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Trust and purchase intent are established, dependent variables in electronic commerce research. Recent studies have highlighted the importance of online product reviews in the development of purchase intention, which has led to the development of a substantial research effort in the realm of electronic word-of-mouth (e-WOM). The purpose of this paper is to incorporate e-WOM, information processing and decision-making theories to propose a model of the development of trust and purchase intention based on online product reviews, and incorporate information overload as a moderating factor.

This study tests the hypotheses using a scenario-based experiment. In total, 157 working adults were asked to read three hotel reviews of different information load. Upon completion, they were then asked to respond to Likert-based questions regarding their trust in the review and purchase intention.

An inverted U-shaped relationship exists between information load and both trust and purchase intention, where low-information load is ineffective at fostering trust and purchase intention, moderate information load is effective at fostering trust and purchase intention, and high-information load is less effective than moderate information load at fostering trust and purchase intention.

Although the authors supported the inverted U-shaped relationship between information load and two outcomes, the authors only tested three different review lengths, resulting in limited precision, it is not clear where the inflection point is (i.e. exactly how many words results in information overload). Future studies might both seek more precision, and also consider more consumer characteristics, such as risk propensity.

Review platform operators with a stake in encouraging a sale should prioritize and highlight reviews of moderate length (which can be assessed automatically via word count), and consider restricting new reviews of products to minimum and maximum word counts.

This study enhances the relevant and growing body of online review research by: bringing uncertainty reduction theory to bear on the consumer’s information search efforts; using information overload, an important construct from classic information processing and decision-making literature to explain consumer behavior; and identifying a review characteristics (information load) which influences consumer attitudes about a review (trust) and the product (purchase intention). Finally, this study enhances research understanding of a specific experiential service: hospitality.

Many online merchants today have adopted web personalization in the form of personalized product recommendations (PPRs) to improve consumer’s decision quality. The purpose of this paper is to reveal the roles of PPRs on consumer decision quality in online shopping from the theoretical perspective of information load.

To explore the dual roles of PPRs on consumer decision quality, this paper develops a research model for it. A 2 (information load: high vs low) × 2 (web personalization: PPRs vs non-PPRs) between-subjects design is conducted to empirically test the model.

The results indicate that: first, information load can increase perceived information overload and decrease perceived information underload; second, PPRs can weaken (enhance) the positive (negative) effect of information load on perceived information overload (perceived information underload); third, both perceived information overload and perceived information underload are negatively associated with consumer’s decision quality.

This paper originally develops a research model that explains the roles of PPRs on consumer decision quality from the theoretical perspective of information load in the online shopping context, which could add new insights to the field of web personalization, especially the impact of web personalization on consumer decision making.

Product presentation plays a critical role in influencing consumers’ purchasing decisions in online shopping. The purpose of this study is to examine the effects of text-image information on online product presentation pages on sales. This study also specifically explores the moderating role of celebrity endorsements in the relationship between text-image presentation information and sales.

This study is based on regression analysis using data sets from the largest online book retailer in China, Dangdang.com.

The results of this study show that there is an inverse U-shaped relationship between word count and book sales. Conversely, image quantity has a positive linear effect on book sales. Furthermore, celebrity endorsements moderate the relationship between word count and sales in two distinct ways. First, the positive effect of increasing word count from low to medium is enlarged by increasing the number of celebrity endorsers. Second, the turning point of the inverse-U relationship between word count and sales rank moves to the right as the number of endorsers increases.

To the best of the authors’ knowledge, this study is among the first to examine the effects of text-image quantity on sales across the full continuum. This study adds understanding on how information load might have distinct consequences on the processing performance of text and images. Furthermore, this study investigates how sales are impacted by the quantity of celebrity endorsers in relation to textual and pictorial information in online shopping contexts, extending our knowledge of the effectiveness of celebrity endorsements.

The main purpose of this study is to introduce the pilots’ load model and developed concept of load measuring system for operator load management.

