatisfied customers. Keeping these factors in mind, organisations must understand how the “Intention to Complain" is high to avoid "Silent Exit".

Existing literature has dwelled significantly on 'customer's intention to complain' around different service industries. Scales have been developed to measure this aspect, albeit specific to the airlines as per Alotaibi (2015), and the tourism industries done by Kim (2009) and brand equity research taken up by Yoo & Donthu (2001), to name a few. Research has indicated that online consumers vary from even conventional offline consumers in several respects. Thus, a one size fitting all model may have a deceptive outcome. This study intends to plug this gap and pave the way for additional research on customer intention to complain. Additionally, to steer customer relationship managers to improve service levels and market competition concurrently, it is essential to create an appropriate tool for measuring intention to complain. The study builds and validates a multidimensional scale for measuring intention to complain online retail.

Literature Review

Consumer complaint behaviour can be characterised as a process which "constitutes a subset of all possible responses to perceived dissatisfaction around a purchase episode during consumption or possession of the goods or services" Crie (2003). It is a significant source of data for the organisation to arbiter the product quality, the price, and the brand itself. This set of information presents not only a prospect to the retailers to bring in remedial changes to their products and service but also will have a constructive influence on the consumer's behaviour consequently as mentioned in the study by Blodgett,et al (1977). A consumer complaint can also be defined as the "expression of dissatisfaction on a consumer's behalf to a responsible party" Landon (1977). On the other hand, Jacoby (1981) defines a consumer complaint as “an action taken by an individual which involves communicating something negative regarding a product or service to either the firm manufacturing or marketing the product or services or to some third-party entity”. Consumer bouncing from a specific brand could lead to a more significant consumer turnover, which would be an added cost for the organisation as it would be costly to attract a fresh consumer than to preserve the current one as cited by Hart,et al (1990). Customer complaints present an opportunity to improve business operations, strengthen customer relationships, increase customer loyalty, and increase business profitability. To achieve this, listening to customers and managing their requirements and complaints is vital as observed in Yu-Hsiang, et al (2016) study. Given the high likelihood and the easiness with which a consumer may exit the relationship and move to a separate service provider with just a click of a mouse, preserving service benchmarks is paramount as ever in this sphere as in the research done by Holloway & Sharon (2003). Companies are yet to provide adequate value to handling consumers appropriately whenever they are concerned with the services or product, and consumers are not treated efficiently as per Homburg & Fürst (2005). Retailers who get few complaints tend to believe that the consumer is generally satisfied with its product and services and would be loyal, this has been found correct in multiple study piloted by Johnston (2001), Blodgett & Haitao (2007) and Fornell & Wernerfeldt.B (1987). It is sited in Blodgett & Haitao (2007) study that once happy with their grievances, there is substantial evidence that consumers become more loyal, and hence more money accrues to the organisation due to repurchase. Scholars cite that it is not the service failure but the organisation's reaction to the service breakdown that prompts dissatisfaction Hoffman, et al.,(1995). Consumer exit or change of loyalty for a particular brand impacts the long-term revenue generated by the organisation as given in Andreassen (1999) research. Conferring to Hirshman (1970) and Fornell & Wernerfeldt.B (1987), the number of Consumers who exit the buyer-seller relationship can be reduced. If a retailer is getting fewer complaints, it may be just because instead of complaining about a defect in the product or service, the consumer switches over to the competition as per Stephens & Gwinner (1998).

Researchers propose that organisations should foster complaining behaviour among their consumers as said by Fornell & Westbrook (1984). Ndubisi & Ling (2006) study talks about how a public complaint (complain made directly to the company) made by the consumer provides the company with an opportunity to focus on it and make restitution by correctly reimbursing them and increasing retention as in. On the other hand, private complaining (complaints made to other Consumers, family members, friends, or peer groups) does not allow the company to correct the wrong, lowering the customer base as mentioned in Bearden & Oliver (1985). Based on Davidow (2003)study, we may note that organisational response to a complaint has six dimensions; these include the speed with which an organisation responds to a customer's complaint; the systems and processes that an organisation has to support the customer's complaint, encourage the customer to complain; does the organisation have an effective redressal policy, which would give the customer the confidence to believe that it was indeed worthwhile to complain; is the organisation ready to acknowledge at the first place that it's product/service was not at par with the customer's expectations; is the organisation willing to explain (elaborate as may be the case) as to why that problem happened at the first place; and finally, did the organisation show assertiveness and commitment to handle the problem.

