How to avoid common mistakes in experimental research?

Mattila, Anna S.; Luo, Anqi; Xue, Xunyue; Ye, Tian

doi:10.1108/ijchm-07-2020-0696

Cited by 35 publications

(36 citation statements)

References 25 publications

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“…In order to develop a prediction model, the sample size must be large enough to ensure stable coefficients. The larger the sample size, the more reliable results [ 56 ]. Using an inadequate sample size, the model may not predict well and be acceptable for future subjects [ 57 ].…”

Section: Discussionmentioning

confidence: 99%

Application of image processing and soft computing strategies for non-destructive estimation of plum leaf area

et al. 2022

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Plant leaf area (LA) is a key metric in plant monitoring programs. Machine learning methods were used in this study to estimate the LA of four plum genotypes, including three greengage genotypes (Prunus domestica [subsp. italica var. claudiana.]) and a single myrobalan plum (prunus ceracifera), using leaf length (L) and width (W) values. To develop reliable models, 5548 leaves were subjected to experiments in two different years, 2019 and 2021. Image processing technique was used to extract dimensional leaf features, which were then fed into Linear Multivariate Regression (LMR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and the Adaptive Neuro-Fuzzy Inference System (ANFIS). Model evaluation on 2019 data revealed that the LMR structure LA = 0.007+0.687 L×W was the most accurate among the various LMR structures, with R2 = 0.9955 and Root Mean Squared Error (RMSE) = 0.404. In this case, the linear kernel-based SVR yielded an R2 of 0.9955 and an RMSE of 0.4871. The ANN (R2 = 0.9969; RMSE = 0.3420) and ANFIS (R2 = 0.9971; RMSE = 0.3240) models demonstrated greater accuracy than the LMR and SVR models. Evaluating the models mentioned above on data from various genotypes in 2021 proved their applicability for estimating LA with high accuracy in subsequent years. In another research segment, LA prediction models were developed using data from 2021, and evaluations demonstrated the superior performance of ANN and ANFIS compared to LMR and SVR models. ANFIS, ANN, LMR, and SVR exhibited R2 values of 0.9971, 0.9969, 0.9950, and 0.9948, respectively. It was concluded that by combining image analysis and modeling through ANFIS, a highly accurate smart non-destructive LA measurement system could be developed.

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Section: Discussionmentioning

confidence: 99%

Application of image processing and soft computing strategies for non-destructive estimation of plum leaf area

et al. 2022

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“…Accordingly, our observations per variable were more than 60. Similarly, Mattila et al (2021) recommended recruiting more than 30 participants per treatment in the experimental designs in an online setting. The observations per treatment for the present study were more than 110, which is sufficiently higher than the minimum threshold.…”

Section: Methodsmentioning

confidence: 99%

Service recovery efforts' influence on consumers' desire to reciprocate and forgiveness: the mediating role of perceived justice

Umar

2022

SAJM

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PurposeThis paper investigated the impact of firms' service recovery efforts on consumers' desire to reciprocate and forgiveness in the hospitality industry of Pakistan. Additionally, this study examined the mediating role of perceived justice between service recovery efforts and their outcomes.Design/methodology/approachUsing snowball sampling technique, an online survey was administered and 259 responses were collected from casual-dining restaurant customers. A partial least squares structural equation modeling (PLS-SEM) and multivariate analysis of covariance (MANCOVA) were used to examine the hypotheses.FindingsThe results indicate that perceived justice significantly mediates the effect of service recovery efforts on the consumers' desire to reciprocate and forgiveness. Moreover, high (vs. low) service recovery efforts lead to high consumer forgiveness.Practical implicationsThe study provides insights for managers on how optimal recovery efforts predict consumers' positive responses and minimize the effect of service failure in South Asian consumers.Originality/valueThis research is among the early endeavors to examine consumers' desire to reciprocate in service recovery context. Also, this is the first study to validate the impact of service recovery efforts on consumers' desire to reciprocate and consumer forgiveness in a South Asian country.

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“…The purpose of including manipulation check questions in social science research is mainly to ensure that the treatment manipulation worked and successfully affected the latent variables that the researchers intended to measure and the manipulation did not affect other constructs in an unintended manner (Mattila et al , 2021; Perdue and Summers, 1986). The independent variables used in this study, service level and recovery type, were directly outlined in the scenario, and it was simple to confirm that these variables were manipulated as intended, unlike latent variables that cannot be altered directly.…”

Section: Methodsmentioning

confidence: 99%

Does social class matter in recovering self-service technology failures?

Kim

Jang

2021

IJCHM

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Purpose This paper aims to examine the interaction effect among the subjective social class, service level and recovery type on post-failure service evaluations (recovery satisfaction and willingness to spread positive word-of-mouth). Design/methodology/approach A total of 270 US consumers were recruited via Amazon MTurk. This study adopted a 2 (Subjective social class: high vs low) × 2 (Service level: luxury vs mid-scale) × 2 (Recovery type: customer self-recovery vs joint recovery) between subjects’ factorial design using a scenario-based survey method. Findings The results from the three-way multivariate analysis of covariance confirmed that a joint recovery is ineffective for high subjective social class individuals in a mid-scale hotel setting. Moreover, the moderated mediation analysis revealed that this tendency can be explained by high subjective social class individuals’ tendency to attribute blame externally to self-service technologies (SSTs). Practical implications The results of this study suggest that mid-scale hotels should deploy employees in the SST service area based on the profile of their main customers. If a mid-scale hotel is positioning itself to appeal to high subjective social class customers, then employees should be aware of the fact that customers may not be highly satisfied if they receive assistance. Originality/value This study expands the current knowledge on customers’ psychological differences based on subjective social class. Furthermore, the findings of this study contribute to academia by providing evidence of external attribution among high subjective social class individuals.

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How to avoid common mistakes in experimental research?

Cited by 35 publications

References 25 publications

Application of image processing and soft computing strategies for non-destructive estimation of plum leaf area

Application of image processing and soft computing strategies for non-destructive estimation of plum leaf area

Service recovery efforts' influence on consumers' desire to reciprocate and forgiveness: the mediating role of perceived justice

Does social class matter in recovering self-service technology failures?

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