In future aeronautical system, the role of operators (pilots and air traffic controllers [ATCOs]) will be in transition from active controlling to passive monitoring. Therefore, the operators’ load (task, information, work and mental) model was developed. There were developed measuring systems integrating into the pilot and ATCOs working environment eye tracking system outside measuring equipment. Operator load management was created by using the measurement.

In future system depending on time and automation level, the role of information and mental load will be increased. In flight simulator practice, developed load management method serves as a good tool for improving the quality of pilot training. According to the test results, the load monitoring and management system increase the safety of operators’ action in an emergency situation.

The developed method were tested in two flight simulators (one developed for scientific investigation and other one applied for pilot training) and ATM management laboratory.

By deployment of the develop load monitoring and management system, the safety of aircraft flights and air transport management will be increased, especially in an emergency situation.

People and society’s acceptance of future highly automated system will be increased.

The analysis focuses on the following: developing operator’s load model as improved situation awareness model of Endsley, developing monitoring system integrated into operator’s working environment, creating load management system.

This study aims to examine the effects of marketing dashboards on resource allocation between exploratory and exploitative activities. It proposes that tactical dashboards will lead managers to place less emphasis on exploratory activities and more emphasis on exploitative activities – with performance consequences – but that these effects will be contingent on the information and decision-making environment.

Study hypotheses were tested using an experiment tracking objective decisions over five periods in the Markstrat simulation. A total of 105 firms, each managed by a team of Master of Business Administration students, were divided into 2 dashboard conditions and a control condition.

Teams given a tactical dashboard were less likely to engage in exploratory activities when information load was high. Tactical dashboards also suppressed exploration early in the simulation. Dashboards were associated with negative firm performance overall.

The research suggests that dashboards can bias resource allocation, but the effects are contingent on the information and decision-making environment. Dashboards demonstrated a negative relationship with performance. The research lacked cognitive process measures and was limited to a single simulated industry type.

Dashboards are not a panacea for decision-making and performance and will need to change under changing conditions. Executives should build flexibility into the design and use of their dashboards and periodically audit the value the dashboard produces.

While widespread in marketing practice, dashboards have received little study and none involving decision-making over time and changing conditions. This research advances on limited existing work by examining objective causal effects.

This paper aims to present a hybrid model to mid-term forecast the load of transmission substations based on the knowledge of expert site and multi-objective posterior framework. The main important challenges in load forecasting are the different behavior of load in specific days. Regular days, holidays and special holidays, days after a holidays and days of load shifting are characterized by abnormal load profiles. The knowledge of these days is verified by expert operators in regional dispatching centers.

In this paper, a hybrid model for power prediction of transmission substations based on the combination of similar day selection and multi-objective posterior technique has been proposed. In the first step, the important data for prediction is provided. Posterior method is used in the second step for prediction that it is based on kernel functions. A multi-objective optimization has been formulated with three type of output accuracy measurement function that it is solved by non-dominated sorting genetic technique II (NSGT-II) method. TOPSIS way is used to find the best point of Pareto.

The presented method has been tested in four scenarios for three different transmission stations, and the test results have been compared. The presented results indicate that the presentation method has better results and is robust to different load characteristics, which can be used for better forecasting of different stations for better planning of repairs and network operation.

The main contributions of this paper can be categorized as follows: A hybrid model based on similar days selection and multi-objective framework posterior is presented. Similar day selection is done by expert site that the day type and days with scheduled repair are considered. Hyperparameters of posterior process are found by NSGT-II based on TOPSIS method.

Cloud computing gives several on-demand infrastructural services by dynamically pooling heterogeneous resources to cater to users’ applications. The task scheduling needs to be done optimally to achieve proficient results in a cloud computing environment. While satisfying the user’s requirements in a cloud environment, scheduling has been proven an NP-hard problem. Therefore, it leaves scope to develop new allocation models for the problem. The aim of the study is to develop load balancing method to maximize the resource utilization in cloud environment.