Hence, understanding the customer's reluctance to complain or why customers behave in the way they do is vital to understand the factors that influence the intention to complain. A study by Hirshman (1970) demonstrates that a customer makes a mental assessment regarding the complaint merit on the lines of product/service dissatisfaction, probability of success (of complaining), the effort it takes to complain and the value of that product or service. The apparent likelihood to get compensation for the complaint lodged is recognised as an essential determinant of complaint voicing found in study conducted by both Blodgett & Anderson (2000) and Day (1984).

The part played by personality attributes in forecasting the complaining behaviour of a consumer-first came to the fore in the 1980s. Bearden & Mason (1984), Moyer (1984) and Richins (1983) found that extraversion and complaint propensity was positively related. The author stated that nonassertive people seem to be more concerned about voicing their complaints regarding unsatisfactory experience in the research. Keng, et al., (1985) found a positive relationship between assertiveness and complaining behaviour. Davidow (2003) mentioned that attitude for complaining is the response of a customer who is displeased with the product or service and to achieve reparation by getting into the act of complaining.

Deleterious emotions like discontent, annoyance or unhappiness are front-runners to the stimulation of complaining behaviour among the consumers as interpreted in the study done by Giese & Cote (2000). Emotional Consumer conduct can also be an amalgamation of antagonism, frustration, sadness, and numerous another undesirable emotional state which cartels to generate adverse response amongst the service provider as in Dallimore et al., (2007).

Suppose the consumer has gone through a previous service failure experience (Prior Complaint Experience). In that case, they learn about the mechanisms, options, prior experiences that will generate a positive attitude towards complaining as per Singh & Wilkes (1996). In addition, those consumers who have gone through a previous complaints experience might conclude how an organisation might retort to the expressed complaints and the linked cost and paybacks attached to the same, the result was noted in study done by Kim et al., (2003). Customer connects more weight to prior positive brand experiences than newly gathered information as gathered in the research done by Krautz & Hoffmann (2017). Day & Landon (1976) research mentioned that another critical reason that urges the choice to pursue or not to pursue compensation is the perceived likelihood of success. The probability of success denotes the consumer's perception of the retailer's willingness to remedy the problem minus the difficulty as stated by Richins (1983). In the model proposed by Blodgett, et al.(1993) for complaining behaviour for the dissatisfied consumer who sought a redress, they mention four factors, Stability/Controllability, Attitude towards complaining, Likelihood of the success of the complaint being made, and the importance of the Product hold in the consumers thought process. Experiments also reveal that customer response to service failure can also be linked to demographic variables like age, education income, these parameters are used in the study conducted by Day (1984) and Jacoby (1981). Age is often used to foresee complaining behaviour. However, its impact is usually invisible, Bearden, et al., (1979) found that age negatively relays to complaint behaviour in the automobile industry. In a universal framework, age has displayed to associate with public complaints positively. Older consumers are predictable for publicly complain more than the younger ones as they have amassed more information and understanding in dealing with the complaint or service failure scenarios this is refered to by Kim et al.,(2003) and Kolodinsky (1993). Elderly consumers often seek information from private sources like word of mouth while deciding which store to support or what product to buy as quoted in the study conducted Lumpkin & Barnett (1982). Complaints are more effective in driving loyalty for strongly tied customers when the feedback is directed toward the provider who failed rather than to an entity external to the failure as per Umashankar et al.,(2017)

Although personal traits and demographics could hold significant cues to identify factors that may influence intention to complain, other factors may not be directly related but will undoubtedly influence. The brand itself could be a reason, Company policies, peer influence, type of consumption, product, cost incurred on the product would also be relevant reasons. The customer involvement with a product, service, or a consumption situation is more likely or willing to commit resources such as time, effort, and money to complain or redress a dissatisfactory experience as specified in the study done by Lau & Ng (2009). On the other hand, Chebat, et al., (2005) stated that, under low involvement situations, customers, in general, are unlikely to be bothered too much about a dissatisfactory experience with a product or service, Pritchard, et al., (1999) in their study mentioned about how highly involved customers tend to show a higher level of satisfaction or dissatisfaction. Marketers must develop an in-depth understanding of consumers' lifestyle preferences, choices, and aspirations, especially the younger generations, these details were mentioned in the study done by Tamana.Anand, et al., (2019).

