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1 – 10 of over 122000Chenfeng Xiong, Xiqun Chen and Lei Zhang
This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models, and demonstration in an agent-based microsimulation.
Abstract
Purpose
This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models, and demonstration in an agent-based microsimulation.
Theory
A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.
Findings
The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.
Originality and value
Based on artificially intelligent agents, learning and search theory, and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based microsimulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.
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Dan Wu, Liuxing Lu and Lei Cheng
This paper aims to establish a theoretical search model on academic social networking sites (ASNSs).
Abstract
Purpose
This paper aims to establish a theoretical search model on academic social networking sites (ASNSs).
Design/methodology/approach
Based on the characteristics of ASNSs and a previous extended sense-making model, this paper first presented an initial model of searching on ASNSs. Next, an online survey was conducted on ResearchGate to understand the search processes and outcomes with the help of a survey questionnaire. In total, 359 participants from 70 countries participated in this online survey. The survey results provided a basis for modifying the initial model.
Findings
Results showed that the theoretical model of searching on ASNSs included motives for searching on ASNSs, identification of needs, search triggered by information needs, search triggered by social needs and outcomes. The search triggered by information needs was significantly positively correlated with learning outcomes. Besides learning outcomes, searching on ASNSs could help user amplify their social networks and promote research dissemination.
Practical implications
Understanding users’ search habits and knowledge acquisition can provide insights for ASNSs to design an interface to support searching and enhance learning. Moreover, the proposed model can help users recognize their knowledge status and learning effects and improve their learning efficiency.
Originality/value
This paper contributes to establishing a theoretical model to understand users’ search process and outcomes on ASNSs.
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Ziming Zeng, Shouqiang Sun, Tingting Li, Jie Yin and Yueyan Shen
The purpose of this paper is to build a mobile visual search service system for the protection of Dunhuang cultural heritage in the smart library. A novel mobile visual search…
Abstract
Purpose
The purpose of this paper is to build a mobile visual search service system for the protection of Dunhuang cultural heritage in the smart library. A novel mobile visual search model for Dunhuang murals is proposed to help users acquire rich knowledge and services conveniently.
Design/methodology/approach
First, local and global features of images are extracted, and the visual dictionary is generated by the k-means clustering. Second, the mobile visual search model based on the bag-of-words (BOW) and multiple semantic associations is constructed. Third, the mobile visual search service system of the smart library is designed in the cloud environment. Furthermore, Dunhuang mural images are collected to verify this model.
Findings
The findings reveal that the BOW_SIFT_HSV_MSA model has better search performance for Dunhuang mural images when the scale-invariant feature transform (SIFT) and the hue, saturation and value (HSV) are used to extract local and global features of the images. Compared with different methods, this model is the most effective way to search images with the semantic association in the topic, time and space dimensions.
Research limitations/implications
Dunhuang mural image set is a part of the vast resources stored in the smart library, and the fine-grained semantic labels could be applied to meet diverse search needs.
Originality/value
The mobile visual search service system is constructed to provide users with Dunhuang cultural services in the smart library. A novel mobile visual search model based on BOW and multiple semantic associations is proposed. This study can also provide references for the protection and utilization of other cultural heritages.
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Souvick Ghosh, Julie Gogoi and Kristen Chua
Turn-taking is beneficial to conversational search success, but the increase in turns and time can also increase the cognitive load of the user. Therefore, in this research paper…
Abstract
Purpose
Turn-taking is beneficial to conversational search success, but the increase in turns and time can also increase the cognitive load of the user. Therefore, in this research paper, the authors view conversational search sessions through the lens of economic theory and use the economic models of search to analyze the various costs and benefits of information-seeking interactions.
Design/methodology/approach
First, the authors built a cost-benefit model for conversational search sessions by defining action types and performing an intellectual mapping of actual sessions into sequences of these actions (using thematic analyses). The authors used the hypothesized cost and benefit actions (obtained from the user-system dialogs), along with the number of turns, utterances and time-related parameters, to propose the mathematical model. Next, the authors tested the model empirically by comparing the model scores to the user satisfaction and task success scores (collected through questionnaires). By representing each session as a bag of actions, the authors developed linear regression models to predict task success and user satisfaction.
