Situation-Based Shifts in Consumer Web Site Benefit Salience: The Joint Role of Affect and Cognition

This study addresses the process by which differences in web site benefit salience arise in consumers’ minds for different anticipated usage situations. We investigate two routes by which situation may determine consumer benefit salience and find support for both route structures. The results indicate that individuals’ relative benefit importance ratings shift between different anticipated usage situations, both directly, and indirectly, through consumers’ anticipated affective states. Furthermore, the number of benefits that is rated as important by consumers is found to also differ depending on their anticipated affective states, providing further insight into why consumer benefit salience may vary across situations.affective route;cognitive route;situatieafhankelijkheid;usage situation;web site benefit salience;HD9696.82

Consumer Preferences for Mass Customization

Increasingly, firms adopt mass customization, which allows consumers to customize products by self-selecting their most preferred composition of the product for a predefined set of modules. For example, PC vendors such as Dell allow customers to customize their PC by choosing the type of processor, memory size, monitor, etc. However, how such firms configure the mass customization process determines the utility a consumer may obtain or the complexity a consumer may face in the mass customization task. Mass customization configurations may differ in four important ways – we take the example of the personal computer industry. First, a firm may offer few or many product modules that can be mass customized (e.g., only allow consumers to customize memory and processor of a PC or allow consumers to customize any module of the PC) and few or many levels among which to choose per mass customizable module (e.g., for mass customization of the processor, only two or many more processing speeds are available). Second, a firm may offer the consumer a choice only between very similar module levels (e.g., a 17” or 18” screen) or between very different module levels (e.g., a 15” or 21” screen). Third, a firm may individually price the modules within a mass customization configuration (e.g., showing the price of the different processors the consumer may choose from) along with pricing the total product, or the firm may show only the total product price (e.g., the price of the different processors is not shown, but only the computer’s total price is shown). Fourth, the firm may show a default version (e.g., for the processor, the configuration contains a pre-selected processing speed, which may be a high-end or low-end processor), which consumers may then customize, or the firm may not show a default version and let consumers start from scratch in composing the product. The authors find that the choices that firms make in configuring the mass customization process affect the product utility consumers can achieve in mass customization. The reason is that the mass customization configuration affects how closely the consumer may approach his or her ideal product by mass customizing. Mass customization configurations also affect consumers’ perception of the complexity of mass customization as they affect how many cognitive steps a consumer needs to make in the decision process. Both product utility and complexity in the end determine the utility consumers derive from using a certain mass customization configuration, which in turn will determine main outcome variables for marketers, such as total product sales, satisfaction with the product and the firm, referral behavior and loyalty. The study offers good news for those who wish to provide many mass customization options to consumers, because we find that within the rather large range of modules and module levels we manipulated in this study, consumers did not perceive significant increases in complexity, while they were indeed able to achieve higher product utility. Second, our results imply that firms when increasing the number of module levels, should typically offer consumers more additional options in the most popular range of a module and less additional options at the extremes. Third, pricing should preferably be presented only at the total product level, rather than at the module and product level. We find that this approach reduces complexity and increases product utility. Fourth, firms should offer a default version that consumers can use as a starting point for mass customization, as doing so minimizes the complexity to consumers. The best default version to start out with is a base default version because this type of default version allows the consumer to most closely approach his or her ideal product. The reason is that consumers when presented with an advanced default may buy a product that is more advanced than they actually need. We also found that expert consumers are ideal targets for mass customization offerings. Expert consumers experience lower complexity in mass customization and complexity has a less negative influence on product utility obtained in the mass customization process, all compared to novice consumers. In general, reducing complexity in the mass customization configuration is a promising strategy for firms as it not only increases the utility of the entire process for consumers, but also allows them to compose products that more closely fit their ideal product

