CAMPOS REQUENA, NELYDA AURORA
Preferred name
CAMPOS REQUENA, NELYDA AURORA
Main Affiliation
Email
ncamposr@udd.cl
ORCID
0000-0001-5214-0273
Scopus Author ID
57218763693
8 results
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Item type:Publication, Optimal pricing of protected areas under multiple sites demand models(Elsevier BV, 2025-07); 9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Distributional justice and hydropower development: A case study of Chile's equity tariff scheme(Elsevier BV, 2025-03); ; Scopus© Citations 1 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Eco‐Innovation: Drivers and Obstacles for Agriculture Firms in a Developing Country(Wiley, 2025-11-14); ; ; <jats:title>ABSTRACT</jats:title> <jats:p>Sustainable agriculture is becoming increasingly vital for food security and economic growth, particularly as the agricultural sector grapples with challenges posed by climate change. This study, grounded in the environmental innovation theory, resource‐based view, and dynamic capabilities approach, uses logistic and multinomial logistic regression models to evaluate the eco‐innovation behavior of agricultural firms in Chile. We focus on the impact of technology push, market pull, regulatory push or pull, and firm‐specific factors on eco‐innovations. Our key findings reveal that cost savings, market factors, non‐R&D through external knowledge acquisition, cooperation with other agricultural firms, and firm size are the most significant drivers of eco‐innovation in this context. Our results diverge from typical findings in the literature regarding the influence of regulatory push/pull factors, as we observed no significant effects of these variables. This study contributes to the understanding of eco‐innovation drivers in agriculture, an understudied sector in developing countries. This study provides crucial insights for policymakers and industry stakeholders who aim to enhance sustainability practices in response to global environmental challenges.</jats:p>Scopus© Citations 3 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A comparison of mixed logit and latent class models to estimate market segments for seafood faced with ocean acidification(2022); ; 1Scopus© Citations 3 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Preference-based Segments from Mixed Logit, Latent Class, and Latent Class Mixed Logit Models: A Monte Carlo Comparison(Springer Science and Business Media LLC, 2025-07-15); 36Scopus© Citations 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Market segmentation under the choice modelling framework<jats:p>This thesis examines market segmentation from an economic perspective by drawing on the choice modelling framework. The market segmentation literature focuses on providing information to marketers about customers, especially about their wants, needs and preferences regarding different products or services. A challenge confronting marketing researchers is to improve the estimation of market segments. The latent class model (LCM) has been extensively used to capture consumers’ heterogeneity and identify market segments. However, some questions remain unanswered. How reliable and effective is the LCM in capturing customer heterogeneity, especially unobserved heterogeneity? How are estimation results affected if customers do not consider all information provided in the choice tasks; that is, when attribute non-attendance (ANA) occurs? To answer these questions, this thesis assessed the LCM in two ways. First, it was compared with an alternative model––the mixed logit model (MLM)––and the role of individual-specific posterior distributions (ISPs) was evaluated to account for unobserved heterogeneity and identify market segments. Second, the thesis assessed the role of ANA in modelling and identifying market segments. This thesis consists of three studies. The first study identifies market segments by examining customer heterogeneity from the ISP in the MLM and the LCM. When using the ISP in the LCM as the basis for segmentation, there is an explicit recognition that class membership is probabilistic. The identified market segments are compared in terms of the number of customers in each segment and their characteristics. The results suggest differences in both customer number and characteristics. The second study performs a Monte Carlo simulation to determine the impact of consumer heterogeneity on the accuracy of the LCM and the MLM in identifying market segments. The design comprises four experiments with two levels of heterogeneity (low and high) and the presence or absence of small (niche) segments. The results showed that the accuracy of the models is contingent on the level of heterogeneity of individuals. Specifically, when heterogeneity is low, segments estimated by the LCM are more precise; however, when heterogeneity is high, the MLM outperforms the LCM. The results also suggest that using ISPs as the basis for segmentation in the LCM makes it possible to identify market segments more accurately when heterogeneity is high. In the presence of niche segments, there was no evidence of one model outperforming the other. The third study accounts for ANA in identifying market segments using an LCM. Images were added to text, allowing consumers to visualise attribute levels. This was used to reduce ANA as a coping mechanism for complex tasks, thereby better capturing genuine consumer preferences. The results showed that inferred ANA combined with stated ANA in the LCM improves model performance. Moreover, using images to present attribute levels improves model performance when ANA is accounted for in the estimation</jats:p>1 3