Findings
Through feature analysis and significance testing, the authors identify the different parameters that contribute significantly to user satisfaction and task success scores. Error analysis shows that the model predicts task success and user satisfaction reasonably well, with the average prediction error being 0.5 for both (on a 5-point scale).
Originality/value
The authors' research is an initial step toward building a mathematical model for predicting user satisfaction and task success in conversational search sessions.
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Tanmoy Hazra, C.R.S. Kumar and Manisha Nene
The purpose of this paper is to propose a model for a two-agent multi-target-searching scenario in a two-dimensional region, where some places of the region have limited resource…
Abstract
Purpose
The purpose of this paper is to propose a model for a two-agent multi-target-searching scenario in a two-dimensional region, where some places of the region have limited resource capacity in terms of the number of agents that can simultaneously pass through those places and few places of the region are unreachable that expand with time. The proposed cooperative search model and Petri net model facilitate the search operation considering the constraints mentioned in the paper. The Petri net model graphically illustrates different scenarios and helps the agents to validate the strategies.
Design/methodology/approach
In this paper, the authors have applied an optimization approach to determine the optimal locations of base stations, a cooperative search model, inclusion–exclusion principle, Cartesian product to optimize the search operation and a Petri net model to validate the search technique.
Findings
The proposed approach finds the optimal locations of the base stations in the region. The proposed cooperative search model allows various constraints such as resource capacity, time-dependent unreachable places/obstacles, fuel capacities of the agents, two types of targets assigned to two agents and limited sortie lengths. On the other hand, a Petri net model graphically represents whether collisions/deadlocks between the two agents are possible or not for a particular combination of paths as well as effect of time-dependent unreachable places for different combination of paths are also illustrated.
Practical implications
The problem addressed in this paper is similar to various real-time problems such as rescue operations during/after flood, landslide, earthquake, accident, patrolling in urban areas, international borders, forests, etc. Thus, the proposed model can benefit various organizations and departments such as rescue operation authorities, defense organizations, police departments, etc.
Originality/value
To the best of the authors’ knowledge, the problem addressed in this paper has not been completely explored, and the proposed cooperative search model to conduct the search operation considering the above-mentioned constraints is new. To the best of the authors’ knowledge, no paper has modeled time-dependent unreachable places with the help of Petri net.
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Dheeraj Awasthy, Arindam Banerjee and Bibek Banerjee
Existing literature offers conflicting evidence on how prior product knowledge influences amount of information search. A majority of these studies are based on variants of cost…
Abstract
Purpose
Existing literature offers conflicting evidence on how prior product knowledge influences amount of information search. A majority of these studies are based on variants of cost benefit frameworks where consumers engage in search until the benefits from information search exceed search costs. The purpose of this paper is to develop an expectancy theory‐based framework to model consumers' information search and its antecedents, including motivation to search as an intervening construct.
Design/methodology/approach
The framework is tested using data from real consumers engaged in their actual purchase decisions, in an emerging market context, using longitudinal survey research design. The data are analysed using structural equation modeling to test the hypothesized model. The model shows an acceptable fit with X2 (271, 487)=640.252, p < 0.00 and 0.95 CFI.
Findings
Results indicate that the relationship between prior product knowledge to information search is mediated by motivation to search. Prior product knowledge influences motivation to search through its influence on the consumer's perceived ability to search and his/her perceived value of additional information. Furthermore, perceived ability to search is the strongest predictor of motivation to search. The parsimony of the proposed framework in providing a simpler account of factors influencing the search process along with its managerial implications is discussed.
Practical implications
The findings suggest that perceived ability to search and perceived value of additional information are two important levers that managers could use for achieving desired results. Furthermore, perceived ability to search is an important mediator, which completely mediates the relationship between prior product knowledge and motivation to search. These findings also provide strong indications about the need to simplify the search process for consumers, especially in the context when novelty is predominantly marketed.
