Journal of Marketing Analytics最新文献

This study assessed airport service quality by conducting importance-performance analysis (IPA) of user-generated content and examining the usefulness of a priori segmentation in the airport industry. The data were drawn from 35,138 Web reviews of airports worldwide shared online via the Skytrax website. Importance ratings were derived using the indirect method based on an artificial neural network. The results reveal that the most important attributes are staff and queuing time. The findings also include that service quality attributes’ importance and priority areas needing improvement vary according to traveler type, airport experience category, and region of origin. This study produced valuable insights into how airports can use IPA to leverage their passengers’ online reviews in order to enhance service quality and address customer heterogeneity.

Environmental and market factors play an essential role in shaping the evaluation of the strategy and performance of organizations as well as moderating these relationships. This study examines how the effectiveness of different marketing actions changes in the passenger car industry by considering brand-based data during economic contraction, which most countries experience, especially during and after the COVID-19 era. Different marketing activities’ effectiveness means examining each action’s impact on performance. Performance is measured by sales volume and market share through quarterly data between 2010 and 2018, while advertising, pricing, and distribution are considered marketing actions in this study. The model is estimated using the fixed effect panel estimated generalized least square. The results support that price competitiveness and investing in advertising, including social media during recessions, are two critical factors enhancing performance through sales volume in the car segment covered in the study. In addition, distribution intensity positively influences the sales volume of each car brand and model, while this effect does not change during the recession. In the case of performance measurement through market share, the total impact of advertising expense on performance during a downturn is found to be zero in contrast to the sales volume-based analysis. The price-related result is found to be in a similar direction, while distribution hurts performance during economic contraction. Unlike most studies in the literature, the study offers the opportunity to discuss the impact of economic contradiction, both in an understudied industry and in a sub-sector/segment where competition and sales are intense.

In 2007, the European Commission introduced the term “dual career” (DC) to indicate the specific challenges elite sportspersons face in combining a sports career with a work career. Considering that companies are encouraged to have a social role through their Corporate Social Responsibility (CSR), the implementation of DC could contribute to the advancement of the European DC discourse through internal strategies that are aligned with the communicated CSR-based external image. Thus, the present study aims at understanding employees-sportspersons’ perceptions and their potential contributions to the value of the brand they work for. Starting from a knowledge base of 22 in-depth interviews administered to a sample of athletes and coaches from eight different European countries, a content analysis has been conducted using the hermeneutic approach and qualitative datamining through a CAQDAS tool (T-Lab). Results show that employee-sportspersons possess specific capabilities, such as time management and teamworking, which could significantly contribute to the brand value. However, these capabilities are not sufficiently recognized by the employer brand, showing a misalignment between the promised brand social commitments and the actual delivery of such promises, thereby undermining the authenticity of the brand’s social-driven aims and the overall authenticity of the brand’s CSR-based commitments.

This article describes a data-driven case study for teaching and assessing students’ skills in marketing analytics, specifically in pricing. This case study combines teaching econometrics to analyze data and substantive marketing to derive managerial insights. The econometric challenge requires students to set up and implement a regression analysis to derive the demand function, detect multicollinearity, and select appropriate data visualizations. The substantive challenge requires deriving optimal pricing decisions and understanding how the parameters of the demand function impact optimal prices and the associated profit. We test the case study in a marketing analytics exam and discuss the performance of 134 students. Beyond assessing student performance in an exam, the case study facilitates teaching through in-class group work or assignments. Free of charge, under a liberal CC BY license, we encourage other educators to use the case study in their teaching. We provide the necessary data and a sample solution using the statistical programming language R.

