Persona:
Rodrigo Moya, Beatriz

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ORCID
0000-0003-1619-5714
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Rodrigo Moya
Nombre de pila
Beatriz
Nombre

Filtros

2020 - 20222
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Autor: Lorente Martínez, Javier × search.filters.applied.f.itemDepartamento: Organización de Empresas ×

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Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Analysis of the adoption of customer facing in-store technologies in retail SMEs
    (Elsevier, 2020-11) Lorente Martínez, Javier; Navío Marco, Julio; Rodrigo Moya, Beatriz
    Brick and mortar stores are suffering the dramatic revolution of the retail sector. Customer facing in-store technologies (CFIST) are a key component of the inevitable transformation of retail stores; yet the reasons to adopt such technologies by business owners may be little known. Based on a TOE and TAM inspired framework, this study analyses the drivers of such decision by small and medium size enterprises using a survey methodology. The results show that the attitude towards technology is the strongest predictor of the intention to adopt CFIST, highlighting the role of the top management in technology decisions. This conclusion has important implications for practitioners. This research is the first to address the adoption of CFIST by SMEs and therefore set the path for further studies about the impact and adoption of in-store technology in SMEs.
  • Miniatura
    Publicación
    Are retailers leveraging in-store analytics? An exploratory study
    (Emerald, 2022-05-03) Lorente Martínez, Javier; Navío Marco, Julio; Rodrigo Moya, Beatriz
    Purpose The purpose of this study is to analyse the level of adoption of in-store analytics by brick-and-mortar retailers. Web analytics technology has been widely adopted by online retailers, and the technology to gather similar information in physical stores is already available. This study explores how such technology is valued and adopted by retailers. Design/methodology/approach This study is based on interviews and a focus group of 21 retail executives using a semi-structured interview methodology. An in-store analytics service was defined, along with specific key performance indicators (KPIs) and use cases to structure respondents' feedback. Findings Although noteworthy differences have been found in the value of KPIs and use cases by type of business, the main finding is that none of the respondents reached the stage of a brick-and-mortar data-driven company. In-store analytics services are in the early stages of Rogers' (1983) model of diffusion of innovations. Three main reasons are presented: lack of technology knowledge, budget priority and a data culture inside the companies. Practical implications The results should encourage scholars to further investigate the drivers accelerating the adoption of these technologies. Practitioners and solution providers should strive for improvement in the simplicity of their solutions. Originality/value This study is the first to analyse the level of adoption of in-store analytics from the perspective of retailers.