Marketing Analysis
Author: Gaurav Chopade
Methods for Latent Moderation Analysis in Marketing Research
Pieters et al (2022) emphasize that the study compares six methods for latent moderation analysis and offers valuable recommendations. Notably, the choice of methods depends on the researcher’s discretion, particularly when reliabilities of moderation variables . The factor scores method and the latent product method are generally recommended across various conditions, demonstrating small biases and standard errors. The multigroup, corrected means, and product indicators methods are deemed suitable for specific settings, while the means method is discouraged due to significant biases, especially at lower reliabilities. The two-step factor scores (TSFS) method is introduced and found to perform well, providing accessibility and small biases. The study opens avenues for further research, suggesting exploration of Bayesian estimation, consideration of correlated measurement errors, and extension of simulations to multilevel or multitime data. Overall, the study aims to enhance moderation theory testing, urging researchers to adopt more reliable methods for latent moderation analysis.
A Bibliometric Analysis of Research on Digital Marketing
BANSAL, P et al (2023) state that the comprehensive analysis of digital marketing publications from 1989 to 2022 reveals a significant surge since 2002, highlighting the transformative impact of digital advancements on marketing strategies. The study identifies notable contributors, prestigious journals, emerging keywords, and publication trends, showcasing a global interest in the field. The dominance of the US and increasing citations underscore the field’s importance. Exploring the impact of cutting-edge technologies, understanding influencer dynamics, assessing content marketing’s long-term effects, and studying international strategies in developing markets are crucial directions for future research. The study’s limitation to the Scopus database suggests the need for broader database exploration, such as the Web of Science. This research serves as a valuable framework for future studies, guiding researchers in emerging areas of digital marketing, particularly in the context of innovative technologies like artificial intelligence and Big Data. As the digital marketing landscape evolves, these insights pave the way for defining and optimizing successful marketing practices in the future.
Study on Analysis of Customer Satisfaction towards Digital Marketing
S., J. et al (2023) state that the study’s revelations on consumer behavior emphasize a growing inclination towards digital channels, driven by a preference for online interactions and the broader trend of industries embracing digitization. Factors such as time constraints, financial considerations, and health concerns contribute to consumers favoring reputable online retailers with a focus on quality packaging. The trust placed in these platforms extends to the belief that reputable websites are secure against payment system fraud. Considering these aspects, it’s reasonable to conclude that digital marketing, bolstered by customer satisfaction, is poised to supersede traditional marketing methods in the near future.
A Prototyping Analysis of Relationship Marketing
JONES, T. et al (2018) state that the evolution of marketing constructs is intricately tied to everyday experiences, leading to an accumulation of terminology over time. This abundance of words can, however, impede clarity and hinder the field’s progress. To address this issue, they propose embracing prototyping as an organizational method for cohesive terminology. Recognizing that constructs are foundational to social science research, any confusion in their usage has significant implications for both research and practice. The study sheds light on past confusions, outlines potential pitfalls, and offers a pathway for advancing the field with increased clarity and precision in terminology.
Social Commerce Marketing Experimentation through Conjoint Analysis
KAUR, K. & KUMAR, S. (2021) state that the discrete model analysis reveals a reverse relationship between utility and price, influencing various attributes like delivery charges, size availability, photo displays, charity, and warranty. Price emerges as the most impactful attribute, followed by delivery charges, photo displays, and others, as indicated by their importance scores. The correlation analysis demonstrates a strong alignment between estimated and observed preferences, emphasizing the diverse influence of attributes on buying decisions.Furthermore, the study’s simulation profiles predict that the first stimulus card would be the most preferred among the participants, validating the consistency of the models used. This suggests that consumers prioritize specific attributes when making choices, with utility values guiding their preferences. Overall, this research provides valuable insights into the dynamics of consumer decision-making and emphasizes the importance of understanding attribute preferences for effective marketing strategies.
An Exercise to Introduce Artificial Intelligence to the Marketing Classroom using Tone Analysis
DINGUS, R. & BLACK, H. G.(2021) state that the proposed activity not only proves effective as a standalone exercise but also serves as a catalyst for fostering immense interest and curiosity among students. It not only provides a valuable learning experience but also establishes a robust platform for engaging discussions on modern technology. Recognizing that many students are already familiar with tools like Grammarly, this assignment encourages them to critically evaluate the underlying AI mechanisms, prompting a deeper understanding of their use in everyday communication. the appetite for knowledge it ignites among students, whether delving into AI, exploring machine learning, or other related topics. This exercise, offering limitless possibilities, aligns with the call from Ferrell and Ferrell (2020) for marketing educators to seamlessly integrate technology into the classroom. It not only enhances the educational experience but also encourages students to actively explore the dynamic intersection of technology and marketing, fostering a culture of continuous learning and inquiry.