Based on existing literature and a universal conceptualisation indention to complain, the intent is to build and validate a multi-item scale for measuring the same in this study.


Data Collection and Sample Profile

There was an effort to get respondents from all age group, and with a varied monthly income and from across geography, there were respondents from 72 cities. There was an effort to get respondents from all age group, and with a varied monthly income and from across geography, there were respondents from 72 cities. To ensure that the participants in the survey understood the concept of "Intention to Complain" and "Online Retail", they were asked if they purchased online. The scale and the explanations for the same were given with regards to the factors. Of the 461 surveys collected, 443 was found usable; the remaining were excluded because specific important questions were unanswered in Table 1.

Table-1 Demographics
Socio-Demographic Characteristics Focus Group-10 Pre-test-49 Purification-443 Construct Validation-534
Male 7 31 255 293
Female 5 18 188 241
18-24   17 175 208
25-34 4 8 137 190
35-44 3 12 68 75
45-54 4 5 57 48
55-64 1 7   13
65 plus        
Monthly Income        
20000-30000 3 13 171 191
30000-40000 7 12 73 87
40000-50000     95 78
50000-60000   12 33 35
60000-70000   8 13 30
70000+ 2 4 52 113
Educational Level        
Under Graduation   13 23 48
Graduate     174 199
Post-Graduation 10 27 232 271
Doctorate 2 9 8 16

Scale Development and Item Generation

The principal objective of this segment is to extrapolate exact items for the Customer Intention to Complain and choose the items that have content validity. The prime phase in stimulating the items is to echo the customer's intention to complain and generate the item pool. A vast collection of probable items was developed through literature reviews. In total, 89 items were penned down. After the preliminary item pool was created, the researcher and PhD guide and 10 PhD students and 4 professors of Marketing met to assess the face validity and remove redundant items. These sessions led to choosing 76 items with good face validity, which were completed for expert review. Therefore, the item screening generated a reduced pool of 76 Customer Intention to Complain items.

A panel of 10 PhD students and two marketing Professors were requested to contribute to a survey to measure the content validity of the items generated from the initial step. All panellists were given the ordinary course with the meanings of the construct and the scheduled dimensions. In addition, all members were part of an in-person meeting to dodge any possible misunderstanding about the research. The chosen construct and the related area of research were identified to the scholars. Being in line with the process suggested by Anderson J. Gerbing (1991), the members were asked to allocate the items generated from the first step to the construct that each echoed very well. In addition, a choice was provided for items that do not reflect well in either of the proposed dimensions, which was a 'not applicable' category. In tune with the projected method by Anderson J. Gerbing (1991), the cited indices were calculated. Then the items with measures of 0.5 and greater were retained, and those items that did not meet the minimum level of significance were dropped. The final questionnaire included 69 items for measuring Customer Intention to Complain. Each item in the questionnaire was assessed with a Seven-point Likert scale ranging from 1 = ‘strongly disagree’ to 7 = ‘strongly agree’.

Exploratory Factor Analysis

Principal component analysis with Promax rotation on the initial 69 items was conducted using SPSS, and the numbers of factors to be extracted were not restricted. The number of extracted factors were determined based on the eigenvalues, scree test plot and explained variance. Based on Cattell (1966), the screen in the scree plot at seven factors was observed as the proposed Seven-factor model of ‘Consumer Intention to Complain” in the current research. In the pattern matrix, (Hair, et al., (2010) recommend a critical factor loading of 0.50 to achieve significance (p<0.05). Hence, this research employed a factor loading of 0.50 as the minimum cut-off. Based on this analysis, fifty items were removed using an iterative process. Each of the retained 19 items was loaded onto its proposed factor. The scree plot solution showing a 7-factor model was chosen to show the variance explained as 78.22. The item to total correlation ranged from .587-.894. The Factor analysis carried out with the 19 items, which accounted for the 78.22 variance and had the Individual Bottleneck, which had 3 items having Cronbach alpha of 0.812. The product Deficiency factor had 3 items with Cronbach alpha of 0.872, complaint Handling with 4 items with Cronbach alpha of 0.848, Company policy with 3 items having a Cronbach alpha reading of 0.831, brand, peer influence, and purchase involvement had Cronbach alpha of 0.787,0.867 and 0.853, respectively.