The impact of big data analytics capabilities (BDACs) on firms’ sustainable performance (SP) is exerted through a set of underlying mechanisms that operate as a “black box.” Previous research, from the perspective of IT-enabled capabilities, demonstrated that a serial mediation of supply chain management capabilities (SCMCs) and circular economy practices (CEPs) is required to improve SP from BDACs. However, further insight regarding the role of BDACs in the processes of SP creation can be provided by deploying complementary analytics techniques, namely importance-performance map analysis (IPMA), necessary condition analysis (NCA), and combined importance-performance map analysis (cIPMA). This paper applies these techniques to a sample of 210 Spanish companies with the potential for circularity and environmental impact. The results show that BDACs exert a positive total effect toward achieving SP. However, companies still have the potential to improve and benefit from these capabilities. In addition, BDACs are a necessary condition (must-have factor) for all dependent variables in the model, including SP. In this case, high levels of BDACs are required to achieve excellence in SP, justifying organizational initiatives that prioritize the improvement of BDACs to achieve SP goals.

In today’s fiercely competitive business landscape, startups face numerous challenges in achieving scalability and sustainability. The integration of cutting-edge technologies such as Artificial Intelligence (AI), blockchain, and the Internet of Things (IoT) presents promising solutions to these challenges. However, understanding the intricacies of technology acceptance within the startup environment becomes paramount. Technology Acceptance Models (TAMs) have long served as foundational frameworks for understanding technology adoption and integration, but their effectiveness is hindered by inherent limitations. These limitations demand further exploration, particularly when viewed through the entrepreneurial lens. This paper offers a comprehensive analysis of the strengths and limitations inherent in TAM and its extensions, alongside other prominent technology acceptance models. By incorporating an entrepreneurial perspective, the analysis reveals additional challenges stemming from the dynamic nature of startup ecosystems. From a pragmatic standpoint, this paper provides actionable insights for technology-driven entrepreneurial organizations to navigate innovation and technology adoption decisions more intelligently. From a theoretical perspective, it contributes to the refinement and evolution of technology acceptance models, particularly in the context of entrepreneurial ventures. In light of these limitations, the paper offers strategic recommendations for future research endeavors. These include encouraging interdisciplinary collaboration, contextualizing models to suit startup dynamics, conducting longitudinal studies to capture evolving user perceptions, accounting for individual differences in technology adoption, and validating emerging models to reflect contemporary realities. Emphasis is placed on the entrepreneurial imperative of agility and adaptability in navigating the ever-changing landscape of technology acceptance. Moreover, the paper underscores the importance of a multidisciplinary approach and delineates practical implications for organizations and practitioners aiming to sustain technology acceptance and successful implementation within dynamic startup environments. By addressing these constraints, researchers can pave the way for the development of more robust and comprehensive models, better equipped to clarify and predict technology acceptance and usage patterns. Ultimately, this research underscores the critical need for ongoing refinement and innovation within the realm of technology acceptance, providing actionable insights to propel both scholarly discourse and entrepreneurial practice forward.

This article provides a review of the BANOVA R package and an illustration of its uses in Marketing Analytics. The package allows users to conduct regression analyses and analysis of variance for between-subjects, within-subjects, and mixed designs, where the dependent variable follows one of a variety of continuous or discrete distribution functions and the data may have a hierarchical structure. The package uses stan as the underlying computing engine, and enables the calculation of simple effects, floodlight analysis, and mediation analysis. The R package is illustrated through a reanalysis of the observational data by Blake et al. (Psychol Sci 32:315–325, 2021) on the relationship between misogynistic tweets and domestic violence, and of the experimental data by Srna et al. (Psychol Sci 29:1942–1955, 2018) on the perception of multitasking.

This study analyses the evolution of people’s sentiment towards Work from Home (WFH)-related products during the pandemic, using user-generated content from social media platform X on responses for the largest US online furniture stores. We find that people interacted more about WFH products during the Covid-19 lockdowns, but sentiment towards WFH products worsened. For some online furniture stores, Covid-19 restrictions may explain the changes in sentiment, but firms’ idiosyncrasies also play a role. The methodology of this study allows companies to assess the impact of external effects on customers’ sentiments, allowing them to identify specific problems and to connect more naturally with their customers.