AIR CONDITIONING SERVICE QUALITY RESEARCH

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Find out about our ongoing research into how Air Conditioning Service Quality Levels Impact B2B Customer Satisfaction & Loyalty Levels Within The UK Commercial Building Services Industry.

The Focus, Aims and Scope of our Air Conditioning Service Quality Research

Customers are referred to here as Business to Business (B2B) and not to be confused with consumers associated with Business to Consumer (B2C) markets. Service Quality is an antecedent of customer satisfaction & loyalty, linked to companies financial growth through the Service Profit Chain.

Variances between a customers expectations and perceptions of Service Quality negatively effect Customer Satisfaction & Loyalty creating customer retention problems for companies. Marketing attempts to reduce variation through setting customer expectations of Service Quality but can only do this once customer expectations are known. Therefore, not knowing how Service Quality effects Customer Satisfaction & Loyalty creates problems for companies marketing strategy through misalignment of expectations.

The Air Conditioning Industry lacks research into how companies Service Quality levels impact Customer Satisfaction & Loyalty within the UK Commercial Building Services Industry.

While some large Air Conditioning companies may measure their own Commercial Building Services Customer Satisfaction & Loyalty levels no best practice industry benchmark exists for comparison, so Service Quality levels cannot be easily compared.

Research Context for Air Conditioning Service Quality

Taking Lovelocks 1983 classification Air Conditioning services are directed at goods, where recipients are things rather than people and the nature is tangible rather than intangible.

We argue that although the end product is tangible there exists and experiential intangible service element that impacts Service Quality. For example, the end product could meet expectations but the customer experiences may not, effecting Customer Satisfaction & Loyalty.

This simultaneous production and consumption of services limits the ability to reduce variation because of the inability to separate process and production, unlike products.

The human element causes variation for each service delivery because customers have unique needs and wants, and no two experiences are the same.

Therefore, organisations should aim to reduce negative variation by either meeting or exceeding customer Service Quality expectations throughout the service delivery process, improving Customer Satisfaction & Loyalty.

Importance of Air Conditioning Service Quality Research

The research aims to produce a best practice industry benchmark that companies can use to compare their own Service Quality Dimensions / Customer Satisfaction results with.

While doing so it also challenges the ranking system utilised by the NET Promoter Score which uses a 10 point ranking system proposed by Reichheld, Frederick F. (2003) to establish ‘detractors’ (1-6), ‘passives’ (7-8) and ‘promoters’ (9-10) but lacks supporting evidence of universal application across all industry and country segments, proposing an alternative method for identifying and classifying these themes.

We also challenge the proposition by Frederick F. (2003) that there is no correlation between satisfaction and growth by introducing a longitudinal study complete by Keiningham that found equivalent or improved correlation between satisfaction and growth by repeating Reichheld’s original NET promoter analysis, proving there is a correlation between satisfaction and growth while highlighting the influence Service Quality Dimensions have on satisfaction.

Background Literature Review

The Service Profit Chain (Heskett, J. L., et al. (1994), Pugh, S. D., et al. (2002), (Rucci, A. J., et al. (1998), (Heskett, J. L., et al. (1997) has long been established as a way of linking the ‘internal service climate’ to ‘service quality’ then ‘customer satisfaction & loyalty’ and finally ‘company profits and growth’. This is in effect a horizontal ‘balanced scorecard’ used by many organisations to measure and improve short and long term strategy.

‘Service Quality’ (Parasuraman, A., et al. (1988), Setó Pamies, D. (2012), Berry, L. L. and A. Parasuraman (1997), Schneider, B. et al. (1994) is a key driver of ‘customer satisfaction & loyalty’  (Payne, A. et al(2001). Reichheld, Frederick F. 2003 challenges this and found no correlation between satisfaction and growth.

Frederick also claims the ‘word of mouth’ loyalty metric produced by the ‘NET Promoter Score’ is a more reliable indicator of growth, compared with satisfaction and retention and “Companies can boost profits by almost 100 percent by retaining just 5 percent more of their customers.” e.g. by reducing defectors by 5%.

However, a later longitudinal study completed by Keiningham, Timothy L., et al (2007), found equivalent or improved correlation between satisfaction and growth by repeating Reichheld’s original NET promoter analysis, proving there is a correlation between satisfaction and growth.

Parasuraman, A. et al 1985 identified 10 dimensions of ‘service quality’ resulting in the SERVQUAL quantitative model. Through factor analysis and testing Parasuraman, A. et al. (1988) later reduced these to 5 dimensions, with Gap 5 of the SERVQUAL model used measure variances between customer expectation and perception gaps of ‘service quality’.

They also note the model may not be suitable for all industries and may require modification, so we propose to customise it to suit the Business to Business Air Conditioning industry and use a 10 point scale for responses.

The original SERVQUAL model averages individual dimensions together to evaluate overall Service Quality. However, different people within each dimension will place different levels of importance on each dimension Oliver, R.L. (1997), so an overall forced raking of importance for each dimension is required.

Ranking of SERVQUAL dimensions by respondents was successfully completed by Cronin and Taylor (1992) and Teas (1993) and is a proposed modification to the model to improve the research outcome. While SERVQUAL combines perceptions and expectation to produce a gap, Cronin Jr, J. et al 1992, 1994 developed SERVPERF arguing it provides a better predictor of ‘service quality’ than the SERVQUAL gaps model by measuring perceptions only.

Parasuraman, et al 1994a, p. 216 attempted to counter this stating it depends on the context of the research. They suggest SERVPERF is best for ‘predictive’ power, however if measuring ‘critical service shortfalls’ is the objective the SERVQUAL gap model is more suitable.

Based on our focus being to provide an industry best practice benchmark that organisations can use to improve ‘critical service shortfalls’ towards the benchmark we propose the SERVQUAL model over SERVPERF.

The SERVQUAL expectation and perceptions gap will also be used to determine the ‘zone of tolerance’ within each dimension by requesting respondents indicate on a 10 point scale where they perceive the ‘best organisation to be’ and the ‘minimum they would be willing to accept’.

As suggested by Coyne, 1989, Parasuraman, et al 1991b, Oliva, et al, 1992, Parasuraman, et al 1993, Parasuraman, et al 1994a, Storbacka, et al 1994 this can be used to establish a ‘zone of tolerance’ and we propose segmenting responses into the following three themes of ‘detractors’, ‘passives’ and ‘promoters’.

SERVQUAL results will be linked to ‘customer loyalty’ by using a modified version of the ‘NET Promoter score’ Reichheld, Frederick F. (2003).

As discussed earlier while Reichheld, Frederick F. 2003 found no correlation between satisfaction and growth, Keiningham, Timothy L., et al (2007) have through their longitudinal study so we propose it as a suitable method following modification of the model as previously discussed. However, the 10 point ranking system within the ‘NET Promoter score’ method proposed by Reichheld, Frederick F. (2003) to establish ‘detractors’ (1-6), ‘passives’ (7-8) and ‘promoters’ (9-10) lacks supporting evidence of universal application across all industry and country segments.

We therefore propose to utilise the ‘zone of tolerance’ method to identify industry and country specific segments, enabling linkage of the ‘service quality dimensions’ through the ‘service profit chain’ to ‘customer loyalty’.

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