Confirmatory Factor Analysis

The objective of this segment is to improve and confirm the preliminary 19-item scale. Confirmatory factor analysis (CFA) was conducted using Amos 22.0 to estimate a 19-item, seven-dimensional factor model. The CFA was employed to fit the model to the data using maximum likelihood estimation. The assessment of model fix indices is presented in Table -3. As shown in Table-3, the outcomes from the evaluation of model fit were termed good to great as per literature. The iterative procedure was completed with a seven-factor model consisting of 19 items, with no items exhibiting modification indices greater than 11 or standardised residuals greater than 2.15. The 19 items then comprise Three Individual bottlenecks items, three Product deficiency items, four complaint Handling items, three Company Policies, two Brands, two peer influences, and two Purchase Involvement items in Table 2.

Table-2 Reliability and EFA and CFA Reading
Factor Items Cronbach Alpha EFA Loading Item to total correlation Loading p-Value
Individual Bottleneck IND1 0.817 0.863 0.662 0.762 .000
IND2   0.822 0.667 0.798 .000
IND3   0.866 0.655 0.744 .000
Product Deficiency PD1 0.879 0.825 0.662 0.714 .000
PD2   0.961 0.894 1.069 .000
PD3   0.887 0.703 0.762 .000
Complaint Handling CH1 0.882 0.874 0.658 0.653 .000
CH2   0.747 0.587 0.574 .000
Ch3   0.874 0.756 0.881 .000
Ch4   0.855 0.753 0.894 .000
Company Policy CP1 0.835 0.836 0.671 0.755 .000
CP2   0.928 0.802 0.947 .000
CP3   0.797 0.617 0.695 .000
Brand Brand1 0.787 0.908 0.629 0.747 .000
Brand2   0.882 0.629 0.842 .000
Peer Influence Peer1 0.868 0.945 0.766 0.935 .000
Peer2   0.928 0.766 0.821 .000
Purchase Involvement PI1 0.853 0.944 0.759 0.822 .000
PI2   0.927 0.759 0.924 .000
Table-3 Model Fit Indices for A 19-Item Factor
Model Fit Indices   GFI CFI TLI RMSEA
Proposed dimensional Model χ2 (52) = 195.327, p = .000; Normed Chi- Square= 1.503 0.951 0.983 0.978 0.034
Rules of thumb   >.90 >.95 >.95 <.05
  Good Great Great Good

Established from the EFA and CFA results, a final 7-dimension, 19-item scale is determined. The details for these 7 dimensions are mentioned hereunder:

Individual Bottlenecks

The dimension mentions the individual's mindset, which could act as a bottleneck regarding his or her intention to complain. The time taken to complain would make an individual decide whether they want to go through that process or not. The money that an individual will have to spend will also have a bearing on the complaining habit, and lastly, the efforts the individual will have to put in also affect the same. The literature mentioned shows that these parameters have a strong correlation to complaining habits. If a retailer receives fewer complaints, it may be just because rather than complaining about a deficiency in the product or service; the customer is just switching to competition as referred by Goodman (1999) and Stephens & Gwinner (1998). The customer may feel that switching is an easier option keeping in mind the factors that may affect his or her complaint behaviour such as time, money discomfort in the complaint process, previous experience, and the level of dissatisfaction. Thus, an effective Customer Satisfaction Management program is essential because research has found that dissatisfied customers do not usually complain even when they suffer huge losses of money and time. Online retail customers satisfaction will significantly depend on what they decided to buy and what they eventually got; this could be in correlation with the product and its quality, or Mingyao, et al.,(2015) mentioned about the product and the amount of money spent by the Customer. Singh (1988). Talked about how the perceived value of the complaint is the personal valuation of the gap between the benefits and the cost involved in complaining. This epitomises the customer's faith that it is valuable to make the complaint. The latent paybacks to complain comprise reimbursements, exchange, or apology, while the price incurred contains the time and effort in making the complaint as stated in Singh (1988) research. A study done by Hirschman (1970) showed that customer makes a mental assessment as to whether it is worth to complain about the dissatisfaction regarding the product or service, they assess the profitability of success, the effort it takes to complain and the value of that product or service.

