The experience sampling method: examining its use and potential in tourist experience research

Cutler, Sarah; Doherty, Sean; Carmichael, Barbara A.

doi:10.1080/13683500.2015.1131670

Cited by 34 publications

(14 citation statements)

References 48 publications

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“…A search for recent journal articles in which the ESM is employed illustrates the method's popularity and versatility, yielding studies into widely varying topics such as the social experiences of children with Asperger syndrome (Cordier et al, 2016 ); tourists' experiences (Quinlan Cutler et al, 2016 ); involuntary musical imagery (Floridou and Müllensiefen, 2015 ); affect regulation (Catterson et al, 2016 ); body satisfaction and self-esteem in adolescents (Fuller-Tyszkiewicz et al, 2015 ); and chronic fatigue symptom fluctuations (Band et al, 2015 ). The ESM has been most frequently applied, however, in the field of emotion dynamics, mood and psychopathology (e.g., Larson et al, 1980 ; Ebner-Priemer and Sawitzki, 2007 ; Brans et al, 2013 ; for a review of ESM studies in the context of mood disorders, see Ebner-Priemer and Trull, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data

et al. 2017

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The Experience Sampling Method is a common approach in psychological research for collecting intensive longitudinal data with high ecological validity. One characteristic of ESM data is that it is often unequally spaced, because the measurement intervals within a day are deliberately varied, and measurement continues over several days. This poses a problem for discrete-time (DT) modeling approaches, which are based on the assumption that all measurements are equally spaced. Nevertheless, DT approaches such as (vector) autoregressive modeling are often used to analyze ESM data, for instance in the context of affective dynamics research. There are equivalent continuous-time (CT) models, but they are more difficult to implement. In this paper we take a pragmatic approach and evaluate the practical relevance of the violated model assumption in DT AR(1) and VAR(1) models, for the N = 1 case. We use simulated data under an ESM measurement design to investigate the bias in the parameters of interest under four different model implementations, ranging from the true CT model that accounts for all the exact measurement times, to the crudest possible DT model implementation, where even the nighttime is treated as a regular interval. An analysis of empirical affect data illustrates how the differences between DT and CT modeling can play out in practice. We find that the size and the direction of the bias in DT (V)AR models for unequally spaced ESM data depend quite strongly on the true parameter in addition to data characteristics. Our recommendation is to use CT modeling whenever possible, especially now that new software implementations have become available.

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Section: Introductionmentioning

confidence: 99%

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data

et al. 2017

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“…Introducing random signaling and attempting to explore intrapsychic phenomena distinguish the current forms of ESM from earlier forms (Scollon et al , 2003). Data collection for ESM includes both qualitative data, such as repeated open-ended questions (Quinlan Cutler et al , 2018), and quantitative data, such as repeated questionnaires (Sloboda et al , 2001). In this paper, the quantitative data approach is discussed, including the issues relating to data collection and data analysis.…”

Section: An Overview Of Experience Sampling Methodsmentioning

confidence: 99%

“…Many influential management and psychology journals, such as the Academy of Management Journal, Journal of Applied Psychology, Journal of Occupational Health Psychology and Journal of Organizational Behavior , are increasingly publishing studies that used the ESM to collect data. However, very few studies in hospitality management have applied this advanced method to explore short-term fluctuating variables (Ritchie and Hudson, 2009; Quinlan Cutler et al , 2018). The purpose of this paper is to introduce ESM to researchers in hospitality management and engage them in the use of ESM in their future research.…”

Section: Introductionmentioning

confidence: 99%

Applications of the experience sampling method: a research agenda for hospitality management

et al. 2020

IJCHM

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Purpose This study aims to provide researchers in hospitality management with a comprehensive understanding of the experience sampling method (ESM) and to engage them in the use of ESM in their future research. With this critical discussion of the advantages and challenges of the method, researchers can apply it appropriately to deepen and broaden their research findings. Design/methodology/approach This study chooses an empirical example in the context of hotel employees’ surface acting, tiredness and sleep quality to illustrate the application of ESM. Based on the example, this paper conducts a two-level modeling in Mplus, including a cross-level mediation analysis and mean centering. Findings This paper demonstrates the applicability and usefulness of ESM for hospitality research and provides a detailed demonstration of how to use the statistical program Mplus to analyze ESM data. With this paper, researchers will be able to consider how to engage ESM in their future studies. Originality/value To the best of the authors‘ knowledge, this paper is among the first to provide a hands-on demonstration of ESM to hospitality researchers. The authors call for more research in hospitality management to use ESM to answer complex and pressing research questions.

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“…Other scholars have looked at the segmented phases of leisure experiences and their effect on future participation (Harmon and Dunlap 2018 ), as well as the continuation of leisure experiences after participation (Scott and Harmon 2016 ), both key aspects of understanding the role of temporality, particularly as it relates to continuity in leisure repertoires. Likewise, with the interest in intentional leisure experience design, there has been more recent attention paid to the temporal episodes that occur within experiences and the methods to study these across the duration of an experience (e.g., experiential sampling method; see Ito et al 2019 ; Quinlan Cutler et al 2018 ; Zajchowski et al 2017 ).…”

Section: Introductionmentioning

confidence: 99%

A Moment in Time: Leisure and the Manifestation of Purpose

Harmon

Duffy

2020

Int J Sociol Leis

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There has been little consideration given to understanding the concept of time within leisure. Just what is time when considered as an ordering mechanism of our leisure behaviors? Most leisure research has approached the concept of time through a largely Western, monochronic understanding which emphasizes time for its linear ordering and quantifiable qualities. The dominance of this implicit understanding of time is also notably influenced by pressing ideologies that define Western society, such as neoliberalism, which can distort our personal discourse with our own time: we see it as a commoditysomething to be used efficiently and to be invested. What this thoughtpiece aims to do is consider the existential properties of time, particularly the "moment," as an opportunity to "achieve [the] total realization of a possibility" as illustrated by Lefebvre.

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The experience sampling method: examining its use and potential in tourist experience research

Cited by 34 publications

References 48 publications

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data

Applications of the experience sampling method: a research agenda for hospitality management

A Moment in Time: Leisure and the Manifestation of Purpose

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