Originality/value
The paper introduces a motivational measure of search in the literature and shows that the motivational measure is indeed the proximal measure to other antecedent constructs compared to a behavioral measure of search. Perceived ability to search and perceived value of additional information are shown as important mediators between prior product knowledge and motivation to search.
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Jianping Peng, Guoying Zhang, Shaoling Zhang, Xin Dai and Jing Li
– The purpose of this paper is to explore the effects of online advertising spending on automobile sales through both search and non-search advertising.
Abstract
Purpose
The purpose of this paper is to explore the effects of online advertising spending on automobile sales through both search and non-search advertising.
Design/methodology/approach
Sales data of the top 52 vehicle models were collected in two consecutive years in China. The advertising spending data of both formats were collected from a leading consulting company and a major search engine company. Then several empirical models were proposed to evaluate the effects of online advertising on automobile sales. Two extended models were further investigated for search advertising.
Findings
The results revealed that both formats of online advertising have significantly positive effects on automobile sales. However, excessive spending on non-search advertising does not help sales and a moderate budget is preferred. On the other hand, spending on search advertising has no such constraint to improve the vehicle sales.
Practical implications
The empirical findings have proved the importance of online advertising to the automobile companies and thus can help companies improve their decision making in online advertising allocation strategies.
Originality/value
This study provides a better understanding of the relationship between online advertising spending and automobile sales, and helps business to define sophisticated online advertising strategies to improve sales performance.
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Gerd Berget, Andrew MacFarlane and Nils Pharo
A substantial number of models have been developed over the years, with the purpose of describing the information seeking and searching of people in various user groups and…
Abstract
Purpose
A substantial number of models have been developed over the years, with the purpose of describing the information seeking and searching of people in various user groups and contexts. Several models have been frequently applied in user studies, but are rarely included in research on participants with impairments. Models are purposeful when developing theories. Consequently, it might be valuable to apply models when studying this user group, as well. The purpose of this study was to explore whether existing models are applicable in describing the online information seeking and searching of users with impairments, with an overall aim to increase the use of models in studies involving impairments.
Design/methodology/approach
Six models were selected according to the following criteria: the model should address information seeking or searching, include the interaction between users and systems whilst incorporating assistive technology. Two user groups were selected from each of the categories: cognitive, sensory and motor impairments, namely dyslexia, autism, blindness, deafness, paralysation and Parkinson's. The models were then analysed based on known barriers reported for these cohorts.
Findings
All the selected models had potential to be applied in user studies involving impairments. While three of the models had the highest potential to be used in the current form, the other three models were applicable either through minor revisions or by combining models.
Originality/value
This study contributes with a new perspective on the use of models in information seeking and searching research on users with impairments.
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Over the past two decades, online booking has become a predominant distribution channel of tourism products. As online sales have become more important, understanding booking…
Abstract
Purpose
Over the past two decades, online booking has become a predominant distribution channel of tourism products. As online sales have become more important, understanding booking conversion behavior remains a critical topic in the tourism industry. The purpose of this study is to model airline search and booking activities of anonymous visitors.
Design/methodology/approach
This study proposes a stochastic approach to explicitly model dynamics of airline customers’ search, revisit and booking activities. A Markov chain model simultaneously captures transition probabilities and the timing of search, revisit and booking decisions. The suggested model is demonstrated on clickstream data from an airline booking website.
Findings
Empirical results show that low prices (captured as discount rates) lead to not only booking propensities but also overall stickiness to a website, increasing search and revisit probabilities. From the decision timing of search and revisit activities, the author observes customers’ learning effect on browsing time and heterogeneous intentions of website visits.
Originality/value
This study presents both theoretical and managerial implications of online search and booking behavior for airline and tourism marketing. The dynamic Markov chain model provides a systematic framework to predict online search, revisit and booking conversion and the time of the online activities.
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