In this paper, the parallelized task allocation with load balancing (PTAL) hybrid heuristic is proposed for jobs coming from various users. These jobs are allocated on the resources one by one in a parallelized manner as they arrive in the cloud system. The novel algorithm works in three phases: parallelization, task allocation and task reallocation. The proposed model is designed for efficient task allocation, reallocation of resources and adequate load balancing to achieve better quality of service (QoS) results.

The acquired empirical results show that PTAL performs better than other scheduling strategies under various cases for different QoS parameters under study.

The outcome has been examined for the real data set to evaluate it with different state-of-the-art heuristics having comparable objective parameters.

The purpose of this paper is to assess next hour load forecast in medium voltage electricity distribution.

The methodological approach used in this paper, is based on a regressive method – artificial neural network. A real life case study is used for illustrating the defined steps and to discuss the results.

The presence of a de‐regulated environment reinforces the need of short‐term forecast algorithms (STLF). Actions like network management, load dispatch and network reconfiguration under quality of service constraints, require reliable next hour load forecasts. Methodological approaches based on regressive methods such as artificial neural networks are widely used in STLF, with satisfactory results. The construction of an “efficient” artificial neural networks goes through, among other factors, the construction of an “efficient” input vector (IV), in order to avoid over fitting problems and keeping the global simplicity of the model. The explanatory variables normally used, are grouped in two major classes, endogenous and exogenous. The endogenous variables are load values in past instants, and the exogenous variables are normally climatic. The main findings with this kind of vector presents satisfactory results compared to other proposals in the literature.

This paper makes use of a procedural sequence for the pre‐processing phase that allows capturing some predominant relations among certain different sets of the available data, providing a more solid basis to decisions regarding the composition of the IV. To deal with load increasing during the winter period, the forecast average daily temperature was used in order to produce an indicator of the daily load average for the forecast day. This information brings more accuracy to the model.

This paper aims to investigate the influence of map design characteristics on users’ cognitive load and search performance. Two design conditions (symbolic vs non-symbolic) were used to evaluate users’ ability to locate a place of interest.

A total of 19 students (10 male and 9 female, 20-23 years old) participated in this study. The time required for subjects to find a place in the two conditions was used to estimate their searching performance. An electroencephalogram (EEG) device was used to examine students’ cognitive load using event-related desynchronization percentages of alpha, beta and theta brain wave rhythms.

The results showed that subjects needed more time to find a place in the non-symbolic condition than the symbolic condition. The EEG data, however, revealed that users experienced higher cognitive load when searching for a place in the symbolic condition. The authors found that the design characteristics of the map significantly influenced users’ brain activity, thus impacting their search performance.

Outcomes from this study can be used by cartographic designers and scholars to understand how certain design characteristics can trigger cognitive activity to improve users' searching experience and efficiency.

Technological advances and the adaption of higher levels of automation serve as a potential cause of aviation incidents and accidents. This study aims to investigate the effect of automated systems on the operator’s performance total load (work, task, information, communication and mental) in highly advanced systems.

A questionnaire was designed for aviation operators (Pilots, ATCOs) to understand the intensity to which automation has affected their working environment and personal behavior. In total, 115 responses were received from 44 countries worldwide. Approximately, 66% of respondents were pilots, 27% Air traffic controllers and 7% were both pilots and ATCOs with various experience levels.

Based on the results of this questionnaire, this study suggests the following: creating a total load management model to understand the best load balance an operator could perform at providing rapidly updated aviation training methods and approaches investigating the influence and consequences of adding new tools to the operator’s working station and redesigning it to achieve top operator-machine equilibrium redesigning information and alerting systems.

Intrinsic limitations include an implicit expression of bias in the way questions are phrased, ambiguity in question phrasing that leads to incorrect conclusions and challenges regarding articulating complex concepts.

In this paper, the authors aimed to assess and investigate factors leading to current and future incidents and accidents resulting from human factors, specifically caused or developed because of highly automated systems.