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A conceptual confusion has evolved in brand hate (BH) research mainly due to multiple conceptualizations, models and constructs in the field. As such, there is an urgent need to bring these insights together for a holistic understanding of research in BH, fostering its growth. This paper aims to fill this theoretical gap by bringing together the field of BH and delineating opportunities for further research.

A systematic literature review was conducted for a period of about two decades, from 1998 to August 2021. The authors included the English articles published in peer-reviewed academic journals with full texts relevant to this study, leading to a usable sample of 55 articles.

The authors’ findings reveal that the literature has inadequately distinguished BH as emotion and relationship, while the theoretical domain used to explore BH remains largely dominated by the psychology literature. Furthermore, BH research has primarily focused on services, with little distinction made between hatred across product and service context, with most studies set in developed countries. The authors further identify the need to investigate boundary conditions influencing BH and develop a more robust measure of BH to capture its dynamic facet.

By presenting a comprehensive and up-to-date overview of the research in BH and highlighting the future research avenues, this study is believed to spur scholarly research and serve as a valuable tool for the researchers in advancing the research in BH.

Analysis of determinants and antecedents of BH provide managers an opportunity to nip the evil in the bud by preventing such situations that may lead to BH. Furthermore, insights into different BH consequences and boundary conditions allow brand managers to devise appropriate strategies to mitigate adverse reactions and foster positive consumer–brand relationships.

This study provides a thorough analysis of the current state of BH research in one place and draws a road map for scholars to further the research in this area.

The purpose of this paper is to highlight the studies of momentum and transmission of heat on mixed convection boundary layer Darcy‒Forchheimer flow of Casson liquid over a linear extending surface in a porous medium. The belongings of homogeneous‒heterogeneous retorts are also affianced. The mechanism of heat transmission is braced out in the form of Cattaneo‒Christov heat flux. Appropriate restorations are smeared to revolutionize coupled nonlinear partial differential equations conforming to momentum, energy and concentration of homogeneous‒heterogeneous reaction equations into coupled nonlinear ordinary differential equations (ODEs).

Numerical elucidations of the transmogrified ODEs are accomplished via a dexterous and trustworthy scheme, namely optimal homotopy analysis method. The convergence of planned scheme is exposed with the support of error table.

The exploration of mixed convection Darcy‒Forchheimer MHD boundary layer flow of incompressible Casson fluid by the linear stretched surface with Cattaneo‒Christov heat flux model and homogeneous‒heterogeneous reactions is checked in this research. Imitations of the core subsidized flow parameters on velocity, temperature and concentration of homogeneous‒heterogeneous reactions solutions are conscripted. From the recent deliberation, remarkable annotations are as follows: non-dimensional velocities in x_a− and x_b− directions shrink, whereas the non-dimensional temperature upsurges when the Casson fluid parameter ameliorates. Similar impact of Casson fluid parameter, magnetic parameter, mixed convection parameter, inertia parameter, and porosity parameter is observed for both the components of velocity field. An escalation in magnetic parameter shows the opposite attitude of temperature field as compared with velocity profile. Similar bearing of Casson fluid parameter is observed for both temperature and velocity fields. Enhancement in concentration rate is observed for growing values of (N_s) and (Sc), and it reduces for (k₁). Both temperature and concentration of homogeneous‒heterogeneous upturn by mounting the magnetic parameter. Demeanor of magnetic parameter, Casson fluid parameter, heat generation parameter is opposite to that of Prandtl number and thermal relaxation parameter on temperature profile.

In many industrial and engineering applications, the current exploration is utilized for the transport of heat and mass in any system.

As far as novelty of this work is concerned this is an innovative study and such analysis has not been considered so far.

The purpose of this paper is to address the entropy analysis of the 3D flow of Maxwell nanofluid containing gyrotactic microorganism in the presence of homogeneous–heterogeneous reactions with improved heat conduction and mass diffusion models over a stretched surface. Improved models are supported out by utilizing Cattaneo–Christov heat flux and generalized Fick’s law, respectively.

Governing equations which present the given flow phenomenon are modeled in the form of PDEs by applying boundary layer analysis and then suitable makeovers are engaged to transfigure prevailing partial differential equations into a set of ordinary differential equations. Transformed equations are handled via optimal homotopy analysis process in computational tool Mathematica and also a special case of already published work is substantiated and found to be in excellent settlement.

The bearing of innumerable convoluted physical parameters on velocity, temperature, concentration, reaction rate, the concentration of motile microorganism and entropy generation are presented and deliberated through graphs. Moreover, the convergence of the homotopic solution is presented in tabular form which confirms the reliability of the proposed scheme. It is perceived that mounting values of the magnetic parameter and Brinkman number boosts the irreversibility analysis and Bejan number diminishes for these parameters. Moreover, the growing values of Prandtl and Schmidt numbers reduce the temperature and concentration fields, respectively.

The work contained in this paper has applications in a different industry.

The work contained in this paper is original work and it is good for the researcher in the field of applied mathematics.

This paper addresses the three-dimensional flow of viscous nanofluid bounded by two plates. The lower plate stretches while the upper plate remains stationary. The fluid is electrically conducting in the presence of an applied magnetic field. In addition, the Hall, ion slip and Joule heating effects are retained. Governing equations for the considered physical happening are modeled under the phenomenon of boundary layer analysis.

Both analytical and numerical solutions for the resulting nonlinear system are derived. Numerical solutions have been presented by using bvp4c and NDSolve techniques. The homotopy analysis method is utilized for the development of convergent analytical solutions. A comparative study for the presented solutions is made. An excellent agreement between analytical and numerical solutions is noticed.

The dimensionless velocities, temperature and concentration are examined physically by two-dimensional plots, stream plot and tabular values. It is observed that Hall and ion slip parameters reduce the velocity field and temperature profile increases for the mounting values of the Eckert number.

This manuscript contains the novel contents which comprise the Hall and ion slip effects for the transportation of heat and mass for the flow of viscous nanofluid.

The purpose of this paper is to propose a process mining approach to help in making early predictions to improve students’ learning experience in massive open online courses (MOOCs). It investigates the impact of various machine learning techniques in combination with process mining features to measure effectiveness of these techniques.

Student’s data (e.g. assessment grades, demographic information) and weekly interaction data based on event logs (e.g. video lecture interaction, solution submission time, time spent weekly) have guided this design. This study evaluates four machine learning classification techniques used in the literature (logistic regression (LR), Naïve Bayes (NB), random forest (RF) and K-nearest neighbor) to monitor weekly progression of students’ performance and to predict their overall performance outcome. Two data sets – one, with traditional features and second, with features obtained from process conformance testing – have been used.

The results show that techniques used in the study are able to make predictions on the performance of students. Overall accuracy (F1-score, area under curve) of machine learning techniques can be improved by integrating process mining features with standard features. Specifically, the use of LR and NB classifiers outperforms other techniques in a statistical significant way.

Although MOOCs provide a platform for learning in highly scalable and flexible manner, they are prone to early dropout and low completion rate. This study outlines a data-driven approach to improve students’ learning experience and decrease the dropout rate.

Early predictions based on individual’s participation can help educators provide support to students who are struggling in the course.

This study outlines the innovative use of process mining techniques in education data mining to help educators gather data-driven insight on student performances in the enrolled courses.