| 1. |
|
|
| 2. |
|
|
| 3. |
- Otterbring, Tobias, 1985-, et al.
(author)
-
Positive gender congruency effects on shopper responses : Field evidence from a gender egalitarian culture
- 2021
-
In: Journal of Retailing and Consumer Services. - : Elsevier. - 0969-6989 .- 1873-1384. ; 63
-
Journal article (peer-reviewed)abstract
- This field study examined how customer-employee interactions are affected by the congruency between an employee's gender and the perceived gender image of the consumption context in one of the most gender equal cultures in the world (Scandinavia). Mystery shoppers had a service encounter with an employee across a set of physical commercial settings that were classified according to their gender image. The mystery shoppers noted the gender of the employee, provided employee evaluations, and indicated word-of-mouth (WOM) ratings. Shoppers who had a gender congruent service encounter (e.g., a female employee in a “feminine” consumption context) reported more favorable employee evaluations and WOM ratings than shoppers who had a gender incongruent service encounter (e.g., a female employee in a “masculine” consumption context), with the impact of gender congruency on WOM ratings mediated by employee evaluations, particularly with respect to competence inferences. These findings highlight the ethical dilemma of a positive gender congruency effect, as it can generate superior consumer responses but also risks resulting in gender occupational segregation.
|
|
| 4. |
- Rahal, Manal, et al.
(author)
-
Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework
- 2024
-
In: ECBS 2023. - : Springer. - 9783031492518 - 9783031492525 ; , s. 42-59
-
Conference paper (peer-reviewed)abstract
- Creating resilient machine learning (ML) systems has become necessary to ensure production-ready ML systems that acquire user confidence seamlessly. The quality of the input data and the model highly influence the successful end-to-end testing in data-sensitive systems. However, the testing approaches of input data are not as systematic and are few compared to model testing. To address this gap, this paper presents the Fault Injection for Undesirable Learning in input Data (FIUL-Data) testing framework that tests the resilience of ML models to multiple intentionally-triggered data faults. Data mutators explore vulnerabilities of ML systems against the effects of different fault injections. The proposed framework is designed based on three main ideas: The mutators are not random; one data mutator is applied at an instance of time, and the selected ML models are optimized beforehand. This paper evaluates the FIUL-Data framework using data from analytical chemistry, comprising retention time measurements of anti-sense oligonucleotide. Empirical evaluation is carried out in a two-step process in which the responses of selected ML models to data mutation are analyzed individually and then compared with each other. The results show that the FIUL-Data framework allows the evaluation of the resilience of ML models. In most experiments cases, ML models show higher resilience at larger training datasets, where gradient boost performed better than support vector regression in smaller training sets. Overall, the mean squared error metric is useful in evaluating the resilience of models due to its higher sensitivity to data mutation.
|
|
