Social Media and New Product Introduction: Role of Proactiveness, Risk-Taking and Market Dynamism

2019 ◽  
Vol 2019 (1) ◽  
pp. 17714
Author(s):  
Avimanyu Datta ◽  
Smita Srivastava ◽  
Stoney Brooks
Author(s):  
Irina Wedel ◽  
Michael Palk ◽  
Stefan Voß

AbstractSocial media enable companies to assess consumers’ opinions, complaints and needs. The systematic and data-driven analysis of social media to generate business value is summarized under the term Social Media Analytics which includes statistical, network-based and language-based approaches. We focus on textual data and investigate which conversation topics arise during the time of a new product introduction on Twitter and how the overall sentiment is during and after the event. The analysis via Natural Language Processing tools is conducted in two languages and four different countries, such that cultural differences in the tonality and customer needs can be identified for the product. Different methods of sentiment analysis and topic modeling are compared to identify the usability in social media and in the respective languages English and German. Furthermore, we illustrate the importance of preprocessing steps when applying these methods and identify relevant product insights.


Author(s):  
Saurav Snehvrat ◽  
Swarup Dutta

Purpose The purpose of this paper is to explore the multi-faceted role of metaroutines in dealing with nested ambidexterity challenges experienced during new product introductions (NPIs) at Tata Motors, an Indian automotive giant. Design/methodology/approach This study utilizes an in-depth multi-level case study highlighting the ambidexterity dynamics across strategic, business unit and functional levels. Findings The authors visualize the NPI system found in the firm, including the interactions between structure, process and governance, as a metaroutine. Based on this visualization, the authors argue that certain ostensive (like voice of customer, commonality) and performative (role of leadership and creative recombination) aspects of the metaroutine aid exploratory and exploitative learning across levels at Tata Motors. Further, the authors argue that the role of embedded NPI metaroutine aspects in promoting multi-level ambidexterity offers a distinct form when compared with other academically established forms of structural, contextual and temporal ambidexterity. Practical implications This study focuses on the aspects of the NPI metaroutine that enable ambidexterity within the studied firm. The authors argue that apart from the structural, temporal and contextual mechanisms, managers also need to focus on the nuances of NPI metaroutines and their potential to promote ambidexterity across levels. Originality/value The authors visualize the interactions between the process, structure and governance mechanisms, related to NPI, as a metaroutine. The authors argue that metaroutine enabled approaches to ambidexterity offer a distinct form when compared with other academically established forms of structural, contextual and temporal ambidexterity. Also, metaroutine enabled ambidexterity explains a possible way through which multi-level ambidexterity can be promoted and managed within organizations.


2021 ◽  
pp. 187-198
Author(s):  
Shima Zahmatkesh ◽  
Alessio Bernardo ◽  
Emanuele Falzone ◽  
Edgardo Di Nicola Carena ◽  
Emanuele Della Valle

Industries that sell products with short-term or seasonal life cycles must regularly introduce new products. Forecasting the demand for New Product Introduction (NPI) can be challenging due to the fluctuations of many factors such as trend, seasonality, or other external and unpredictable phenomena (e.g., COVID-19 pandemic). Traditionally, NPI is an expertcentric process. This paper presents a study on automating the forecast of NPI demands using statistical Machine Learning (namely, Gradient Boosting and XGBoost). We show how to overcome shortcomings of the traditional data preparation that underpins the manual process. Moreover, we illustrate the role of cross-validation techniques for the hyper-parameter tuning and the validation of the models. Finally, we provide empirical evidence that statistical Machine Learning can forecast NPI demand better than experts.


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