SERVQUAL MODEL as a Service Quality Measure

SERVQUAL MODEL as a Service Quality Measure 1.0 Introduction A great deal of service-quality research in recent decades has been devoted to the development of measures of service quality. In particular, the SERVQUAL instrument (Parasuraman et al., 1988) has been widely applied and valued by academics and practicing managers (Buttle, 1996). However, several studies have identified potential difficulties with the use of SERVQUAL (Carman, 1990; Cronin and Taylor, 1992; Asubonteng et al., 1996; Buttle, 1996; Van Dyke et al., 1997; Llosa et al., 1998). These difficulties have related to the use of so-called difference scores, the ambiguity of the definition of consumer expectations, the stability of the SERVQUAL scale over time, and the dimensionality of the instrument. As a result of these criticisms, questions have been raised regarding the use of SERVQUAL as a measure of service quality. 1.1 The SERVQUAL scale When the SERVQUAL scale was developed by Parasuraman et al. (1985, 1988), their  aim was to provide a generic instrument for measuring service quality across a broad range of service categories. Relying on information from 12 focus groups of consumers, Parasuraman et al. (1985) reported that consumers evaluated service quality by comparing expectations (of service to be received) with perceptions (of service actually received) on ten dimensions: tangibles, reliability, responsiveness, communication, credibility, security, competence, understanding/knowing customers, courtesy, and access. In a later (Parasuraman et al. (1988) work, the authors reduced the original ten dimensions to five: (1) tangibles (the appearance of physical facilities, equipment, and personnel); (2) reliability (the ability to perform the promised service dependably and accurately); (3) responsiveness (the willingness to help customers and provide prompt service); (4) empathy (the provision of individual care and attention to customers); and (5) assurance (the knowledge and courtesy of employees and their ability to inspire trust and confidence). Each dimension is measured by four to five items (making a total of 22 items across the five dimensions). Each of these 22 items is measured in two ways: (1) the expectations of customers concerning a service; and (2) the perceived levels of service actually provided. In making these measurements, respondents are asked to indicate their degree of agreement with certain statements on a seven-point Likert-type scale (1 strongly disagree to 7 strongly agree). For each item, a so-called gap score (G) is then calculated as the difference between the raw perception-of-performance score (P) and the raw expectations score (E). The greater the gap score (calculated as G  ¼ P minus E), the higher the score for perceived service quality. Chapter 2: Literature Review 2.0 Introduction Despite the widespread use of the SERVQUAL model to measure service quality, several theoretical and empirical criticisms of the scale have been raised. Buttle (1996) summarised the major criticisms of SERVQUAL in two broad categories theoretical and operational. Theoretical issues comprise: Paradigmatic objections: SERVQUAL is based on a disconfirmation paradigm rather than an attitudinal paradigm; and SERVQUAL fails to draw on established economic, statistical and psychological theory. Gaps model: there is little evidence that customers assess service quality in terms of P E gaps. Process orientation: SERVQUAL focuses on the process of service delivery, not the outcomes of the service encounter. Dimensionality: SERVQUALs five dimensions are not universals; the number of dimensions comprising SQ is contextualized; items do not always load on to the factors which one would a priori expect; and there is a high degree of intercorrelation between the five RATER dimensions. Operational criticisms include: Expectations: the term expectation is polysemic; consumers use standards other than expectations to evaluate SQ; and SERVQUAL fails to measure absolute SQ expectations. Item composition: four or five items can not capture the variability within each SQ dimension. Moments of truth (MOT): customers assessments of SQ may vary from MOT to MOT. Polarity: the reversed polarity of items in the scale causes respondent error. Scale points: the seven-point Likert scale is flawed. Two administrations: two administrations of the instrument cause boredom and confusion. Variance extracted: the over SERVQUAL score accounts for a disappointing proportion of item variances. The above criticism will be discussed below. 