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The purpose of this paper is to integrate job demands–resources theory and the episodic process model to examine the relationships between episodic cognitive mechanisms (i.e. cognitive interference and attentional pull), work engagement and performance. It is hypothesized that an episode characterized by less cognitive interference and more attentional pull (i.e. attraction toward the work activity) is associated with the highest levels of work engagement and job performance. Additionally, it is hypothesized that episodic challenge/hindrance job demands boost/diminish the positive relationship between episodic job resources and work engagement.

Using experience sampling methodology, 48 employees used their smartphones to complete surveys three times a day for one week, resulting in 266 observations.

Results of multilevel analyses suggest that episodic hindrance job demands (but not challenge job demands) moderate the positive relation between job resources and work engagement.

This study is unique in that it captures fluctuating cognitive processes (i.e. attentional pull and cognitive interference) that take place during work activities.

Robots face fundamental challenges in achieving reliable and stable operations for complex home service scenarios. This is one of the crucial topics of robotics methods to imitate human beings’ advanced cognitive characteristics and apply them to solve complex tasks. The purpose of this study is to enable robots to have the ability to understand the scene and task process in complex scenes and to provide a reference method for robot task programming in complex scenes.

This paper constructs a task modeling method for robots in complex environments based on the characteristics of the perception-motor memory model of human cognition. In the aspect of episodic memory construction, the task execution process is included in the category of qualitative spatio-temporal calculus. The topology interaction of objects in a task scenario is used to define scene attributes. The task process can be regarded as changing scene attributes on a time scale. The qualitative spatio-temporal activity graphs are used to analyze the change process of the object state with time during the robot task execution. The tasks are divided according to the different values of scene attributes at different times during task execution. Based on this, in procedural memory, an object-centered motion model is developed by analyzing the changes in the relationship between objects in the scene episode by analyzing the scene changes before and after the robot performs the actions. Finally, the task execution process of the robot is constructed by alternately reconstructing episodic memory and procedural memory.

To verify the applicability of the proposed model, a scenario where the robot combines the object (one of the most common tasks in-home service) is set up. The proposed method can obtain the landscape of robot tasks in a complex environment.

The robot can achieve high-level task programming through the alternating interpretation of scenarios and actions. The proposed model differs from traditional methods based on geometric or physical feature information. However, it focuses on the spatial relationship of objects, which is more similar to the cognitive mechanism of human understanding of the environment.

Using positive psychology theories, the authors build a model to test whether episodic fluctuations in strengths use coincide with changes in flow experiences and further predict risk-taking behavior and attentional performance.

A field study covering five working days was conducted among 164 Chinese employees; twice a day, they were asked to complete questionnaires regarding their strengths use and flow experiences during the previous hour (N = 938 observations). Immediately afterward, their risk-taking behaviors and attentional performance were tested using computerized tasks.

Multilevel analyses showed that when employees used their strengths more often in the previous hour, they also reported an increase in flow. Episodic fluctuations in flow were positively associated with risk taking and negatively related to attentional performance.

Employees should be encouraged to use their strengths more at work, as this might increase their flow experiences. At the same time, they should pay attention to the downsides of flow (i.e. less attention after flow) at an episodic level.

The authors add to previous studies by using a more objective approach, namely employing computerized tasks on risk-taking behavior and attention to capture the behavioral outcomes of work-related flow.

This research proposes and evaluates hypotheses about patterns of communication in a multi‐party, multi‐issue negotiation. Data were from 36 four‐person groups. We found that the majority of groups initiated negotiations with a distributive phase and ended with an integrative phase—strong support for Morley and Stephenson's (1979) rational model of negotiation. We identified transitions between both strategic orientations (integration, distribution) and strategic functions (action, information), but found that the first transition was more likely to result in a change of orientation than of function and that negotiators were more likely to change either orientation or function (single transition) than to change both aspects of the negotiation simultaneously (double transition). Finally, we determined that negotiators used process and closure strategies to interrupt distributive phases and redirect negotiations to an integrative phase.

Among the many influences on group decision making efficacy that have been identified by group researchers, the process that a group follows in arriving at a decision is widely regarded as one of the most important. This chapter reviews the research on group decision making processes for the purpose of explicating (a) the nature of group process, (b) the factors that influence group process, (c) the role that communication plays in group process, and (d) the influence of group process on decision making efficacy. The chapter concludes with suggestions for future research.

This paper aims to suggest a dynamic model incorporating the important dimensions that exist in negotiation processes.

To produce a general and conceptual theory of negotiation, the grounded theory methodology is deployed.