A Meta-Meta Analysis of Effect Sizes in Marketing Research
EISEND, M. (2015) state that this study assesses the progress and value of marketing knowledge, finding a medium-sized effect size in explaining real-world phenomena. Despite limitations in explanatory power, marketing research contributes significantly. Variations in knowledge across subject areas are attributed to complexity, maturity, and research-based differences. Marketing knowledge has increased over time, but at a decreasing rate, reaching maturity. Specialization and fragmentation characterize the current era, impacting different subject areas differently. Implications include the need for targeted research in areas with lower explanatory power and strategic considerations for scholars’ career development based on effect sizes in specific subject areas.
A state-level Analysis of e-NAM
VENKATESH, P. et al.(2021) state that the analysis of e-NAM performance reveals challenges, with only 15% of APMC markets linked and farmers’ participation at around 13%. Cereals dominate trade, but concentration is observed, indicating limited state participation. Monetary incentives and farmers’ engagement strongly influence trade value. E-NAM prices are generally lower than Agmarketnet prices, and inter-market trade is negligible. To enhance trading, suggestions include expediting unified licenses, addressing infrastructure and manpower issues, implementing a uniform price quoting system, resolving technical glitches, and conducting large-scale awareness campaigns to increase farmer participation. Overall, strategic improvements are needed for both intra-market and inter-market trading in e-NAM.
An Extendwd Paradigm for Measurement Analysis of Marketing Constructs Applicable to Panal Data
BAUMGARTNER, H. & STEENKAMP, J.-B. E. M.(2006) state that the article highlights three key issues in marketing measurement. Firstly, it emphasizes the importance of distinguishing between trait and state aspects of a construct, offering a model to investigate both stable and transient components. Secondly, it introduces a comprehensive scale development paradigm that considers various sources of measurement error, providing a detailed diagnosis of item and scale quality. Thirdly, the methodology incorporates item and scale means, addressing measurement invariance and enabling justified cross-sectional and longitudinal comparisons. The proposed approach demands more effortful data collection but enhances construct validation and diagnostic information. The empirical illustration reveals systematic measurement error concerns, challenges conventional reliability estimates, and underscores the impact on substantive conclusions. The authors suggest potential opportunities for collecting longitudinal data, emphasizing consumer panels and student surveys. Further research is recommended to explore model parameterization and extend the methodology to include antecedents, consequences, and participant heterogeneity. The article calls for a deeper understanding of sources of error to enhance marketing measurement and advance the field as a science.
Solving Marketing Problems with Conjoint Analysis
VRIENS, M. (1994) state that methodological developments have been well-reviewed, understanding why conjoint analysis should be used for specific marketing problems is crucial for practitioners. The literature review reveals that conjoint analysis is valuable for addressing various marketing problems, including market segmentation, product development, and pricing. It is applicable to a wide range of products and services, from fast-moving consumer goods to consumer durables and industrial goods. However, there are limitations, such as its inability to capture the complexity of certain markets, especially those driven by image characteristics like cigarettes and jeans. Additionally, constraints like low research budgets or limited time may make conjoint analysis challenging. Despite these limitations, the potential of conjoint analysis is evident, and its adoption by marketing managers and researchers can be facilitated by recognizing its applicability to specific contexts and problems.
Conclusion:
The collection of studies provides a comprehensive overview of diverse topics within marketing research. Pieters et al. (2022) delve into latent moderation analysis methods, recommending factor scores and latent product methods for reliability, while introducing the two-step factor scores method. Bansal et al. (2023) conduct a bibliometric analysis of digital marketing publications, revealing a surge since 2002 and highlighting future research directions. A study on customer satisfaction towards digital marketing (S., J. et al., 2023) emphasizes the growing preference for digital channels, foreseeing digital marketing surpassing traditional methods. Jones et al. (2018) propose using prototyping to address terminology issues in marketing constructs, aiming for clarity. Kaur and Kumar’s (2021) social commerce marketing experimentation uncovers insights into consumer decision-making dynamics. Dingus and Black (2021) introduce AI to the marketing classroom, fostering student interest and critical evaluation. Eisend (2015) conducts a meta-meta analysis, noting marketing knowledge’s progress and variations across subject areas. Venkatesh et al.’s (2021) state-level analysis of e-NAM reveals challenges and suggests strategic improvements. Baumgartner and Steenkamp (2006) present a paradigm for marketing measurement, emphasizing trait and state aspects. Lastly, Vriens (1994) reviews methodological developments in conjoint analysis, highlighting its value for specific marketing problems despite limitations.