2.1: Paradigmatic objections (Theoretical Criticisms) Two major criticisms have been raised. First, SERVQUAL has been inappropriately based on an expectations disconfirmation model rather than an attitudinal model of SQ. Second, it does not build on extant knowledge in economics, statistics and psychology. SERVQUAL is based on the disconfirmation model widely adopted in the customer satisfaction literature. In this literature, customer satisfaction (CSat) is operationalised in terms of the relationship between expectations (E) and outcomes (O). If O matches E, customer satisfaction is predicted. If O exceeds E, then customer delight may be produced. If E exceeds O, then customer dissatisfaction is indicated. According to Cronin and Taylor (1992; 1994) SERVQUAL is paradigmatically flawed because of its ill-judged adoption of this disconfirmation model. Perceived quality, they claim, is best conceptualised as an attitude. They criticise Parasuraman et al. for their hesitancy to define perceived SQ in attitudinal terms, even though Parasur aman et al. (1988) had earlier claimed that SQ was similar in many ways to an attitude. Cronin and Taylor observe: Researchers have attempted to differentiate service quality from consumer satisfaction, even while using the disconfirmation format to measure perceptions of service qualityà ¢Ã¢â€š ¬Ã‚ ¦ this approach is not consistent with the differentiation expressed between these constructs in the satisfaction and attitude literatures. Iacobucci et al.s (1994) review of the debate surrounding the conceptual and operational differences between SQ and CSat concludes that the constructs have not been consistently defined and differentiated from each other in the literature. She suggests that the two constructs may be connected in a number of ways. First, they may be both different operationalisations of the same construct, evaluation. Second, they may be orthogonally related, i.e. they may be entirely different constructs. Third, they may be conceptual cousins. Their family connections may be dependent on a number of other considerations, including for example, the duration of the evaluation. Parasuraman et al. (1985) have described satisfaction as more situation- or encounter-specific, and quality as more holistic, developed over a longer period of time, although they offer no empirical evidence to support this contention. SQ and CSat may also be related by time order. The predominant belief is that SQ is the logical predecessor to CSat, but this remains unproven. Cronin and Taylors critique draws support from Olivers (1980) research which suggests that SQ and CSat are distinct constructs but are related in that satisfaction mediates the effect of prior-period perceptions of SQ and causes revised SQ perceptions to be formed. SQ and CSat may also be differentiated by virtue of their content. Whereas SQ may be thought of as high in cognitive content, CSat may be more heavily loaded with affect (Oliver, 1993). Cronin and Taylor suggest that the adequacy-importance model of attitude measurement should be adopted for SQ research. Iacobucci et al. (1994) add the observation that in some general psychological sense, it is not clear what short-term evaluations of quality and satisfaction are if not attitudes. In turn, Parasuraman et al. (1994) have vigorously defended their position, claiming that critics seem to discount prior conceptual work in the SQ literature, and suggest that Cronin and Taylors w ork does not justify their claim that the disconfirmation paradigm is flawed. In other work, Cronin and Taylor (1994) comment that: Recent conceptual advances suggest that the disconfirmation-based SERVQUAL scale is measuring neither service quality nor consumer satisfaction. Rather, the SERVQUAL scale appears at best an operationalisation of only one of the many forms of expectancy disconfirmation. A different concern has been raised by Andersson (1992). He objects to SERVQUALs failure to draw on previous social science research, particularly economic theory, statistics, and psychological theory. Parasuraman et al.s work is highly inductive in that it moves from historically situated observation to general theory. Andersson (1992) claims that Parasuraman et al. abandon the principle of scientific continuity and deduction. Among specific criticisms are the following: First, Parasuraman et al.s management technology takes no account of the costs of improving service quality. It is naÃÆ' ¯ve in assuming that the marginal revenue of SQ improvement always exceeds the marginal cost. (Aubrey and Zimbler, 1983., Crosby., 1979, Juran., 1951 and Masser., 1957) have addressed the issue of the costs/benefits of quality improvement in service settings.) Second, Parasuraman et al. collect SQ data using ordinal scale methods (Likert scales) yet perform analyses with methods suited to interval-level data (factor analysis). Third, Parasuraman et al. are at the absolute end of the street regarding possibilities to use statistical methods. Ordinal scales do not allow for investigations of common product-moment correlations. Interdependencies among the dimensions of quality are difficult to describe. SERVQUAL studies cannot answer questions such as: Are there elasticities among the quality dimensions? Is the customer value of improvements a linear or non-linear function? Fourth, Parasuraman et al. fail to draw on the large literature on the psychology of perception. 