The core process in this model is dubbed “goal-oriented balancing” and describes how he negotiator is continuously balancing opposing, and seemingly contrasting, forces in a situation specific and dynamic manner to reach agreements. Based on these findings, this study also suggests a concept to describe negotiations that is focused on collaboration and that is not an oxymoron as is the concept of “win–win”.

This conceptual model can be used by managers and practitioners to navigate in a negotiation process.

This is the first grounded theory study in negotiation research and attempt to describe negotiation processes as dynamic events in which different dimensions are managed simultaneously.

In response to the lack of connection between employee development and employee engagement, this study explores the existing empirical findings regarding these two concepts. Based on the conservation of resources theory, the authors propose a novel theoretical framework that can better leverage the identified antecedents and relationships for future research.

An integrative literature review of 64 empirical studies published in peer-reviewed journals was conducted.

Three different levels of antecedents, including the work environment, social exchange and individual characteristics, are identified. Employee development and employee engagement exhibit reciprocal relationships. Considering the role of job performance as a catalyst, the authors propose an upward gain spiral model to advance both research and practice.

This integrative literature review aims to facilitate discussions based on three distinct sub-dimensions: physical, emotional and cognitive energies, relevant to both employee development and employee engagement. Through this distinction, a more comprehensive understanding of the connection between employee development and employee engagement can be cultivated.

Subtractive manufacturing process is the controlled removal of unwanted material from the parent workpiece for having the desired shape and size of the product. Several types of available machine tools are utilized to carry out this manufacturing operation. Selection of the most appropriate machine tool is thus one of the most crucial factors in deciding the success of a manufacturing organization. Ill-suited machine tool may often lead to reduced productivity, flexibility, precision and poor responsiveness. Choosing the best suited machine tool for a specific machining operation becomes more complex, as the process engineers have to consider a diverse range of available alternatives based on a set of conflicting criteria. The paper aims to discuss these issues.

Case-based reasoning (CBR), an amalgamated domain of artificial intelligence and human cognitive process, has already been proven to be an effective tool for ill-defined and unstructured problems. It imitates human reasoning process, using specific knowledge accumulated from the previously encountered situations to solve new problems. This paper elucidates development and application of a CBR system for machine tool selection while fulfilling varying user defined requirements. Here, based on some specified process characteristic values, past similar cases are retrieved and reused to solve a current machine tool selection problem.

A software prototype is also developed in Visual BASIC 6.0 and three real time examples are illustrated to validate the application potentiality of CBR system for the said purpose.

The developed CBR system for machine tool selection retrieves a set of similar cases and selects the best matched case nearest to the given query set. It can successfully provide a reasonable solution to a given machine tool selection problem where there is a paucity of expert knowledge. It can also guide the process engineers in setting various parametric combinations for achieving maximum machining performance from the selected machine tool, although fine-tuning of those settings may often be required.

The purpose of this paper is to find the event memory characteristics hidden in trade data.

First, historical trade data are analyzed to define the events described by multi‐dimensional characteristic variables. The variables containing information are employed to build the event description patterns. Furthermore, a search engine is developed for calendar events, which can search for events in historical data and produce a collection of events. The search engine also extracts relevant system reaction phenomena described by trend distribution for each event pattern. Finally, both event patterns and system reactions construct the episodic memory model.

The event patterns and the system reactions are used to define the episodic memory model. The search methods for the episodic memory model obtained from trade data set are given.

Accessibility and availability of data are the main limitations affecting where the method can be applied.

The method is helpful for traders when judging the current trade situation from historical memory.

The paper presents a new episodic memory modeling method based on trade data.

This paper aims to focus on the autonomous behavior selection issue of robotics from the perspective of episodic memory in cognitive neuroscience with biology-inspired attention system. It instructs a robot to follow a sequence of behaviors. This is similar to human travel to a target location by guidance.

The episodic memory-driving Markov decision process is proposed to simulate the organization of episodic memory by introducing neuron stimulation mechanism. Based on the learned episodic memory, the robotic global planning method is proposed for efficient behaviors sequence prediction using bottom-up attention. Local behavior planning based on risk function and feasible paths is used for behavior reasoning under imperfect memory. Aiming at the problem of whole target selection under redundant environmental information, a top-down attention servo control method is proposed to effectively detect the target containing multi-parts and distractors which share same features with the target.

Based on the proposed method, the robot is able to accumulate experience through memory, and achieve adaptive behavior planning, prediction and reasoning between tasks, environment and threats. Experimental results show that the method can balance the task objectives, select the suitable behavior according to current environment.

The behavior selection method is integrated with cognitive levels to generate optimal behavioral sequence. The challenges in robotic planning under uncertainty and the issue of target selection under redundant environment are addressed.