Roles of barriers and gender in explaining consumers' chatbot resistance in banking: a fuzzy approach
Bibliographic record
Abstract
Purpose This study strives to better understand resistance to chatbots in the banking sector. To achieve this, it proposes a model based on the paradigm of resistance to innovation and the complexity theory. In addition, it explores the role of gender in relation to chatbot resistance. Design/methodology/approach Data are collected in France using a snowball sampling technique. The sample is composed of 385 participants. FsQCA is used to identify all possible combinations of usage, value, risk, tradition and image barriers, as well as two gender conditions that predict resistance to chatbots. Findings The results reveal that the sample provides four possible solutions/combinations that may explain resistance to chatbots. These are: (i) a combination of usage, value, risk and tradition barriers, (ii) a combination of value, risk, tradition and image barriers, (iii) a combination of usage, value, risk and image barriers, along with the male gender and (iv) a combination of usage, value, tradition and image barriers, along with the female gender. Research limitations/implications This study provides valuable and straightforward theoretical and managerial implications. The proposed solutions suggest a deep understanding of chatbot resistance. Chatbot developers and marketers can highly benefit from these findings to enhance user acceptance. Originality/value In this study, barriers are envisioned within the larger context of innovation resistance. The interactions among barriers causing resistance to chatbots are examined through the lens of the complexity theory, while the data analysis employs the fsQCA approach. Furthermore, this study sheds light on the role of gender in explaining chatbot resistance in the banking sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".