2.2: Gaps Model A related set of criticisms refer to the value and meaning of gaps identified in the disconfirmation model. Babakus and Boller (1992) found the use of a gap approach to SQ measurement intuitively appealing but suspected that the difference scores do not provide any additional information beyond that already contained in the perceptions component of the SERVQUAL scale. They found that the dominant contributor to the gap score was the perceptions score because of a generalised response tendency to rate expectations high. Churchill and Surprenant (1982), in their work on CSat, also ponder whether gap measurements contribute anything new or of value given that the gap is a direct function of E and P. It has also been noted that: while conceptually, difference scores might be sensible, they are problematic in that they are notoriously unreliable, even when the measures from which the difference scores are derived are themselves highly reliable (Iacobucci et al., 1994). Also, in the context of CSat, Oliver (1980) has pondered whether it might be preferable to consider the P E scores as raw differences or as ratios. No work has been reported using a ratio approach to measure SQ. Iacobucci et al. (1994) take a different tack on the incorporation of E-measures. They suggest that expectations might not exist or be formed clearly enough to serve as a standard for evaluation of a service experience. Expectations may be formed simultaneously with service consumption. Kahneman and Miller (1986) have also proposed that consumers may form experience-based norms after service experiences, rather than expectations before. A further issue raised by Babakus and Inhofe (1991) is that expectations may attract a social desirability response bias. Respondents may feel motivated to adhere to an I-have-high-expectations social norm. Indeed, Parasuraman et al. report that in their testing of the 1988 version the majority of expectations scores were above six on the seven-point scale. The overall mean expectation was 6.22 (Parasuraman et al., 1991b). Teas (1993a; 1993b; 1994) has pondered the meaning of identified gaps. For example, there are six ways of producing P E gaps of -1 (P = 1, E = 2; P = 2, E = 3; P = 3, E = 4; P = 4, E = 5; P = 5, E = 6; P = 6, E = 7). Do these tied gaps mean equal perceived SQ? He also notes that SERVQUAL research thus far has not established that all service providers within a consideration or choice set, e.g. all car-hire firms do, in fact, share the same expectations ratings across all items and dimensions. A further criticism is that SERVQUAL fails to capture the dynamics of changing expectations. Consumers learn from experiences. The inference in much of Parasuraman et al.s work is that expectations rise over time. An E-score of seven in 1986 may not necessarily mean the same as an E-score in 1996. Expectations may also fall over time (e.g. in the health service setting). Grà ¶nroos (1993) recognises this weakness in our understanding of SQ, and has called for a new phase of service quality research to focus on the dynamics of service quality evaluation. Wotruba and Tyagi (1991) agree that more work is needed on how expectations are formed and changed over time. Implicit in SERVQUAL is the assumption that positive and negative disconfirmations are symmetrically valent. However, from the customers perspective, failure to meet expectations often seems a more significant outcome than success in meeting or exceeding expectations (Hardie et al., 1992). Customers will often criticise poor service performance and not praise exceptional performance. Recently, Cronin and Taylor (1992) have tested a performance-based measure of SQ, dubbed SERVPERF, in four industries (banking, pest control, dry cleaning and fast food). They found that this measure explained more of the variance in an overall measure of SQ than did SERVQUAL. SERVPERF is composed of the 22 perception items in the SERVQUAL scale, and therefore excludes any consideration of expectations. In a later defence of their argument for a perceptions-only measure of SQ, Cronin and Taylor (1994) acknowledge that it is possible for researchers to infer consumers disconfirmation through arithmetic means (the P E gap) but that consumer perceptions, not calculations, govern behavior. Finally, a team of researchers, including Zeithaml herself (Boulding et al., 1993), has recently rejected the value of an expectations-based or gap-based model in finding that service quality was only influenced by perceptions. 2.3: Process orientation SERVQUAL has been criticized for focusing on the process of service delivery rather than outcomes of the service encounter. Grà ¶nroos (1982) identified three components of SQ: technical, functional and reputational quality. Technical quality is concerned with the outcome of the service encounter, e.g. have the dry cleaners got rid of the stain? Functional quality is concerned with the process of service delivery, e.g. were the dry cleaners counter staff courteous? Reputational quality is a reflection of the corporate image of the service organization. While technical quality focuses on what, functional quality focuses on how and involves consideration of issues such as the behaviour of customer contact staff, and the speed of service. Critics have argued that outcome quality is missing from Parasuraman et al.s formulation of SQ (Cronin and Taylor, 1992; Mangold and Babakus, 1991; Richard and Allaway, 1993). Richard and Allaway (1993) tested an augmented SERVQUAL model which they cl aim incorporates both process and outcome components, and comment that the challenge is to determine which process and outcome quality attributes of SQ have the greatest impact on choice[1]. Their research into Domino Pizzas process and outcome quality employed the 22 Parasuraman et al. (1988) items, modified to suit context, and the following six outcome items: (1) Dominos has delicious home-delivery pizza. (2) Dominos has nutritious home-delivery pizza. (3) Dominos home-delivery