Search results

The idea that a significant portion of what consumers do, feel, and think is driven by automatic (or “implicit”) cognitive processes has sparked a wave of interest in the development of assessment tools that (attempt to) capture cognitive processes under automaticity conditions (also known as “implicit measures”). However, as more and more implicit measures are developed, it is becoming increasingly difficult for consumer scientists and marketing professionals to select the most appropriate tool for a specific research question. We therefore present a systematic overview of the criteria that can be used to evaluate and compare different implicit measures, including their structural characteristics, the extent to which (and the way in which) they qualify as “implicit,” as well as more practical considerations such as ease of implementation and the user experience of the respondents. As an example, we apply these criteria to four implicit measures that are (or have the potential to become) popular in marketing research (i.e., the implicit association test, the evaluative priming task, the affect misattribution procedure, and the propositional evaluation paradigm).

The aim of this chapter is to explore to what extent artificial intelligence (AI) could be introduced in new product development (NPD) process to support the decision making from the development to the launch of a new idea. Building on qualitative data from multiple cases from alcohol sector, the study reveals that AI could be introduced to reduce the risk of unsuccessful development and launch of a new product, supporting all the phases by systematically integrating feedbacks from the market. Specifically, AI can be used to create new recipes/flavours for alcohol drinks. Results also show that integrating AI from the idea scoping to the final product launch is feasible with the support of external stakeholders. The chapter concludes with some recommendations for theory and practice.

Forecasts from dynamic factor models potentially benefit from refining the data set by eliminating uninformative series. This paper proposes to use prediction weights as provided by the factor model itself for this purpose. Monte Carlo simulations and an empirical application to short-term forecasts of euro area, German, and French GDP growth from unbalanced monthly data suggest that both prediction weights and least angle regressions result in improved nowcasts. Overall, prediction weights provide yet more robust results.

Intrapreneurship (IP) and Strategic Human Resource Management (SHRM) are a paradigm in which the current global scenario of increased homeoffice and professional globalization can have the capacity to stimulate professionals’ autonomy and new business orientations able to re-invent new strategies, services, technologies and even leadership development. This study, of an exploratory nature, aims to analyze the synchronicities between IP and SHRM, raised by relational dynamics translated into leadership, organizational culture and individual practices having as a facilitating factor technology as an agent of change for continuous improvement (based on the Kaizen philosophy). It is supported by a qualitative analysis through a case study of a leading Portuguese group, Grupo Salvador Caetano, which has been in existence for 75 years.

The results demonstrate that dynamic relations are the synchronicities of IP and SHRM as long as stimulated and transmitted to collaborators, and that technology, facilitated these processes. The flexibility of SHRM, the sequence of delegation and implementation of relational dynamics must be the key for the synchronicities of SHRM and IP to be two phenomena that go side by side and contribute to more effective performance and evolution among collaborators, as they support each other in creating firms’ value for customers. Some contributions to theory and practice, raised through a logic of “in-house entrepreneurship,” are also presented at the end of the study.