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Research on market information use in product innovation suggests that firms utilize two key strategic decision-making processes: incremental and comprehensive. Drawing from organizational information processing theory, literature implies that these processes operate differently. However, this assumption remains untested. Moreover, the degree to which a comprehensive process affects the innovation strategy outcomes depends on market information time sensitivity (MITS) and analyzability. To-date, no study has tested these assertions, either. Finally, it is suggested that meaningful market strategy is a key driver of new product success and it is important to understand how decision-making processes influence it under differing time sensitivity and analyzability.

Based on survey data from 250 Chinese firms, authors use structural equation modeling to test the hypotheses.

The results generally support authors’ contentions. More specifically, marketing strategy outcomes are influenced by marketing strategy incrementality (MSI) and marketing strategy comprehensiveness (MSC) differently. Further, time sensitivity moderates the effect of both MSI and MSC on outcomes, except for the effect of MSI on decision quality. Finally, analyzability moderates the relationships between decision making processes and certain strategy outcomes such as between MSI and meaningfulness.

Drawing from information processing theory, authors argue that incremental and comprehensive marketing strategy decision making for new product operate differentially under the same conditions. Further, the effects of these decision processes on outcomes depend on time sensitivity and analyzability of market information. Finally, auhtors argue that meaningful market strategy is a driver of success. The authors find support for most of our hypotheses and provide directions for future research.

Big data marketing analytics (BDMA) has been discovered to be a key contributing factor to developing necessary marketing capabilities. This research aims to investigate the impact of the technology and information quality of BDMA on the critical marketing capabilities by differentiating between firms with low and high perceived market performance.

The responses were collected from marketing professionals familiar with BDMA in North America (N = 236). The analysis was done with partial least squares-structural equation modelling (PLS-SEM).

The results indicated positive and significant relationships between the information and technology quality as exogenous constructs and the endogenous constructs of the marketing capabilities of marketing planning, implementation and customer relationship management (CRM) with mainly moderate effect sizes. Differences in the path coefficients in the structural model were detected between firms with low and high perceived market performance.

This research indicates the critical role of technology and information quality in developing marketing capabilities. The study discovered heterogeneity in the sample population when using the low and high perceived market performance as the source of potential heterogeneity, the presence of which would likely cause a threat to the validity of the results in case heterogeneity is not considered. Thus, this research builds on previous research by considering this issue.

Data-driven marketing is replacing conventional marketing strategies. The modern marketing strategy is based on insights derived from customer behavior information gathered from their facial expressions and neuro-signals. This study explores the potential for face recognition and neuro-marketing in modern-day marketing.

The study conducts an in-depth examination of the extant literature on neuro-marketing and facial recognition marketing. The articles for review are downloaded from the Scopus database, and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is then used to screen and choose the relevant papers. The systematic literature review method is applied to conduct the study.

An extensive review of the literature reveals that the domains of neuro-marketing and face recognition marketing remain understudied. The authors’ review of selected papers delivers five neuro-marketing and facial recognition marketing themes that are essential to modern marketing concepts.

Neuro-marketing and facial recognition marketing are artificial intelligence (AI)-enabled marketing techniques that assist in gaining cognitive insights into human behavior. The findings would be of use to managers in designing marketing strategies to enhance their marketing approach and boost conversion rates.

The uniqueness of this study lies in that it provides an updated review on neuro-marketing and face recognition marketing.

The rapid development of live-streaming commerce has increased companies’ marketing effectiveness. While previous studies have explored the effects of its technical features on consumers, the effects of marketing-related factors remain unknown. This study aims to investigate the effects of the marketing elements of live-streaming commerce on consumers’ purchase intentions.

The research model is derived from the Yale model and the benefit–risk framework. To test the study hypotheses, data were collected through a questionnaire survey of 392 live-streaming shoppers and analyzed using SmartPLS.

The empirical results indicate that broadcaster competence and online crowding increase consumers’ perception of price attractiveness while reducing their perceived uncertainty. Information diagnosticity also reduces consumers’ perceived uncertainty. Furthermore, purchase intention is positively and negatively affected by perceived price attractiveness and perceived uncertainty, respectively. Finally, product scarcity moderates the relationships between broadcaster competence, online crowding, information diagnosticity, perceived price attractiveness and perceived uncertainty.

The study identifies the different marketing elements in live-streaming commerce and their effects on consumers’ value evaluations and purchase intentions. The findings provide comprehensive insights into the antecedents of live-streaming shopping and offer new perceptions and recommendations for practitioners.

This study aims to investigate organizational learning, represented by the absorptive capacity, as a condition for the firm to learn about marketing data and make more informed decisions. The authors also aimed to understand how the behavior of micro, small and medium enterprises (MSME) businesses differ in this scenario through a multilevel perspective.

Placing absorptive capacity as a mediator of the relationship between business analytics and rational marketing decisions, the authors analyzed data from 224 Brazilian retail companies using structural equation modeling estimated with partial least squares. To test the cross-level moderation effect, the authors also performed a multilevel analysis in RStudio.