pizza has flavourful sauce. (4) Dominos provides a generous amount of toppings for its home-delivery pizza. (5) Dominos home-delivery pizza is made with superior ingredients. (6) Dominos prepared its home-delivery pizza crust exactly the way I like it. These researchers found that the process-only items borrowed and adapted from SERVQUAL accounted for only 45 per cent of the variance in customer choice; the full inventory, inclusive of the six outcome items, accounted for 71.5 per cent of variance in choice. The difference between the two is significant at the 0.001 level. They conclude that process-and-outcome is a better predictor of consumer choice than process, or outcome, alone. In defense of SERVQUAL, Higgins et al., (1991) have argued that outcome quality is already contained within these dimensions: reliability, competence and security. 2.4: Dimensionality Critics have raised a number of significant and related questions about the dimensionality of the SERVQUAL scale. The most serious are concerned with the number of dimensions and their stability from context to context. There seems to be general agreement that SQ is a second-order construct, that is, it is factorially complex, being composed of several first-order variables [2]. SERVQUAL is composed of the five RATER [3] factors. There are however, several alternative conceptualizations of SQ. As already noted, Grà ¶nroos (1984) identified three components technical, functional and reputational quality; Lehtinen and Lehtinen (1982) also identify three components interactive, physical and corporate quality; Hedvall and Paltschik (1989) identify two dimensions willingness and ability to serve, and physical and psychological access; Leblanc and Nguyen (1988) list five components corporate image, internal organisation, physical support of the service producing system, staff/customer interaction, and the level of customer satisfaction. Parasuraman et al. (1988) have claimed that SERVQUAL: provides a basic skeleton through its expectations/perceptions format encompassing statements for each of the five service quality dimensions. The skeleton, when necessary, can be adapted or supplemented to fit the characteristics or specific research needs of a particular organization. In their 1988 paper, Parasuraman et al. also claimed that the final 22-item scale and its five dimensions have sound and stable psychometric properties. In the 1991b revision, Parasuraman et al. found evidence of consistent factor structure à ¢Ã¢â€š ¬Ã‚ ¦ across five independent samples. In other words, they make claims that the five dimensions are generic across service contexts. Indeed, in 1991, Parasuraman et al. claimed that SERVQUALs dimensions and items represent core evaluation criteria that transcend specific companies and industries (1991b) [4]. 2.5: Number of dimensions When the SERVQUAL instrument has been employed in modified form, up to nine distinct dimensions of SQ have been revealed, the number varying according to the service sector under investigation. One study has even produced a single-factor solution. Nine factors accounted for 71 per cent of SQ variance in Carmans (1990) hospital research: admission service, tangible accommodations, tangible food, tangible privacy, nursing care, explanation of treatment, access and courtesy afforded visitors, discharge planning, and patient accounting (billing)[5]. Five factors were distinguished in Saleh and Ryans (1992) work in the hotel industry conviviality, tangibles, reassurance, avoid sarcasm, and empathy. The first of these, conviviality, accounted for 62.8 per cent of the overall variance; the second factor, tangibles, accounted for a further 6.9 per cent; the five factors together accounted for 78.6 per cent. This is strongly suggestive of a two-factor solution in the hospitality industry. The researchers had initially assumed that the factor analysis would confirm the [SERVQUAL] dimensions but this failed to be the case. Four factors were extracted in Gagliano and Hathcotes (1994) investigation of SQ in the retail clothing sector personal attention, reliability, tangibles and convenience. Two of these have no correspondence in SERVQUAL. They conclude the [original SERVQUAL scale] does not perform as well as expected in apparel speciality retailing. Three factors were identified in Bouman and van der Wieles (1992) research into car servicing customer kindness, tangibles and faith [6]. The authors were not able to find the same dimensions for judging service quality as did Berry et al. One factor was recognized in Babakus et al.s (1993b) survey of 635 utility company customers. Analysis essentially produced a single-factor model of SQ which accounted for 66.3 per cent of the variance. The authors advance several possible explanations for this unidimensional result including the nature of the service, (which they describe as a low-involvement service with an ongoing consumption experience), non-response bias and the use of a single expectations/perceptions gap scale. These researchers concluded: With the exception of findings reported by Parasuraman and his colleagues, empirical evidence does not support a five-dimensional concept of service quality. In summary, Babakus and Boller (1992) commented that the domain of service quality may be factorially complex in some industries and very simple