Product Deficiency

This dimension talks about the deficiency in the product once the customer has the same and how it will impact the customer's intention to complain. The correlation the product has with regards to the percentage of loss incurred of the total amount he or she spends on purchases made online. Thus, an effective Customer Satisfaction Management program is essential because research has found that dissatisfied customers do not usually complain even when they suffer huge losses of money and time. Online retail customers satisfaction will significantly depend on what they decided to buy and what they eventually got. Mingyao, et al. (2015) and John & Laurens 1978 cited that this could correlate with the product and its quality, or the product and the amount of money spent by the Customer. From an economic perspective, they expect that in exchange for the money they give to the organisation, they receive a fair amount of product or service as in research of John & Laurens (1978) from a relational perspective, wherein the customer expects that he/she is treated with respect and consideration and due importance is given to their thoughts, and a value is attached to them. If there is a service imbalance, then the same can be restored by the reparation provided by the organisation, or it leads to retaliation where the customer attempts to punish the organisation and pushes for compensation.

Complaint Handling

This dimension talks about overall how difficult it is for the individual to get into the complaint process. This would include the company's follow-up process; Money transfer is late, the company has a reputation of poor service keeping my past experiences in mind, the back-end services /operator service/call Centre, etc., are flawed. What could be the procedures that can reduce barriers to complaining, can the complaint process produce an expectation of a successful outcome, can the complaint process or procedure minimise cost and increase the expectation of success and speed of the process. One chief factor that encourages the choice to seek or not to seek reimbursement is the perceived likelihood of success as per Day & Landon (1976). Richins (1983) discussed that the possibility of success denotes the customer's perception of the retailer's willingness to remedy the problem minus the difficulty.

Company Policy

As the name suggests, this dimension debates about how 'company policies' impact the Customers Intention to complain. This parameter talks about the correlation of the 3 items which is there in the customer thought process regarding complaint behaviour. A customer will raise a complaint if "I know that the company has a sure policy on Voice of the Customer". " I will raise a complaint if I know that the company has 'Service Improvement' as its priority", and lastly will raise a complaint if "I know that the company revisits its complaint handling procedures regularly". Do the policies encourage and enable the management to incorporate the voice of the customer into decision-making? Does it prioritise the customer, service improvement priorities, and the importance of resource allocation decisions, competition mapping concerning complaint procedures and Customer service benchmarks, reward excellent service, and correct poor service. Despite organisations being careful about taking care of problems that can occur in the relationship between the customer and the company, which could lead to confrontations, still sufficient importance to handling customer's properly whenever they have an issue with the services or product, are not effectively handled, referred by Homburg & Fürst (2005). Johnston (2001) talkes about how retailers who receive few complaints have a thought process that the customer is generally satisfied by its product and services and would be loyal. Complaints are a natural phenomenon of any service activity because mistakes are an unavoidable part of all human endeavour. Ndubisi & Ling (2006) study showed that a public complaint (Complaint made directly to the company) made by the customer gives the company a chance to address the same and make amends by appropriately compensating him or her and thus improving retention. On the other hand, private complaining (Complaints made to other Customers, family members, friends, or peer groups) does not give the company a chance to rectify the wrong, which reduces the customer base as righted deduced in the research done by Bearden & Oliver (1985). Since a present consumer's thought process regarding a company is usually based on their experience, positive/negative experiences could strengthen/weaken the customer's imprint of and outlook towards the company. Cebhat & Codjovi (2005) research signifies that customer who are not satisfied with an organisation's services do not voice their concern.


This dimension talks about the "Brand" as the influencer or deterrent to the customer complaint behaviour and how it impacts the same. Love is blind versus the Love becomes hate effects. The "Love is blind" effect argues that customers with a strong relationship with the brand are more likely to forgive a service failure. As a result, they retaliate to a lesser extent than the customer having a weaker relationship. These customers are more reluctant to hurt a partner or to terminate a meaningful relationship. A "Love becomes hate" effect suggests that customer who possesses strong relationships tend to retaliate more vigorously than those with a weak relationship. Good complaint culture and processes may well lead to improved financial performances. How much market share the brand has, and how long has the brand been in the market to understand its customer needs. Financial switching cost (Loss of brand loyalty benefits) and Relational switching costs (affiliation with the brand which is both emotional and psychosomatic) as quoted by Burnham, et al., (2003). Organisations essentially need to appreciate this animated feature of their customers, damaging their customer association and injuring brand image. Kolodinsky.J (1992) talked about older customers are more likely to stop patronising a brand vis-a-via their younger counterparts because of dissatisfaction faced due to complaint is not appropriately handled by the organisation.