Situation Variation in Consumers’ Media Channel Consideration

In this article, the authors investigate consumers’ consideration of media channels during different usage situations. They develop a model that explains consumers’ media channel consideration as a function of the media channel’s perceived benefits. In addition, they hypothesize that the usage situation affects consumers’ media channel consideration and that situation-based benefit requirements moderate the effect of the benefits on their channel consideration. The authors test the hypothesized relationships using survey data from 341 consumers regarding their consideration of 12 different media channels used by manufacturers to communicate product information across three product-related usage situations. The results of the analyses support the proposed model structure and confirm the expected relationships among perceived media channel benefits, usage situations, media channel requirements, and consumers’ media channel consideration

Situation-Based Shifts in Consumer Web Site Benefit Salience: The Joint Role of Affect and Cognition

This study addresses the process by which differences in web site benefit salience arise in consumers’ minds for different anticipated usage situations. We investigate two routes by which situation may determine consumer benefit salience and find support for both route structures. The results indicate that individuals’ relative benefit importance ratings shift between different anticipated usage situations, both directly, and indirectly, through consumers’ anticipated affective states. Furthermore, the number of benefits that is rated as important by consumers is found to also differ depending on their anticipated affective states, providing further insight into why consumer benefit salience may vary across situations

A stochastic inventory policy with limited transportation capacity

In this paper we consider a stochastic single-item inventory problem. A retailer keeps a single product on stock to satisfy customers stochastic demand. The retailer is replenished periodically from a supplier with ample stock. For the delivery of the product, trucks with finite capacity are available and a fixed shipping cost is charged whenever a truck is dispatched regardless of its load. Furthermore, linear holding and backorder costs are considered at the end of a review period. A replenishment policy is proposed to determine order quantities taking into account transportation capacity and aiming at minimizing total average cost. Every period an order quantity is determined based on an order-up-to logic. If the order quantity is smaller than a given threshold then the shipment is delayed. On the other hand, if the order quantity is larger than a second threshold then the initial order size is enlarged and a full truckload is shipped. An order size between these two thresholds results in no adaption of the order quantity and the order is shipped as it is. We illustrate that this proposed policy is close to the optimal policy and much better than an order-up-to policy without adaptations. Moreover, we show how to compute the cost optimal policy parameters exactly and how to compute them by relying on approximations. In a detailed numerical study, we compare the results obtained by the heuristics with those given by the exact analysis. A very good cost performance of the proposed heuristics can be observed

Consumers' intention to use health recommendation systems to receive personalized nutrition advice

Background: Sophisticated recommendation systems are used more and more in the health sector to assist consumers in healthy decision making. In this study we investigate consumers' evaluation of hypothetical health recommendation systems that provide personalized nutrition advice. We examine consumers' intention to use such a health recommendation system as a function of options related to the underlying system (e.g. the type of company that generates the advice) as well as intermediaries (e.g. general practitioner) that might assist in using the system. We further explore if the effect of both the system and intermediaries on intention to use a health recommendation system are mediated by consumers' perceived effort, privacy risk, usefulness and enjoyment. Methods. 204 respondents from a consumer panel in the Netherlands participated. The data were collected by means of a questionnaire. Each respondent evaluated three hypothetical health recommendation systems on validated multi-scale measures of effort, privacy risk, usefulness, enjoyment and intention to use the system. To test the hypothesized relationships we used regression analyses. Results: We find evidence that the options related to the underlying system as well as the intermediaries involved influence consumers' intention to use such a health recommendation system and that these effects are mediated by perceptions of effort, privacy risk, usefulness and enjoyment. Also, we find that consumers value usefulness of a system more and enjoyment less when a general practitioner advices them to use a health recommendation system than if they use it out of their own curiosity. Conclusions: We developed and tested a model of consumers' intention to use a health recommendation system. We found that intermediaries play an important role in how consumers evaluate such a system over and above options of the underlying system that is used to generate the recommendation. Also, health-related information services seem to rely on endorsement by the medical sector. This has considerable implications for the distribution as well as the communication channels of health recommendation systems which may be quite difficult to put into practice outside traditional health service channels

