Journal of Marketing Analytics最新文献

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.

Generative Artificial Intelligence (GAI) marks a groundbreaking shift in research. Unlike traditional AI, GAI can generate novel insights and content using natural language processing. Using case study methodology, this paper explored GAI's application in identifying research gaps in aviation's use of Additive Manufacturing (AM), focusing on Design Optimization. Recent advances, such as ChatGPT-4, enable GAI to process extensive data and recognize complex patterns. The research method includes paper selection, GAI-driven gap analysis, and thematic extraction. Generative AI uncovered research domains but has limitations in content attribution and accuracy. Nevertheless, GAI promises to revolutionize knowledge discovery and problem-solving across various fields.

With the increasing amount of data being generated, marketers and marketing researchers face the challenge of effectively analyzing and interpreting insights from the data. The volume of data poses challenges for humans; however, using automated content analysis techniques frees the researcher to focus on the distilled data. Even though multiple forms of text analysis techniques have been discussed in prior marketing literature, few articles simplify the techniques enough to allow for easy adoption by readers. This article discusses three text analysis techniques and then applies these techniques to a dataset of 1287 newspaper articles following the major demonetization announcement in India. It provides an interesting insight into the life of the Indian citizen faced with a government-mandated drive that demonetized 86% of the currency, endangering everyday retail transactions in a cash-dominated economy. Interesting insights emerging from simple techniques such as comparative word frequencies and sentiment analysis are presented which highlight the coping techniques used by the people to continue retail transactions. The initial desperation led to attempts to use the demonetized currency notes by splurging on gold, liquor, and fuel. Once the awareness about the absence of valid currency seeped in, people focused on more thought-out attempts to sustain normal retail transactions. Further, topic modeling was applied to discover the underlying topics in the data corpus, which further revealed the repertoire of coping strategies used by the people. A topic that stood out in the analysis was related to retail-focused mobile payment services, which subsequently found large-scale acceptance in the economy. The article drives home the point that while automated content analysis may provide a quick and simplified view of the data, the role of the researcher in qualitatively interpreting the data is not trivial.

Businesses today operate in a digital transformation era which relates to accumulation of internet-based technologies that reshape how companies derive insights that affect innovation. With the spread of technologies, managers face dilemmas how to relate to existing technologies. One prominent contemporary technology is big data (BD). While research articulates its importance, it ignores its combination with existing traditional systems (i.e., small data; SD) and how firms cultivate this development. This study examines the impact of SD, BD that constitute marketing analytics, and their intersected relationship on data-driven insights en route implications on marketing innovation. Based on quantitative and qualitative studies and ambidexterity framework, the study proposes the combination of SD and BD (data ambidexterity) as key driver of marketing insights and innovation. A conceptual model was tested using regressions, path analyses, and robustness checks. Findings show that new data technologies should not overshadow older ones and suggest a hierarchy of analytic effects (SD < BD < SD × BD). While BD has a stronger effect than SD on insights, their combination is more beneficial than each in isolation. Moreover, the effect of this combination on marketing innovation is mediated through data-driven insights. The study addresses the paucity of research on SD and BD, proposes a more holistic approach, extends the potential of marketing analytics, and exhibits theoretical and practical implications of data ambidexterity.

This study examines the interplay between entrepreneurial marketing orientation and business performance among SMEs in the services sector, with a particular focus on competitive aggressiveness as a mediating factor. Drawing from a sample of 320 service-based companies via both online and paper-based surveys, the research employs structural equation modelling using AMOS software to analyse the data. The results underscore a significant positive relationship between entrepreneurial marketing orientation and business performance among service-based SMEs in Jordan. By delving into the unique marketing challenges and opportunities faced by these enterprises, this research not only offers actionable insights for practitioners but also enriches the entrepreneurial marketing discourse. This exploration delves into the domain of entrepreneurial marketing orientation, emphasizing its critical role in enhancing SME competitiveness and growth within a developing economy. Incorporating marketing analytics, the study offers a detailed understanding that enriches academic literature and informs policy development for sustainable economic progress.

Mindfulness as a concept has existed for thousands of years in the Buddhist tradition and other meditative practices. The Buddhist path to nirvana incorporates the ability to be “in the moment.” This research aims to examine how mindfulness can affect impulse buying. Using personality traits from the Big 5 Personality Traits and the 3M Model of Motivation and Personality, we construct a model to study the predictive relationships to Mindfulness. Results show that mindfulness is related to impulse buying, either affectively or cognitively. The more mindful the consumer is, the less likely he/she will make an impulse buy. Additionally, consciousness did not positively affect the need for arousal or material things. The more conscientious you are, the less the need to be aroused. This indicates that companies need to differentiate between very conscientious consumers and consumers who are not. The more a consumer looks for information, the less likely any marketing cues, such as advertisements or social media, will arouse him/her. This study can help marketers understand the most critical personality traits affecting mindfulness and impulse buying.