Scale Development

Vooris, Ryan; Clavio, Galen

doi:10.1002/9781118901731.iecrm0229

Cited by 4 publications

(7 citation statements)

References 18 publications

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“…Retention of the factors was decided through Kaiser–Guttman criterion; factors having eigenvalues more than at least one were retained (Carpenter, 2018; Vooris & Clavio, 2017). The minimum loading in the study was 0.657, as explained in Table 3, which is above the minimum acceptable limit of 0.4 Carpenter (2018) and Nunnally and Bernstein (1994).…”

Section: Findings and Analysismentioning

confidence: 99%

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E-SERV-EX: A Multi-item Scale for Measuring Customer Expectations from the Online Retail Services

Tyagi,

Kumar,

Gulyani

et al. 2023

NMIMS Management Review

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Purpose: The purpose of this article is the scale development, refinement, and psychometric evaluation of the multi-item scale (E-SERV-EX) for assessing the customers’ expectations from the online retail services and exploring the impact of different demographic and behavioral factors on customer’s expectation. Design/Methodology/Approach: It was conclusive research, which is quantitative and cross-sectional in nature. Data were collected through a survey method using a structured questionnaire from 518 respondents, selected through judgmental sampling from Delhi NCT. The primary statistical tools used in the study were exploratory factor analysis, confirmatory factor analysis for scale development, and partial least square-structural equation modelling for hypothesis testing. Findings: The final scale had 31 items divided into nine dimensions. Assurance/trust, efficiency, fulfillment/reliability, responsiveness, security/privacy, web-design, personalization, price aspects, and customer engagement. Scales demonstrated good psychometric properties based on the findings from various reliability and validity tests conducted in this study. Web design was the most crucial factor, and personalization was the least important factor expected. When we speak about demographic factors, males had more expectations than females in individual and overall expectations. With an increase in age and income, customers’ expectations from online retailer services decrease. With the increase in distance from the physical retail outlet, customers’ expectations from online retail service increase. Consumers with more experience in internet usage and online retail usage had higher expectations. Consumers who surf and purchase more from online retailers also expect more. Practical Implications: The e-expectation scale developed in this study will help marketers and retailers better understand e-service quality expectations. Knowing the consumers’ expectations would help the retailers in framing the e-marketing mix and strategies. The expectations scale can be used in policy formulation and web designing. This scale will also help fill GAP 1 (expected service and management’s perceptions of consumer expectations) and GAP 5 (customer expectations and customer perceptions) of the service quality gap model of Parasuraman, Zeithaml, and Berry. Originality/Value: This research paper contributes to the literature by developing, refining, and evaluating a novel multi-item scale, E-SERV-EX, with good psychometric properties for measuring customer expectations from online retail services whereas most of the papers in the past measured the perceptions. E-SERV-EX can be employed by marketers, retailers, and policymakers to develop effective e-marketing strategies, web designs, and policy formulation by understanding consumer expectations. Most importantly, this scale helps fill GAP 1 (expected service and management’s perceptions of consumer expectations) and GAP 5 (customer expectations and customer perceptions) of the service quality gap model proposed by Parasuraman, Zeithaml, and Berry.

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Section: Findings and Analysismentioning

confidence: 99%

“…In addition, all of them were significant at the 0.001 level. Average Variance Extracted (AVE) of every factor was above 0.50 (ranged between 0.619 and 0.895) to demonstrate convergent validity (Hair et al, 2014; Vooris & Clavio, 2017). Additionally, all the composite reliability (CR) values were more than AVE values (Min.…”

Section: Findings and Analysismentioning

confidence: 99%

E-SERV-EX: A Multi-item Scale for Measuring Customer Expectations from the Online Retail Services

