Search results

This paper aims to investigate the impact of assortment size and attribute quantity on the depth and content of consumer information searches.

For a computer-aided experiment using an information display board, participants (n = 393) were placed in a simulated shopping situation that involved choosing a product among three sets of frequently purchased, low-involvement, FMCG alternatives.

The findings show that when the assortment size increases, consumers acquire information from more products and cues but sacrifice product attributes. In particular, this sacrifice comes at the expense of secondary product attributes (e.g. nutrition information, country of origin), whereas primary product attributes (e.g. brand name, price) remain constant. Attribute quantity does not have a significant effect on information search.

Provided that several strategies rely on providing more information to consumers with the aim of making more deliberate and better choices, the findings suggest that they may have a limited effect in product categories in which the assortment size is wide. The authors discuss the implications for category management and public policy.

Information searches are measured by means of three different variables (searched cues, searched products and searched attributes), which enable a more complex exploration of the consumer information search process.

With the prevalence of the sharing economy phenomenon, there are an increasing number of hosts on Airbnb who manage more than one listing. Managing more listings likely makes hosts more seasoned in terms of serving guests, but it may undermine host quality due to hosts’ constrained capability. This paper aims to examine the effects of host quality attributes and the number of listings per host on the reservation performance of these listings.

Using a large-scale but granular data set of 5,805 active listings of 4,608 Airbnb hosts in Austin, Texas, this study estimates the effects of host attributes (host quality and listing quantity) on the performance of the hosts’ Airbnb listings through a blend of regression models.

This study evidences that host quality attributes significantly influence listing performance through cue-based trust. In addition, this study finds a “trade-off” between host quality and the quantity of their listings. As the number of listings managed by a host increases, the performance effects of host quality diminish.

The business implications of this study include the suggestion that sharing economy businesses such as Airbnb should sustain service quality through incentivizing hosts to improve host quality while balancing the quantity of listings managed.

This study contributes to the literature through its meaningful theoretical extension in the sharing economy context and unique data-driven insights enabled by an analytical approach. It addresses the critical but less researched topic of host quality and listing quantity and generates important practical business and policy implications.

The purpose of this study is to develop a framework for categorizing and evaluating stakeholders that addresses the key five constraints of The Theory of Stakeholder Identification and Salience (TSIS), including (1) binary attributes, (2) heterogeneous stakeholders in each category, (3) ignoring stakeholder-organization relationship, (4) ignoring stakeholders' communication frequency and (5) ignoring fringe stakeholders.

In the first step, a set of solutions for the limitations and constraints of TSIS was extracted by holding three rounds of the Delphi method with the participation of 42 senior and middle Iranian managers in various organizations and based on it, “Basic Analysis for Stakeholder Evaluation and Classification” (BASEClass) was developed as an enhanced theoretical and empirical framework for stakeholder analysis. In the second Step BASEClass is validated by conducting an empirical study in an organization with the participation of 46 managers, experts and specialists.

BASEClass is an enhanced theoretical and methodological framework for classifying stakeholders based on the three primary attributes of legitimacy, power and urgency, and also the communication quantity as a complementary attribute in a 3D cubical schema, prioritizing stakeholders in several cubes based on one of the multi-criteria group decision-making methods.

BASEClass effectively reduces the mentioned limitations and constraints of TSIS and as a result can improve the effectiveness of strategies for dealing with different stakeholders.

This paper seeks to propose a new approach for tackling the uncertainty and imprecision of identifying suitable supplier offers, evaluating these offers and choosing the best alternatives in bi‐negotiation. In a build‐to‐order supply chain, the handling of uncertainties is addressed by a real time information sharing system and appropriate supplier selection.

A methodology integrated analytic hierarchy process (AHP) with bi‐negotiation agents based on the multi‐criteria decision‐making approach and software agent technique is then developed to take into account both qualitative and quantitative factors in supplier selection.

During the decision‐making between buyer and suppliers, the AHP process matches product characteristics with supplier characteristics. Next, agents assist the user in the debate to negotiate a joint representation of the supplier chosen and automatically justify proposals with this joint representation.

This study focused on a multi‐attribute negotiation mechanism including qualitative conditions, which enables automated negotiation on multiple attributes. Finally, a fuzzy membership function represented the joint representation's cognition for each condition such as quantity, price, quality, and delivery for the outsourced component. A case study in a high‐end computer manufacturing company is given to demonstrate the potential of the methodology.

The purpose of this paper is to effectively deal with querying of classification with membership.