and unidimensional in others. In effect, they claim that the number of SQ dimensions is dependent on the particular service being offered. In their revised version, Parasuraman et al. (1991b) suggest two reasons for these anomalies. First, they may be the product of differences in data collection and analysis procedures. A more plausible explanation is that differences among empirically derived factors across replications may be primarily due to across-dimension similarities and/or within dimension differences in customers evaluations of a specific company involved in each setting. Spreng and Singh (1993) have commented on the lack of discrimination between several of the dimensions. In their research, the correlation between Assurance and Responsiveness constructs was 0.97, indicating that they were not separable constructs. They also found a high correlation between the combined Assurance-Responsiveness construct and the Empathy construct (0.87). Parasuraman et al. (1991b) had earlier found that Assurance and Responsiveness items loaded on a single factor and in their 1988 work had found average intercorrelations among the five dimensions of 0.23 to 0.35. In testing their revised version (Parasuraman et al., 1991b), Parasuraman and colleagues found that the four items under Tangibles broke into two distinct dimensions, one pertaining to equipment and physical facilities, the other to employees and communication materials. They also found that Responsiveness and Assurance dimensions showed considerable overlap, and loaded on the same factor. They suggested that this was a product of imposing a five-factor constraint on the analyses. Indeed, the additional degrees of freedom allowed by a subsequent six-factor solution generated distinct Assurance and Responsiveness factors. Parasuraman et al., (1991a) have now accepted that the five SERVQUAL dimensions are interrelated as evidenced by the need for oblique rotations of factor solutionsà ¢Ã¢â€š ¬Ã‚ ¦to obtain the most interpretable factor patterns. One fruitful area for future research, they conclude, is to explore the nature and causes of these interrelationships. It therefore does appear that both contextual circumstances and analytical processes have some bearing on the number of dimensions of SQ. 2.6: Contextual stability Carman (1990) tested the generic qualities of the SERVQUAL instrument in three service settings a tyre retailer, a business school placement centre and a dental school patient clinic. Following Parasuraman et al.,s suggestion, he modified and augmented the items in the original ten-factor SERVQUAL scale to suit the three contexts. His factor analysis identified between five and seven underlying dimensions. According to Carman, customers are at least partly context-specific in the dimensions they employ to evaluate SQ. In all three cases, Tangibles, Reliability and Security were present [7]. Responsiveness, a major component in the RATER scale, was relatively weak in the dental clinic context. Carman also commented: Parasuraman, Zeithaml and Berry combined their original Understanding and Access dimensions into Empathyà ¢Ã¢â€š ¬Ã‚ ¦ our results did not find this to be an appropriate combination. In particular he found that if a dimension is very important to customers they are likely to be decomposed into a number of sub-dimensions. This happened for the placement centre where Responsiveness, Personal attention, Access and Convenience were all identified as separate factors. According to Carman, this indicates that researchers should work with the original ten dimensions, rather than adopt the revised five-factor Parasuraman et al., (1988) model. 2.7: Item loadings In some studies (e.g. Carman, 1990), items have not loaded on the factors to which they were expected to belong. Two items from the Empathy battery of the Parasuraman et al., (1988) instrument loaded heavily on the Tangibles factor in a study of dental clinic SQ. In the tyre retail study, a Tangibles item loaded on to Security; in the placement centre a Reliability item loaded on to Tangibles. An item concerning the ease of making appointments loaded on to Reliability in the dental clinic context, but Security in the tyre store context. He also found that only two-thirds of the items loaded in the same way on the expectations battery as they did in the perceptions battery. Carman supplies other examples of the same phenomena, and suggests that the unexpected results indicate both face validity and a construct validity problem. In other words, he warns against importing SERVQUAL into service setting contexts without modification and validity checks. Among his specific recommendations is the following: We recommend that items on Courtesy and Access be retained and that items on some dimensions such as Responsiveness and Access be expanded where it is believed that these dimensions are of particular importance. He also reports specific Courtesy and Access items which performed well in terms of nomological and construct validity. Carman (1990) further suggested that the factors, Personal attention, Access or Convenience should be retained and further contextualised research work be done to identify their significance and meaning. 