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Prior research suggests that there is enough residual uncertainty in conflict situations so that a person's attitude towards risk may influence his or her conflict behavior. This paper explores the level of dyadic conflict arising from negotiation between partners having different combinations of risk propensities. Dyadic conflict was measured as the sum of each dyadic partner's conflict score using the Rahim Organizational Conflict Inventory‐I. Risk propensities of negotiators were induced The results from the experiment provide clear evidence in support of the research hypothesis that in a dyad, the greater the disparity between the negotiating partners in their risk‐taking propensities, the greater will be the levels of dyadic conflict. The result suggests that conflict models of negotiating under uncertainty need to include risk propensities of the players to expand their descriptive power.

The purpose of this paper is to first examine whether intellectual capital (IC) information is considered in firm valuation. Next, to examine two issues: financial analysts' investment recommendations when faced with different combinations of performance levels (i.e. above or below industry average) of financial and IC measures, and the role of financial and IC measures with different performance levels and holding periods (i.e. short‐term vs long‐term) for the investment on analysts' recommendations.

The first part of the paper used secondary (both archival and survey) data. The second part was an experiment.

The findings in the first part show that, after controlling for the effect of financial performances on firm value, measures of IC are still significant explanatory variables (of firm value). The second part shows that the financial and IC measures affect financial analysts' investment recommendations differently depending on the measures' levels of performance and the time horizon for holding the investments.

The limitations of the paper are as follows: the use of secondary data from a single country limits its generalizability; and the results of the experiment are parameterized by the research design such as the amount of information provided to the financial analysts. Extending the analyses to other settings and using time‐series data represent future research opportunities.

The research makes three contributions to the IC literature. First, it extends the studies on the relevance of IC in capital market research by broadening its scope to include measures of IC other than R&D intensity. Next, it provides evidence of the informativeness of IC measures in market valuation of firms and analysts' recommendations, thus lending credence to the arguments of reports and researchers for more external communication of IC information. Finally, this study is one of the first to examine a broader scope of IC in the capital market context and the use of IC by sophisticated market participants. With policy‐makers and standard‐setting bodies considering proposals to enhance information on IC in financial reports, it is important to broaden the scope of IC metrics and understand their role in enhancing firm value to develop a framework for reporting IC.

Risk‐related attitudes are important in today's business environment because as organisations become flattened decisions are pushed down in the hierarchy and a greater percentage of the individuals in the organisation must become proficient decision makers [16]. Risk preference is important because recent work shows that: (1) as risk tolerance increases so does the individual's effectiveness in some decision‐making activities [6], and that (2) self confidence is related to risk taking [8]. This study was designed to examine the relationships among information‐system‐related knowledge and experience, confidence (i.e. personal prospects), risk preference (i.e. planning horizon), and risk‐taking propensity (i.e. utility for risk).

This study aims to undertake a comprehensive examination of heat transfer by convection in porous systems with top and bottom walls insulated and differently heated vertical walls under a magnetic field. For a specific nanofluid, the study aims to bring out the effects of different segmental heating arrangements.

An existing in-house code based on the finite volume method has provided the numerical solution of the coupled nondimensional transport equations. Following a validation study, different explorations include the variations of Darcy–Rayleigh number (Ra_m = 10–10⁴), Darcy number (Da = 10^–5–10^–1) segmented arrangements of heaters of identical total length, porosity index (ε = 0.1–1) and aspect ratio of the cavity (AR = 0.25–2) under Hartmann number (Ha = 10–70) and volume fraction of φ = 0.1% for the nanoparticles. In the analysis, there are major roles of the streamlines, isotherms and heatlines on the vertical mid-plane of the cavity and the profiles of the flow velocity and temperature on the central line of the section.

The finding of a monotonic rise in the heat transfer rate with an increase in Ra_m from 10 to 10⁴ has prompted a further comparison of the rate at Ra_m equal to 10⁴ with the total length of the heaters kept constant in all the cases. With respect to uniform heating of one entire wall, the study reveals a significant advantage of 246% rate enhancement from two equal heater segments placed centrally on opposite walls. This rate has emerged higher by 82% and 249%, respectively, with both the segments placed at the top and one at the bottom and one at the top. An increase in the number of centrally arranged heaters on each wall from one to five has yielded 286% rate enhancement. Changes in the ratio of the cavity height-to-length from 1.0 to 0.2 and 2 cause the rate to decrease by 50% and increase by 21%, respectively.

Further research with additional parameters, geometries and configurations will consolidate the understanding. Experimental validation can complement the numerical simulations presented in this study.

This research contributes to the field by integrating segmented heating, magnetic fields and hybrid nanofluid in a porous flow domain, addressing existing research gaps. The findings provide valuable insights for enhancing thermal performance, and controlling heat transfer locally, and have implications for medical treatments, thermal management systems and related fields. The research opens up new possibilities for precise thermal management and offers directions for future investigations.

Artificial intelligence (AI) has become an input to the production of goods and services. Therefore, a general question is there that “How the labor hour/human resource will be replaced by the artificial intelligence?” To answer this question, the paper considers that both AI and the human resources (HR) are the inputs to the firm and explains the choice between the two with reference to the customer relationship management. The paper derives the individual firms and the industry demand functions of the AI and the HR when both are present in the production of the identical or closely related goods and services. Moreover, the paper also shows the strategic behavior of an individual firm with the industry in selecting the AI and the HR. It has been shown that the individual firm's choice in the industry depends on the choice of the industry leader. The paper explains the supermodular game between the firms in an industry.

Game theory, industrial organization and non-convexity theories have been used in this paper to identify the choice between the HR and the AI in the customer relationship management.

The paper explains analytically the preference and demand for AI in the industry. Individual firm's strategic behavior and decision on choosing AI and the industry equilibrium have been studied logically. Moreover, the paper gives some light on the question of employment in presence of AI. The paper proves that in the presence of AI, labor demand will not be reduced but both will be used.

This work proves for the first time using some logical derivation that AI will not crowd out labor from the market. Moreover, to run AI, labor should also be used. It has been proved that to complete a job with speed and quality, both AI and HR are to be used.