Peer Influence

This dimension talks about who influences the customers’ intent to complain. It direct effect on people by the peers, or an individual who gets encouraged to follow his/her peers by changing their attitudes, values, or behaviours to conform to those of the influencing group or individual. Ndubisi & Ling (2006) study mentioned about public complaint (Complaint made directly to the company) made by the customer gives the company a chance to address the same and make amends by appropriately compensating him or her and thus improving retention On the other hand, Bearden & Oliver (1985) research suggested that private complaining (complaints made to other customers, family members, friends, or peer groups) does not give the company a chance to rectify the wrong, reducing the customer base.

Purchase Involvement

Whether the purchase is made for self-consumption, or it is made for a third party where if the product that reaches them is not of good quality, it could lead to a loss of face for the person who purchased it. The degree of information processing and the importance of a consumer attaches to a product while buying it. In other words, it shows how involved a customer is towards a product personally, socially, and economically. For instance, I have purchased a product for another individual, and the price is high; on the other hand, I have purchased a product and have bought it for someone special, and the price is high.

Scale Validation

The objective of this section of the research is to examine the Customer Intention to Complain scale inside a nomological net of a central conceptual model and conduct further scale validation. A fresh set of 534 data was collected for Reliability and Validity aspects. First, Cronbach's α coefficients and composite reliability (CR), as depicted in Table 4 were checked to calculate the reliability of the 7 factor-dimension constructs. Cronbach's α coefficients range from .804 to .883 for the eight dimensions, exceeding the threshold value of .700 as per Nunnally & Bernstein (1994). The CR values range from .842 to .936, exceeding the conventional minimum of .700 Bentler (2009). These statistical outcomes show a high level of reliability.

Table-4 Reliability of the Scale
Dimensions Items Cronbach Alpha Composite Reliability
Individual Bottleneck 3 0.804 0.873
Product Deficiency 3 0.871 0.914
Complaint Handling 4 0.855 0.936
Company Policy 3 0.839 0.878
Brand 2 0.818 0.842
Peer 2 0.858 0.906
Purchase Involvement 2 0.883 0.911

After that, the construct validity is assessed from two facets: convergent and discriminant validities. One way to evaluate convergent validity is by evaluating CFA factor loadings (Table-5) as mentioned by Anderson J. Gerbing (1991), and the Model fit as mentioned in (Table- 6) also confirms the nomological validity of the proposed scale. All the strong loadings support the convergent validity. The t-values were significant, which offers additional evidence for the existence of a strong relationship between all Seven first-order constructs and the second-order construct, CIC (Customer Intention to Complain). In addition, the average variance extracted (AVE) values shown in Table 7 meet the recommended 0.5 by Fornell & Larcker (1981), which can further confirm the convergent validity. As per the measures recommended by Fornell & Larcker (1981), this instrument's good discriminant validity can also be ensured due to the square root of the AVE value of each factor exceeding the pairwise correlations between the factors as shown in Table 7. To summarise, all the validation outcomes show that the scale is an instrument with good reliability and construct validity.

Table-5 CFA Standardized Loading, Factor Loading.
Factor Items Standardised Loading Factor Loading
Individual Bottleneck IND1 0.711 0.826
IND2 0.824 0.851
IND3 0.746 0.826
Product Deficiency PD1 0.692 0.815
PD2 1.039 0.946
PD3 0.789 0.885
Complaint Handling CH1 0.741 0.815
CH2 0.631 0.773
Ch3 0.863 0.868
Ch4 0.852 0.849
Company Policy CP1 0.785 0.826
CP2 0.908 0.901
CP3 0.725 0.792
Brand Brand1 0.786 0.875
Brand2 0.879 0.831
Peer Influence Peer1 0.913 0.902
Peer2 0.824 0.919
Purchase Involvement PI1 0.851 0.92
PI2 0.931 0.91
Table- 6 Model Fit Indices Baseline Comparisons
Proposed 7-Dimensional Model 0.956 0.982 0.976 0.981 0.036
Rules of Thumb >.9 >.95 >.95 >.95 <.05
Good Great Great Great Good
Table 7 Construct Validity of the Scale. Average Variance Extracted, CR- Composite Reliabilities, Construct Correlations and Square Root of AVE  
  Average Variance Extracted Individual Bottleneck Product Deficiency Complaint Handling Company Policy Brand Peer Purchase Involvement  
Individual Bottleneck 0.709 0.842              
Product Deficiency 0.781 0.072 0.884            
Complaint Handling 0.731 0.191 0.179 0.794          
Company Policy 0.707 0.159 0.125 0.124 0.841        
Brand 0.728 0.167 0.125 0.043 0.535 0.853      
Peer 0.828 0.195 0.046 0.061 0.123 0.113 0.91    
Purchase Involvement 0.837 0.151 0.078 0.032 0.193 0.239 0.353 0.915  


