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This paper attempts to identify the biases in decision-making of individual investors. The paper aims to develop and validate a higher-order behavioral biases scale.

Scale development is done by identifying the relevant items of the scale through existing literature and then, adding new items for some biases. In phase 1, using a structured questionnaire, data was collected from 274 investors who invest in financial markets. The major dimensions of the scale have been pruned by using exploratory factor analysis administered on data collected in phase 1. Higher-order CFA is used to analyze the data and to validate the scale on another set of data (collected in phase 2) containing 576 investors.

The study reveals that the scale for measuring behavioral biases has many dimensions. It has two second-order factors and 13 zero-order constructs. Two second-order constructs have been modeled on the basis of cause of errors in investment decision-making, that is, biases caused due to cognition, biases caused due to emotions.

Behavioral biases are yet to receive a due attention, especially, in the Indian context. The present research is focusing on providing an empirically tested scale to test the behavioral biases. Some of the biases, which have been analyzed using secondary data in previous studies, have been tested with the help of statements in this study.

The purpose of the current article is to explore the rotational behavior on nanofluid flow over an exponentially stretching surface. Heat and mass flux are formulated upon Cattaneo–Christov theory.

Effect of thermophoretic, Brownian motion and thermally convective conditions is further retained. Novel boundary layer approximations are applied to transform the governing equations of continuity, momentum, energy and nanoparticle volume fraction. Convergent series solutions are obtained to manage the rotating flow with the aid of homotopy analysis method (HAM).

Depending on the several dimensionless parameters including the local rotation parameter the Prandtl number Pr, the thermophoresis parameter, the Brownian motion parameter, the Lewis number Le, Biot number Bi, Deborah number in terms of heat flux relaxation parameter and Deborah number in terms of mass flux relaxation parameter with the dimensionless physical quantities are deliberated through graphs. Present results are also likened with the foregoing results in significance.

No such assumptions have been made for the development of analytical solution so far.

Purpose – This study aims to investigate how behavioural biases affect cryptocurrency investment choices. The study also evaluates how risk tolerance mediates the relationship between behavioural biases and investment decision-making.

Need for the Study – The study is required to refine research methods and to ensure the reliability and validity of findings on behavioural biases in cryptocurrency investment decision-making.

Methodology – This pilot study involved responses from individuals in India’s western and northern regions who either invested in cryptocurrencies or had adequate knowledge of such investments. To assess the normality, validity, and reliability of the questionnaire data, a sample of 51 individuals was analysed using SPSS software.

Findings – The results of this study validate the reliability of the questionnaire in conducting pilot research by attaining high reliability with high coefficients of measures and reasonable normality.

Originality/value – The study confirmed the tool’s efficiency to analyse various specific antecedents influencing investing choices.

This article intends to explore critical factors that affect the adoption of organic farming in emerging economies. The authors respond to the calls from policymakers, non-government organizations, business firms and scholars to improve the farmers' awareness of the negative impact of synthetic chemical pesticides, phosphorus, potassium fertilizers and mineral nitrogen used in traditional farming.

Through self-administered survey questionnaires, responses were obtained from 397 farmers (conventional) regarding organic farming adoption in Haryana (India). The survey responses were collected between October 2022 and December 2022. The authors apply the “partial least squares structural equation modeling” (PLS-SEM) to test the framed hypotheses.

The present article demonstrates six critical determinants of organic farming adoption, i.e. behavioral, cultivation, economic, governmental, marketing, and social factors. These six factors drive 71.0% (R²) variation in organic farming adoption. Governmental factors have a positive but insignificant influence on organic farming adoption. Interestingly, the impact of behavioral and cultivation factors is crucial per path coefficient values.

For the first time, the authors conducted a study on organic farming adoption in Haryana that lies in its context-specific implementation, utilization of localized knowledge and expertise, regional policy support, agricultural diversification and community participation. Future research can build upon by adding agriculture scientists to the study to respond to the cost, quality of the crop and impact of socio-economic policies as moderators/mediators on adoption decisions.