Tyagi,

Kumar,

Gulyani

et al. 2023

NMIMS Management Review

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Section: Scale Developmentmentioning

confidence: 99%

“…The scale development procedure followed in this study is based on five major steps that have been followed in earlier studies (i.e. Churchill, 1979; Gerbing and Anderson, 1988; Netemeyer et al , 1995; DeVellis, 2016; Vooris and Clavio, 2017; Clark et al , 2019):Qualitative approach:Step 1: Item generation; andStep 2: Item purification.Quantitative approach:Step 3: Item validation;Step 4: Confirmation of measurement model (CFA); andStep 5: Testing nomological validity.…”

Section: Review Of Literaturementioning

confidence: 99%

HEd-INNOSERV: perceived service innovation scale for the higher education sector

Manohar¹

2020

BIJ

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PurposeThe study aims to emphasize the need for an exclusive theory, approach and measurement scale for service innovation. In the past three decades, the importance of services and service-related industry has grown tremendously. Well-established scales used for research in manufacturing cannot be directly adopted and measured in the service industry. This article follows the synthesis approach by including both technological and non-technological typologies for measuring service innovation. This is followed by reporting the effect of service innovation on outcome performances. The context of the study is the higher education sector.Design/methodology/approachAn integrated research design was used to collect data from students in various parts of south India. In total, two focus group discussions and three in-depth interviews were conducted for item generation. Confirmatory factor analysis was performed for the reliability and validity of the scale. The study developed the HEd-INNOSERV scale consisting of seven dimensions comprising 34 items.FindingsThe study developed the HEd-INNOSERV scale consisting of seven dimensions comprising 34 items. The empirical results demonstrate that the scale is reliable, valid and generalizable across higher education institutions (HEIs). The scope for future research is to develop a generalized scale that can measure across the entire service sector.Research limitations/implicationsThe scale shall help researchers in testing the conceptual models earlier developed in the service innovation domain. Similarly, HEIs could measure their stakeholders' perceptions of their innovation activity. Further, the result indicates that innovation enhances the reputation of the institution, which ultimately results in positive word of mouth.Practical implicationsCommercialization of the scale by developing an appropriate algorithm would help institutions in measuring their innovation-led initiatives continually and establish quality and standards. The scale can be used complementarily with other measures adopted from regulatory and rating agencies.Originality/valueThe HEd-INNOSERV scale shall help in optimizing the existing business processes of HEIs by helping them consciously introduce appropriate technological and non-technological innovations.

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“…Finally, any psychological measuring instrument requires an analysis of the psychometric qualities of the perceptual constructs and questionnaires based on them in terms of validity, reliability, and measurement invariance. Techniques and criteria for this purpose have been developed extensively in the social sciences (Vooris and Clavio 2017). Typical requirements for psychological questionnaire instruments comprise the use of latent measurement models, the demonstration of convergent and discriminant validity ("Do the scale's subdimensions actually measure what they are supposed to measure and are sub-dimensions sufficiently different from each other?…”

Section: Psychometrics and Scale Development In Room Acousticsmentioning

confidence: 99%

The Language of Rooms: From Perception to Cognition to Aesthetic Judgment

Weinzierl

Lepa

Thiering

2020

Modern Acoustics and Signal Processing

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Rooms are not perceptual objects themselves; they can only be perceived through their effect on the presented signal, the sound source, and the human receiver. An overview of different approaches to identify the qualities and the dimensions of "room acoustical impression" will be provided, that have resulted in psychological measuring instruments for room acoustical evaluation from the audience perspective. It will be outlined how the psychoacoustic aspects of room acoustical perception are embedded in a socio-cultural practice that leads to an aesthetic judgment on the quality of performance venues for music and speech.

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Scale Development

Cited by 4 publications

References 18 publications

E-SERV-EX: A Multi-item Scale for Measuring Customer Expectations from the Online Retail Services

E-SERV-EX: A Multi-item Scale for Measuring Customer Expectations from the Online Retail Services

HEd-INNOSERV: perceived service innovation scale for the higher education sector

The Language of Rooms: From Perception to Cognition to Aesthetic Judgment

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