With a broader aim at Item generation and validation of an instrument for measuring Customer Intention to complain, this experimental study has been performed on the sample online buyers. The Customer Intention to Complain measurement instrument intends to find the vital characteristics that develop an individual's intention to complain, which, if developed, may ensure that he/she will not quietly exit the brand and will express their dissatisfaction. This would enable the organisation to retain them, perceived recovery quality affects satisfaction, Customer satisfaction influences customer loyalty and Customer loyalty significantly impacts a company's profitability.

The research analysed the most quoted factors that impact CIC as per literature Hirshman (1970). His study mentioned product/service dissatisfaction, the profitability of success (of complaining), the effort it takes to complain, and its value or service. Perceived likelihood to get a redress is recognised as an essential determinant of complaint voicing as cited by Blodgett & Anderson (2000) and Day (1984). Bearden & Mason (1984), Moyer (1984),Richins (1983),Giese & Cote (2000) and Dallimore, et al., (2007) looked at the personality traits which impacted complaining behaviour. Researchers have studies the emotional reasons for complaining; the related attitude parameters were studied by Singh & Wilkes (1996). Krautz & Hoffmann (2017) and Chebat et al., (2005) and others took up the study from the Brand perspective, how responsive the brand is to the complaint. The Demographic parameters were also taken up in past studies, which included age, income, education, and knowledge level.

The study was carried out with 69 items. The number of extracted factors were determined based on the eigenvalues, scree test plot and explained variance. The research employed a factor loading of 0.50 as the minimum cut-off; fifty items were removed using an iterative process. Each of the retained 19 items was loaded onto its proposed factor. The scree plot solution showing a 7-factor model was chosen to show the variance explained as 78.22. The proposed Seven-factor model Scale to measure 'Consumer Intention to Complain" in the current research were Individual Bottleneck, Product Deficiency, Complaint Handling, Company Policies, Brand, Peer and Purchase Involvement.

Numerous sequences of experimental validation, including the exploratory factor analysis, confirmatory factor analysis and reliability analysis, supported the 7-factor scale. The research exhibited good psychometric properties of the scale. The findings proved that Customer Intention to Complain is a measurable construct. Evaluation of which is essential for improving customer satisfaction and, in a way, repurchase behaviour leads to enhanced profitability as research shows that an increase of customer retention by 5% can lead to profitability increase from 25% to almost 95%. Further, the success rate of selling to an existing customer is as high as 60-70%, while to a new customer, it ranges from 5-20% Reichheld (2014). The model can improve managers' understanding concerning customer intention to complain. The same can be used to design broader relationship marketing and understand the customer's mindset regarding complaining behaviour. Brands can develop strategies for enhancing the customers' intention to complain and activate the same so that they are encouraged to stay and complain rather than exiting from the brand. The research proposes that providing and controlling the quality of information and establishing long term online marketing strategies to create a strong relationship between customers with the online retailer are beneficial, keeping the competition in the market in mind.

As no research can be free from limitations, so is the case with this research. This research has been grounded on data collated from participants drawn only from India; therefore, this study is primarily India based, We cannot verify or establish whether our conclusions and implications would apply to other countries. Our research has focused mainly on online retail; thus, the findings may not hold good for the traditional brick & mortar store retail; especially because there is a 'physical interaction' between the customer and service provider. Thus, future research should be carried out to reproduce a similar cross-cultural study across other service groups and other environments.


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