Reference:
BANSAL, P. et al (2023) A Bibliometric Analysis of Research on Digital Marketing. IUP Journal of Management Research, [s. l.], v. 22, n. 4, p. 38–53, 2023. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=5d075c2f-5de9-3b30-b24b-534415b18dda. Acesso em: 22 fev. 2024.
BAUMGARTNER, H.& STEENKAMP, J.-B. E. M.(2006) An Extended Paradigm for Measurement Analysis of Marketing Constructs Applicable to Panel Data. Journal of Marketing Research (JMR), [s. l.], v. 43, n. 3, p. 431–442, 2006. DOI 10.1509/jmkr.43.3.431. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=6631f966-5a18-3ae3-bb8e-8229ca371001. Acesso em: 22 fev. 2024.
DINGUS, R.& BLACK, H. G.(2021) Choose Your Words Carefully: An Exercise to Introduce Artificial Intelligence to the Marketing Classroom Using Tone Analysis. Marketing Education Review, [s. l.], v. 31, n. 2, p. 64–69, 2021. DOI 10.1080/10528008.2020.1843361. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=d883d7f5-b1b7-3643-8e78-a2a628e5d336. Acesso em: 22 fev. 2024.
EISEND, M.(2015) Have We Progressed Marketing Knowledge? A Meta-Meta-Analysis of Effect Sizes in Marketing Research. Journal of Marketing, [s. l.], v. 79, n. 3, p. 23–40, 2015. DOI 10.1509/jm.14.0288. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=0f8ddd21-2b57-3bd2-a722-f06daf5b5569. Acesso em: 22 fev. 2024.
JONES, T. et al.(2018) A prototyping analysis of relationship marketing constructs: what constructs to use when. Journal of Marketing Management, [s. l.], v. 34, n. 9/10, p. 865–901, 2018. DOI 10.1080/0267257X.2018.1520281. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=6a8562f1-01bd-3a13-a7f9-946479d556c5. Acesso em: 22 fev. 2024.
KAUR, K.& KUMAR, S.(2021) Social Commerce Marketing Experimentation Through Conjoint Analysis: Online Consumer Preferences. Journal of Business & Management, [s. l.], v. 27, n. 2, p. 109–132, 2021. DOI 10.6347/JBM.202109_27(2).0004. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=98dde7f2-e7d9-3ba9-b9ac-deae4511be02. Acesso em: 22 fev. 2024.
PIETERS, C. et al (2022) Six Methods for Latent Moderation Analysis in Marketing Research: A Comparison and Guidelines. Journal of Marketing Research (JMR), [s. l.], v. 59, n. 5, p. 941–962, 2022. DOI 10.1177/00222437221077266. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=cc1f7dc1-e2ff-3dc4-b417-77e5922f437d. Acesso em: 21 fev. 2024.
S., J.; et al (2023) A Study on Analysis of Customer Satisfaction towards Digital Marketing in Present Context. ANWESH: International Journal of Management & Information Technology, [s. l.], v. 8, n. 1, p. 1–5, 2023. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=ea89c1d4-df53-3652-9f1a-07d4345a9f1e. Acesso em: 22 fev. 2024.
VENKATESH, P. et al.(2021) The changing structure of agricultural marketing in India: a state-level analysis of e-NAM. Agricultural Economics Research Review, [s. l.], v. 34, p. 97–109, 2021. DOI 10.5958/0974-0279.2021.00018.5. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=e8cd3403-1e4b-3c2f-897b-8d638f707c4a. Acesso em: 22 fev. 2024.
VRIENS, M.(1994) Solving Marketing Problems With Conjoint Analysis. Journal of Marketing Management, [s. l.], v. 10, n. 1–3, p. 37–55, 1994. DOI 10.1080/0267257X.1994.9964259. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=debb65f1-7735-3040-8b97-83c208a7102f. Acesso em: 22 fev. 2024.