Strategic innovation and entrepreneurial ownership: an analysis using GEM data and fuzzy simulation

Scenarios for optimizing timing for new product exits: a trifecta of models' predictive performances

Purpose

The purpose of the study is applying and comparing models that predict optimal time for new product exit based on its demand pattern and survivability. This is to decide whether or not to continue investing in new product development (NPD).

Design/methodology/approach

The study investigates the optimal time for new product exit within the hi-tech sector by applying three models: the dynamic learning demand model (DLDM), the generalized Bass model (GBM) and the hazard model (HM). Further, for inter- and intra-model comparison, the authors conducted a simulation, considering Weiner and exponential price functions to enhance generalizability.

Findings

While higher price volatility signifies an unstable technology, greater investment into research and development (R&D) and marketing results in higher product adoption rates. Imitators have a more prominent role than innovators in determining the longevity of hi-tech products.

Originality/value

The study conducts a comparison of three different models considering time-varying parameters. There are four scenarios, considering variations in advertising intensity and content, word-of-mouth (WOM) effect, price volatility effect and sunk cost effect.

Determinants of alliance productivity and performance: evidence from the automobile industry

Purpose

This study aims to identify critical factors and examine their impact on alliance performance from an organizational learning point of view.

Design/methodology/approach

A modified total interpretive structural modeling (M-TISM) methodology was used in this study. The different paths/links in the developed M-TISM model were further validated by using the Mahindra-Ford alliance case study.

Findings

In this study, a total of seven critical factors were identified using an extensive literature review, and a hierarchical model was developed. Results show that prior alliance experience, inter-partner learning, knowledge transfer, absorptive capacity and knowledge internalization have a positive on the alliance productivity and performance. Furthermore, the findings indicate that prior alliance experience remains essential for alliance productivity and performance, while knowledge transfer and absorptive capacity can contribute to inter-partner learning and knowledge internalization in strategic alliances.

Research limitations/implications

This study can help managers and policymakers to understand the identified critical factors from an organizational learning perspective and understand their impact on the alliance performance in a competitive environment. The managers should know that previous alliance experience, learning from partner firms, building an absorptive capacity, etc., are necessary to achieve superior alliance productivity and performance. For academicians, the M-TISM methodology used in this study can provide a mechanism to perform exploratory research and build a hierarchical model in different management research fields.

Originality/value

The study fills research gaps by identifying key factors, developing a hierarchical model, and examining their impact on the performance of strategic alliances in the Indian automotive industry.