2.8: Item correlations Convergent validity and discriminant validity are important considerations in the measurement of second-order constructs such as SERVQUAL. One would associate a high level of convergent validity with a high level of intercorrelations between the items selected to measure a single RATER factor. Discriminant validity is indicated if the factors and their component items are independent of each other (i.e. the items load heavily on one factor only). Following their modified replication of Parasuraman et al.,s work, Babakus and Boller (1992) conclude that rules for convergence and discrimination do not indicate the existence of the five RATER dimensions. The best scales have a high level of intercorrelation between items comprising a dimension (convergent validity). In their development work in four sectors (banking, credit-card company, repair and maintenance company, and long-distance telecommunications company) Parasuraman et al., (1988) found inter-item reliability coefficients (alphas) varying from 0.52 to 0.84. Babakus and Boller (1992) report alphas which are broadly consistent with those of Parasuraman, varying from 0.67 to 0.83 (see Table III). In their 1991b version, Parasuraman et al. report alphas from 0.60 to 0.93, and observe that every alpha value obtained for each dimension in the final study is higher than the corresponding values in theà ¢Ã¢â€š ¬Ã‚ ¦original study. They attribute this improvement to their rewording of the 22 scale items. Spreng and Singh (1993), and Brown et al., (1993) are highly critical of the questionable application of alphas to difference scores. They evaluate the reliability of SERVQUAL using a measure specifically designed for difference scores (Lord, 1963). Spreng and Singh conclude that there is not a great deal of difference between the reliabilities correctly calculated and the more common [alpha] calculation, an observation with which Parasuraman et al., (1993) concurred when they wrote: The collective conceptual and empirical evidence neither demonstrates clear superiority for the non-difference score format nor warrants abandoning the difference score format. 2.9 Expectations (Operational Criticisms) Notwithstanding the more fundamental criticism that expectations play no significant role in the conceptualization of service quality, some critics have raised a number of other concerns about the operationalization of E in SERVQUAL. In their 1988 work, Parasuraman et al. defined expectations as desires or wants of consumers, i.e. what they feel a service provider should offer rather than would offer (emphasis added). The expectations component was designed to measure customers normative expectations (Parasuraman et al., 1990), and is similar to the ideal standard in the customer satisfaction/dissatisfaction literature (Zeithaml et al., 1991). Teas (1993a) found these explanations somewhat vague and has questioned respondents interpretation of the expectations battery in the SERVQUAL instrument. He believes that respondents may be using any one of six interpretations (Teas, 1993b): (1) Service attribute importance. Customers may respond by rating the expectations statements according to the importance of each. (2) Forecasted performance. Customers may respond by using the scale to predict the performance they would expect. (3) Ideal performance. The optimal performance; what performance can be. (4) Deserved performance. The performance level customers, in the light of their investments, feel performance should be. (5) Equitable performance. The level of performance customers feel they ought to receive given a perceived set of costs. (6) Minimum tolerable performance. What performance must be? Each of these interpretations is somewhat different, and Teas contends that a considerable percentage of the variance of the SERVQUAL expectations measure can be explained by the difference in respondents interpretations. Accordingly, the expectations component of the model lacks discriminant validity. Parasuraman et al. (1991b; 1994) have responded to these criticisms by redefining expectations as the service customers would expect from excellent service organizations, rather than normative expectations of service providers, and by vigorously defending their inclusion in SQ research. Iacobucci et al. (1994) want to drop the term expectations from the SQ vocabulary. They prefer the generic label standard, and believe that several standards may operate simultaneously; among them ideals, my most desired combination of attributes, the industry standard of a nominal average competitor, deserved SQ, and brand standards based on past experiences with the brand. Some critics have questioned SERVQUALs failure to access customer evaluations based on absolute standards of SQ. The instrument asks respondents to report their expectations of excellent service providers within a class (i.e. the measures are relative rather than absolute). It has be