The authors found a partial mediation of the absorptive capacity in the relation between business analytics and rational marketing decisions. The authors also discovered that, in the MSMEs firms’ group, even if smaller companies find it more difficult to use data, those that do may reap more benefits than larger ones. This is due to the influence of size in how firms handle information.

The sample size, despite having shown to be consistent and valid, is considered small for a multilevel study. This suggests that our multilevel results should be viewed as suggestive, rather than conclusive, and subjected to further validation.

Rather than solely positioning business analytics as a tool for decision support, the authors’ analysis highlights the importance for firms to develop the absorptive capacity to enable ongoing acquisition, exploration and management of knowledge.

MSMEs are of economic and social importance to most countries, especially developing ones. This research aimed to improve understanding of how this group of firms could transform knowledge into better decisions. The authors also highlight micro and small firms’ difficulties with the use of marketing data so that they can have more effective practices.

The research contributes to the understanding of organizational mechanisms to absorb and learn from the vast amount of current marketing information. Recognizing the relevance of MSMEs, a preliminary multilevel analysis was also conducted to comprehend differences within this group.

This study identifies factors affecting brand building for academic libraries affiliated with the Ministry of Science, Research and Technology (MSRT) in Iran.

This research applied the grounded theory method based on the three open, axial and selective coding steps (Strauss and Corbin, 1998). The research tool was interviews conducted with 20 experts in librarianship, marketing and branding.

Library building architecture, library information resources and services, librarians' branding, marketing activities and library management are the causal conditions affecting brand building. The national economic situation, the digital publishing situation in the country and different characteristics of the new library community are the intervening conditions affecting brand building. The role of other libraries in society in the scientific education of the new generation provides contextual conditions for brand building. The higher education system and the library parent organization play a part in the operative actions/interactions for brand building. The consequences of brand building are brand image development, brand excellence and brand behavioral loyalty for libraries. Library brand identity is also a core category in brand building.

Facing steep challenges by emergent services, academic libraries are ill-prepared to meet the needs of the new information society solely with traditional services and functions. Academic libraries are required to rebrand themselves to be more successful at delivering a strong performance within a changing information environment by enhancing their brand image and establishing a more effective relationship with users.

The purpose of this study is to review and analyze the status of word-of-mouth (WOM) research in the business-to-business (B2B) context and discuss and identify new possible future directions.

A systematic review was conducted and 36 articles on B2B WOM were collected to evaluate the current state of the literature and clarify possible future research directions.

This thematic analysis categorize these articles into three themes: WOM generation, WOM usage and reference marketing. Under each theme, the authors reveal research findings unique to B2B research and different from business-to-consumer (B2C) WOM research. This study identifies several research questions that should be addressed by future research.

Both academic researchers and business practitioners recognize that WOM plays an essential role in B2B marketing. However, no review paper focuses on WOM in the B2B context. Findings in the B2C WOM literature suggest that WOM substantially influences firms’ performance, but that managers cannot simply attempt to extrapolate B2C findings to the B2B arena. By synthesizing and assessing prior research on WOM in the B2B context, this study contributes to a better understanding of the B2B WOM phenomenon and facilitates future research on this topic.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

There is a contradictive debate on factors influencing mobile phone usage awareness among scholars. This study aims to examine factors influencing mobile phone usage awareness for accessing agricultural marketing information.

A descriptive cross-sectional research design was used with 400 smallholder grape farmers. The use of structured questionnaires, focus group discussions and key informant interviews helped to collect primary data. Data analysis was subjected to descriptive, ordinal logistic regression and thematic approaches.

This study found that farmers were mostly aware of voice calls helping to access buyers and price information. Education, age and sex were the critical factors influencing mobile phone usage awareness among grape smallholder farmers.

This study contributes to scientific knowledge by providing an understanding of the perceived factors on mobile phone usage awareness within the grape subsector to inform policymakers.

This study aims to analyse and understand the current state of research in the field of digital marketing in “library and information science”.

This study used a “bibliometric research design.” A lexical title search was used to obtain the required data set for executing this study, and a comprehensive “indexing and abstracting” database, Web of Science, was used as a data harvesting source. Louvain’s clustering algorithm was used for network metrics.

The findings revealed that research productivity and impact have grown considerably over time, indicating significant attention towards digital marketing research in library and information science (LIS). Moreover, the results showed that the overall author collaboration patterns were weak, hence creating room for development in the author’s collaboration patterns.

The current study could be very beneficial in providing a comprehensive and up-to-date overview of the “digital marketing” research field scholarly output in LIS, which can be used by researchers, practitioners and policymakers to guide their work and make informed decisions.

The originality of this bibliometric study lies in its comprehensive and up-to-date analysis of the current state of research in the field of “digital marketing” in LIS. This study provides a unique and in-depth understanding of the key authors, venues and papers in the field, as well as the trends and patterns in the research.