Search results

This article explores how employees can perceive and be impacted by the fakeness of their company slogans.

This conceptual study draws on the established literature on company slogans, employee audiences, and fake news to create a framework through which to understand fake company slogans.

Employees attend to two important dimensions of slogans: whether they accurately reflect a company’s (1) values and (2) value proposition. These dimensions combine to form a typology of four ways in which employees can perceive their company’s slogans: namely, authentic, narcissistic, foreign, or corrupt.

This paper outlines how the typology provides a theoretical basis for more refined empirical research on how company slogans influence a key stakeholder: their employees. Future research could test the arguments about how certain characteristics of slogans are more or less likely to cause employees to conclude that slogans are fake news. Those conclusions will, in turn, have implications for the morale and engagement of employees. The ideas herein can also enable a more comprehensive assessment of the impact of slogans.

Employees can view three types of slogans as fake news (narcissistic, foreign, and corrupt slogans). This paper identifies the implications of each type and explains how companies can go about developing authentic slogans.

This paper explores the impact of slogan fakeness on employees: an important audience that has been neglected by studies to date. Thus, the insights and implications specific to this internal stakeholder are novel.

The purpose of this paper is to introduce, apply and compare how artificial intelligence (AI), and specifically the IBM Watson system, can be used for content analysis in marketing research relative to manual and computer-aided (non-AI) approaches to content analysis.

To illustrate the use of AI-enabled content analysis, this paper examines the text of leadership speeches, content related to organizational brand. The process and results of using AI are compared to manual and computer-aided approaches by using three performance factors for content analysis: reliability, validity and efficiency.

Relative to manual and computer-aided approaches, AI-enabled content analysis provides clear advantages with high reliability, high validity and moderate efficiency.

This paper offers three contributions. First, it highlights the continued importance of the content analysis research method, particularly with the explosive growth of natural language-based user-generated content. Second, it provides a road map of how to use AI-enabled content analysis. Third, it applies and compares AI-enabled content analysis to manual and computer-aided, using leadership speeches.

For each of the three approaches, nine steps are outlined and described to allow for replicability of this study. The advantages and disadvantages of using AI for content analysis are discussed. Together these are intended to motivate and guide researchers to apply and develop AI-enabled content analysis for research in marketing and other disciplines.

To the best of the authors’ knowledge, this paper is among the first to introduce, apply and compare how AI can be used for content analysis.

Configurational theory can significantly enhance understanding of organisational strategy and diversity. Despite this, there have been limited efforts to examine the value and utility of configurational research as a method for realising manufacturing strategies. This paper introduces a strategic management framework based on configurational theory and an evolutionary classification method (cladistics). Focusing on agile manufacturing concepts, the framework provides a system for collecting and organising information on manufacturing routines and capabilities. This facilitates the processes of strategic analysis, strategic choice and strategic information.

This theoretical paper presents, extends and integrates a number of systems and evolutionary concepts, to demonstrate their relevance to manufacturing strategy formulation. Specifically it concentrates on fitness landscape theory as an approach for visually mapping the strategic options a manufacturing firm could pursue. It examines how this theory relates to manufacturing competitiveness and strategy and proposes a definition and model of manufacturing fitness. In accordance with fitness landscape theory, a complex systems perspective is adopted to view manufacturing firms. It is argued that manufacturing firms are a specific type of complex system – a complex adaptive system – and that by developing and applying fitness landscape theory it is possible to create models to better understand and visualise how to search and select various combinations of capabilities.

Presents an evolutionary management technique (cladistics) which could enable organizations to formally and systematically understand the emergence of new manufacturing forms within their business environment. This fundamental, but important, insight could result in cladograms being used as a tool within a change framework, for achieving successful organizational design and change. Thus, regardless of the industrial sector, managers could use cladograms as an evolutionary analysis technique for determining “where they have been and where they are now”. This evolutionary analysis could be used to formulate coherent and appropriate action for managers who are responsible for organizational design and development.

This paper aims to report on the construction of a scale to measure a firm's stance towards creative consumers; that is, customers who adapt, modify or transform a proprietary offering.

A measurement instrument, called the 3As, is developed to assess the extent to which an organization is aware of its creative customers, its attitude towards its creative customers, and finally the action it takes in response to its creative customers. A total of 178 Executive MBA students were used to fine‐tune a set of items using exploratory factor analysis (EFA).

An empirical test of reliability and validity resulted in three clearly defined factors or dimensions, which correspond to the three constructs of awareness, attitude and action. The relationship between the scales' prediction of stances and a manager's self typing of the organization is assessed, and the relationship between firm stance, environmental turbulence, and performance explored.

This paper provides the first scale for measuring a firm's stance toward creative consumers.

This chapter presents an exposition of the Generalized Fechner–Thurstone (GFT) direct utility function, the system of demand functions derived from it, other systems of demand functions from which it can be derived, and its purpose and the econometric circumstances that motivated its original development. Its use in econometrics is demonstrated by an application to household consumer survey data which explores the relationship between prices, on the one hand, and expected exogenous preference changers such as household size, schooling of heads of household, and other social factors, on the other.

With expenditures totaling $227 billion in 2007, prescription drug purchases are a growing portion of the total medical expenditure, and as this industry continues to grow, prescription drugs will continue to be a critical part of the larger health care industry. This chapter presents a survey on the economics of the US pharmaceutical industry, with a focus on the role of R&D and marketing, the determinants (and complications) of prescription drug pricing, and various aspects of consumer behavior specific to this industry, such as prescription drug regulation, the patient's interaction with the physician, and insurance coverage. This chapter also provides background in areas not often considered in the economics literature, such as the role of pharmacy benefit managers in prescription drug prices and the differentiation between alternative measures of prescription drug prices.

Provides a fresh and novel approach to an established problem; the classification of manufacturing systems. Reviews existing manufacturing classifications and biological taxonomy. Proposes a consistent vocabulary and preliminary guidelines for the successful development of other classifications (FMS types, levels of technology, etc.). Aims to aid the construction of competent classifications that will advance the understanding of manufacturing system modelling and design. Supports proposals by novel comparisons drawn from the “science of diversity”, systematics, and the 200 years of experience that biological taxonomy has to offer.

This chapter presents a Bayesian analysis of the endogenous treatment model with misclassified treatment participation. Our estimation procedure utilizes a combination of data augmentation, Gibbs sampling, and Metropolis–Hastings to obtain estimates of the misclassification probabilities and the treatment effect. Simulations demonstrate that the proposed Bayesian estimator accurately estimates the treatment effect in light of misclassification and endogeneity.