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What Are Enzymes What Do They Do SAT / ACT Prep Online Guides and Tips Cells are the building blocks of life. All living things have cells - from small, single-celled organisms like bacteria to large, multicellular organisms like humans. Cells have specific functions that help keep these varied creatures alive and kicking. In order to make these functions happen, chemical reactions occur within cells. Oftentimes, these chemical reactions are sped up by a special kind of protein called an enzyme. In this article, we’re going to answer a few questions: what are enzymes, what are enzymes made of, and what types of enzymes exist. What Are Enzymes? Enzymes are a special kind of protein that are found in the cells of living organisms. Enzymes are made up of long chains of amino acids that are held together by peptide bonds. Enzymes help with processes like digestion, blood clotting, and hormone production. They basically either catalyze (cause) or speed up chemical reactions that take place in the bodies of living things. Typically, enzymes are only responsible for one kind of chemical reaction. So, an enzyme that helps with digestion won’t also help with blood clotting. We need enzymes because they help us initiate chemical reactions. For every chemical reaction, a specific amount of energy needs to be applied to the reactants before the reaction can happen. Enzymes help make reactions happen. It’s kind of like they are the final push over the hill before you can get a ball to roll down it. What Are Enzymes Made Of? As I mentioned in the previous section, enzymes are made up of long chains of amino acids. Some enzymes are only made up of one chain of amino acids, while others are made up of many chains of amino acids. Each enzyme is made up of a unique chain of amino acids (e.g., no two different types of enzymes have the same amino acid structure) and each enzyme also has its own unique shape. The 6 Types of Enzymes There are six main types of enzymes that occur in chemistry. Oxioreductases These types of enzymes help speed up oxidation and reduction reactions. Basically, they help move oxygen and hydrogen atoms from one place to another. Transferases These types of enzymes help groups of atoms transfer from molecule to molecule more quickly. Hydrolases These types of enzymes use water to break up single bonds between molecules. Many digestive enzymes are hydrolases and do things like break peptide bonds in proteins. Lyases Lyases are similar to hydrolases - they break up or form molecular bonds. Unlike hydrolases, they don’t use water as part of their reaction. Isomerases These types of enzymes speed up isomerization reactions. They rearrange the atoms of a reactant to form an isomer (a molecule with the same chemical formula, but different arrange of atoms than the original reactant). Ligases Ligases enhance the rate of joining of two molecules by using the energy obtained from the breakdown of ATP. How Different Types of Enzymes Work in the Body There are different types of enzymes in the body that can broadly be classified into metabolic, digestive, and food enzymes. Metabolic enzymes are responsible for reactions related to detoxification and energy production. Many of these enzymes occur in the liver, gallbladder, and pancreas. Metabolic enzymes help us use what we get from food (e.g., protein, fat, and carbohydrates) to make sure that our cells are working, clean, and healthy. Digestive enzymes help break down the foods we eat and convert them into energy. An example of these types of enzymes is the enzyme amylase, which breaks down starch into simple sugars.The three most importantdigestive enzymesare protease, amylase, and lipase, which digest protein, carbohydrate, and fat, respectively. Food enzymes aren’t naturally present in the body - we get them from the food we eat or from supplements. An example of a food enzyme is cellulase - we use cellulase to digest fibers, but it’s not produced in the body. We have to get it from the foods we eat. Food enzymes help digestive enzymes do their job and break down what we consume. Here are a few more types of enzymespresent in our body and what they do. Lactase: breaks down lactose, the complex sugar in milk products. Lactose-intolerant people don't have enough of this enzyme. Pectinase: breaks down pectin-rich foods such as citrus fruits, apples, carrots, potatoes, beets, and tomatoes. Bromelain: breaks down food protein into smaller peptones by hydrolysis; helps the body to fight cancer, improves circulation, and treats inflammation. Papain: aids the body in digestion. Catalase: breaks down hydrogen peroxide to water and oxygen. Plays an important role in immune function. Enzyme Definition: Key Takeaways Enzymes are an important kind of protein that help us do many things, including: Break down food into energy Detoxify parts of our body Form new bonds between molecules Enzymes are made of chains of proteins called amino acids. Some of them are naturally occurring in the human body and others we need to consume through food. What's Next? Writing a research paper for school but not sure what to write about? Our guide to research paper topics has over 100 topics in ten categories so you can be sure to find the perfect topic for you. Want to know the fastest and easiest ways to convert between Fahrenheit and Celsius? We've got you covered! Check out our guide to the best ways to convert Celsius to Fahrenheit (or vice versa). Are you studying clouds in your science class? Get help identifying the different types of clouds with our expert guide.

Leadership Research Paper Example | Topics and Well Written Essays - 250 words

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Strategic Assignment Example | Topics and Well Written Essays - 3000 words

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