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This paper aims to develop a novel and generalizable machine-learning based method of measuring established marketing constructs through passive analysis of consumer-generated textual data. The authors term this method scale-directed text analysis.

The method first develops a dictionary of words related to specific dimensions of a construct that is used to assess textual data from any source for a specific meaning. The method explicitly recognizes both specific words and the strength of their underlying sentiment.

Results calculated using this new approach are statistically equivalent to responses to traditional marketing scale items. These results demonstrate the validity of the authors’ methodology and show its potential to complement traditional survey approaches to assessing marketing constructs.

The method we outline relies on machine learning and thus requires either large volumes of text or a large number of cases. Results are reliable only at the aggregate level.

The method detail provides a means of less intrusive data collection such as through scraped social media postings. Alternatively, it also provides a means of analyzing data collected through more naturalistic methods such as open-response forms or even spoken language, both likely to increase response rates.

Scale-directed text analysis goes beyond traditional methods of conducting simple sentiment analysis and word frequency or percentage counts. It combines the richness of traditional textual and sentiment analysis with the theoretical structure and analytical rigor provided by traditional marketing scales, all in an automatic process.

This paper aims to propose a framework for psychological reactance–triggered adverse effects of variety reductions in grocery product categories on shoppers’ patronage intentions.

The paper tests this framework in two field studies with European shoppers.

Participants perceived mild (let alone aggressive or conspicuous) variety reductions as a threat to their prior freedom of choice (i.e. a precondition for the occurrence of domain-specific reactance). Through lower satisfaction with the reduced variety and anger towards the grocer, this threat, in turn, fostered adverse patronage intentions. Such effects depended on product category nature (utilitarian vs hedonic) and shoppers’ intrinsic need for variety, attitude towards private-label items and general proclivity towards experiencing reactance.

By applying psychological reactance theory to a variety reduction context, this paper offers new implications for assortment reduction research. Certain limitations call for future reactance theory–framed inquiry.

The findings caution against traditional grocers’ drastic variety reduction policy and highlight conditions enabling assortment rationalisation without severely affecting freedom of choice.

Drawing on notions such as “the tyranny of choice”, critics have urged traditional grocers to drastically reduce variety. However, this paper shows that shoppers perceive variety reductions as threats to their prior freedom, which traditional grocers themselves educated them to expect and enjoy.

This paper aims to consider decision speed’s role in the largely neglected decision area of product elimination.

Drawing on an inter-disciplinary theoretical background (e.g. organisational, decision speed and product elimination theories), the authors develop and test a framework for decision speed’s effects on the market and financial outcomes of a stratified random sample of 175 consumer product eliminations.

In contrast to decision speed research that hypothesised (and often failed to confirm) linearity, results show inverted ∪-shaped decision speed-to-decision outcomes relationships, with curvatures moderated by product importance, environmental complexity and turbulence.

Findings are suggestive of several implications for the above theories (e.g. contribution to the dialogue about performance-enhancing value of rational vs incremental decision-making; evidence that excessive decision speed may become too much of a good thing). Certain design limitations (e.g. sampling consumer goods’ manufacturers only) point at avenues for future inquiry into the product elimination decision speed-to-outcomes link.

Managerially, the findings suggest that product eliminations’ optimal market and financial outcomes depend on a mix of speed and search in decision-making and that this mix requires adjustments to different levels of product importance, interdependencies with other decision areas of the firm and environmental turbulence.

The paper makes a twofold contribution. It enriches decision speed research, by empirically addressing speed’s outcomes in relation to a decision area that is not necessarily strategic and represents the first explicit empirical investigation into outcomes of decision speed in product line pruning decision-making.

This study aims to examine consumers’ perception of artificial intelligence (AI) and AI marketing communication.

Twenty in-depth interviews were conducted to collect data and phenomenological reduction was used to analyze data.

Findings suggest that consumers’ interpretation of AI is multidimensional and relational with a focus on functionality and emotion, as well as comparison and contrast between AI and human beings; consumers’ perception of voice-assisted AI centers on the aspects of function, communication, adaptation, relationship and privacy; consumers consider AI marketing communication to be unavoidable and generally acceptable; and consumers believe that AI marketing communication to be limited in its effect on influencing their evaluation of products/brands or shaping their consumptive behaviors.

According to the authors' knowledge, this study is the first research project to gauge consumers' perception on AI and AI marketing communication.