A New Method of Measuring Online Media Advertising Effectiveness: Prospective Meta-Analysis in Marketing

The authors introduce a new method, prospective meta-analysis in marketing (PMM), to estimate consumer response to online advertising on a large and adaptive scale. They illustrate their approach in a field study in the U.S., China and the Netherlands, covering equivalent ad content on social media, online video, display banner, and search engines. The authors tested a conceptual framework based on attention and engagement using a technological solution that allow them to observe participants browsing and clicking activity in depth from their own residences, offices, or places of choice to use the tested media platforms, e.g., Facebook, Weibo, Google, Baidu and others. The authors show how consumers respond differently to the same ad depending on how distant they are from purchase, and uncover which channels are most appropriate to which user at different stages of the funnel. They also show how engagement and attention strengthen consumer response to advertising. The authors show how PMM produces exploratory findings, confirmatory findings, and replications by systematically organizing the incremental exploration of complex phenomena with cycles of discovery and validation

iSAM2 : incremental smoothing and mapping using the Bayes tree

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Sage for personal use, not for redistribution. The definitive version was published in International Journal of Robotics Research 31 (2012): 216-235, doi:10.1177/0278364911430419.We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the square root information matrix of the simultaneous localization and mapping (SLAM) problem. In this paper, we highlight three insights provided by our new data structure. First, the Bayes tree provides a better understanding of the matrix factorization in terms of probability densities. Second, we show how the fairly abstract updates to a matrix factorization translate to a simple editing of the Bayes tree and its conditional densities. Third, we apply the Bayes tree to obtain a completely novel algorithm for sparse nonlinear incremental optimization, named iSAM2, which achieves improvements in efficiency through incremental variable re-ordering and fluid relinearization, eliminating the need for periodic batch steps. We analyze various properties of iSAM2 in detail, and show on a range of real and simulated datasets that our algorithm compares favorably with other recent mapping algorithms in both quality and efficiency.M. Kaess, H. Johannsson and J. Leonard were partially supported by ONR grants N00014-06-1-0043 and N00014-10-1-0936. F. Dellaert and R. Roberts were partially supported by NSF, award number 0713162, “RI: Inference in Large-Scale Graphical Models”. V. Ila has been partially supported by the Spanish MICINN under the Programa Nacional de Movilidad de Recursos Humanos de Investigación

Evolving Synaptic Plasticity with an Evolutionary Cellular Development Model

Since synaptic plasticity is regarded as a potential mechanism for memory formation and learning, there is growing interest in the study of its underlying mechanisms. Recently several evolutionary models of cellular development have been presented, but none have been shown to be able to evolve a range of biological synaptic plasticity regimes. In this paper we present a biologically plausible evolutionary cellular development model and test its ability to evolve different biological synaptic plasticity regimes. The core of the model is a genomic and proteomic regulation network which controls cells and their neurites in a 2D environment. The model has previously been shown to successfully evolve behaving organisms, enable gene related phenomena, and produce biological neural mechanisms such as temporal representations. Several experiments are described in which the model evolves different synaptic plasticity regimes using a direct fitness function. Other experiments examine the ability of the model to evolve simple plasticity regimes in a task -based fitness function environment. These results suggest that such evolutionary cellular development models have the potential to be used as a research tool for investigating the evolutionary aspects of synaptic plasticity and at the same time can serve as the basis for novel artificial computational systems

Statistical disclosure control in tabular data

Data disseminated by National Statistical Agencies (NSAs) can be classified as either microdata or tabular data. Tabular data is obtained from microdata by crossing one or more categorical variables. Although cell tables provide aggregated information, they also need to be protected. This chapter is a short introduction to tabular data protection. It contains three main sections. The first one shows the different types of tables that can be obtained, and how they are modeled. The second describes the practical rules for detection of sensitive cells that are used by NSAs. Finally, an overview of protection methods is provided, with a particular focus on two of them: “cell suppression problem” and “controlled tabular adjustment”.Postprint (published version