The authors propose a scheme comprising a layer of Bloom filter for membership checking and a second layer based on neural network for dealing with the classification requirement.

Not only could false positives be dramatically decreased, but also classification could be achieved with the proposed scheme.

The experimental data were randomly generated instead of real‐world ones.

It is difficult to implement this scheme in a real‐world environment, such as the internet. Second, the neural network requires time to converge to a satisfactory level.

Internet ethic might be compromised by hackers once they find a way around the filtering mechanism.

The neural network was moditified and utilized for the first time to be suitable for our purpose. Second, the two‐layer design shows effectiveness.

The purpose of this study is to examine the impact of neighborhood design templates on residential home values in King County, WA, USA. Previous research examines a number of individual design factors; this study combines these factors into typologies and tests for the impacts of the composite set of design features.

The study analyzes over 27,000 home sales with a hedonic price model to measure the impacts across three large, regional submarkets. Neighborhood design categories are developed using a cluster analysis on a set of individual neighborhood attributes.

The key finding from this research is that the impact of more traditional (“urban”) design packages on home values is highly contextual. For the older and denser neighborhoods in the study area, a more traditional design results in a significantly positive impact on home values. In the new and more suburban regions of the study area, this effect is not found.

Prior work focused on valuing design attributes individually. The study argues that neighborhood design is better conceived of as a “package”, as the value of a given design element may depend on other co-located attributes. This is the first study, to the authors’ knowledge, to treat physical neighborhood design variables as a composite whole and to attempt to value their impact on home values as such.

Reviews the development of Irish housing policy from the eighteenth century to the present. Emphasizes the importance of measuring quality in housing, where housing may be described by a vector of measured characteristics. Undertakes a detailed price determination of these various characteristics using a hedonic model. Explains observed house prices in terms of the various housing characteristics using a hedonic model, from which a set of implicit or “hedonic” prices emerges. Reviews the theoretical basis of such an approach and develops a model specifically for aggregated data, as other studies in this area used disaggregated models. Obtains quantitative estimates of the qualitative and quantitative characteristics of the housing bundle before and after a series of model transformations. The results indicate the premium associated with certain housing characteristics. They emphasize, in particular, the importance of floor area and location in determining new Irish estate house prices.

The purpose of this paper is to qualitatively analyze and compare people’s objectives when participating in two sets of co-creation initiatives – business-to-consumer (B2C) and business-to-business (B2B) – to what the theory in the field states about that participation.

A qualitative approach has been adopted; it uses laddering, a qualitative technique, in a novel manner through the analysis of an abstract product: the co-creation process.

Results in B2C point to a disconnection between the motivation of participants and what the theory suggests that should be expected from a co-creation agenda. In the B2B setting, the disconnections are much smaller.

The research used small and narrow samples. Additionally, the research considers only the consumers’ perspective.

Considering the context in which they compete (industrial or consumer market), companies must come up with better selection criteria for co-creators and must be more specific in setting and pursuing the goals of the co-creation projects. By doing so, organizations can achieve more fruitful results in those innovation initiatives.

The present study is innovative in the use of laddering to understand not a product nor a service, but a process: co-creation. The study reveals that, despite the buzz about co-creation, practical examples suggest that this process may not be as fruitful or satisfying as the theories suggest.

Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability.

To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems.

First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated.

An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

The purpose of this paper is to introduce a multi-level framework for semantic modeling (MFSM) based on four signification levels: objects, classes of entities, instances and domains. In addition, four fundamental propositions of the signification process underpin these levels, namely, classification, decomposition, instantiation and contextualization.

The deductive approach guided the design of this modeling framework. The authors empirically validated the MFSM in two ways. First, the authors identified the signification processes used in articles that deal with semantic modeling. The authors then applied the MFSM to model the semantic context of the literature about lean manufacturing, a field of management science.

The MFSM presents a highly consistent approach about the signification process, integrates the semantic modeling literature in a new and comprehensive view; and permits the modeling of any semantic context, thus facilitating the development of knowledge organization systems based on semantic search.

The use of MFSM is manual and, thus, requires a considerable effort of the team that decides to model a semantic context. In this paper, the modeling was generated by specialists, and in the future should be applicated to lay users.

The MFSM opens up avenues to a new form of classification of documents, as well as for the development of tools based on the semantic search, and to investigate how users do their searches.

The MFSM can be used to model archives semantically in public or private settings. In future, it can be incorporated to search engines for more efficient searches of users.

The MFSM provides a new and comprehensive approach about the elementary levels and activities in the process of signification. In addition, this new framework presents a new form to model semantically any context classifying its objects.