International Journal of Business Forecasting and Marketing Intelligence
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International Journal of Business Forecasting and Marketing Intelligence (5 papers in press)
Restoring Digitization for Sustainable Banking-Business in India by Upendra Nath Shukla Abstract: Recent initiatives of demonetisation and goods and service tax (GST) in India have promulgated more transactions via formal banking channels through digital modes making India a sustainable, less-cash economy. But current penetration of digital banking is in India is only 18% (Mckinsey Asia Financial Survey). Hence this research paper has a purpose to understand the root causes and customer hitches for digital banking and to suggest a model to increase the penetration of digital banking in India. A survey of 690 banking customers was carried out to understand customers interest for digital banking. Out of the six identified hitches for digital banking it is found that hitches of identity theft while using NET banking and hitches due to misuse of confidential information by companies are the most significant predictors to estimate the customers interest for using digital banking. The customers interest for digital banking varies significantly with age. Keywords: Digitization; Banking; Sustainability; Customers; Demonetization; India; Market; Competition; Online. DOI: 10.1504/IJBFMI.2019.10023521
Neuromarketing: A change in marketing tools and techniques by Sadhna Shukla Abstract: The much-hyped field in cognitive sciences, neuromarketing still in its nascent stage, examines the brain responses to various marketing stimuli in order to comprehend the physiological genesis of consumers choice. This study provides an integrative outline of the sophisticated tools and technologies used by neuromarketers for gaining hidden insight into consumer behaviour. Conventional marketing research approaches provided biased, intermediary solutions in revealing the true opinion of consumers. This paper also strives to present a snapshot of merits and demerits of traditional marketing and advance neuromarketing research approaches. However, earlier studies on neuromarketing have explored how different methodologies of neuromarketing are used to reveal the mysteries of consumer decision-making. Attempt has been made to integrate all the methodologies of market research to effectively probe and synthesise customers underlying emotions with the help of triune brain model. Thereby, concluding with ethical issues and dilemma faced by the neuromarketing companies in todays world. Keywords: Cognitive Science; Consumer behavior; Decision Making; Ethical; Neuromarketing; Stimuli. DOI: 10.1504/IJBFMI.2020.10023522
Critical Success Factors for Digital Platforms in B2B and B2C Environments: An Explicative Multiple Case Study Approach by Heiko Haase, Stefan Kolb, Anika Berens Abstract: Digital platforms belong to the fastest growing businesses in today's world. This paper explores critical success factors for the establishment of such platforms, focusing on both the B2B and B2C context. For this purpose, it uses a novel and confirmative qualitative approach, conducting explicative case studies of six globally leading digital platforms. Although all research theses investigated (business model's frontend and backend, agility, team diversity, ecosystem partnerships, visionary leadership and growth engines) were relevant for the analysed platforms, pivoting and branding strategy resulted as outstanding success factors. From a theoretical viewpoint, the paper contributes to enhancing the knowledge about the underlying drivers of digital platform businesses and offers options for future research. In practical terms, the outcomes contribute to effective platform creation and management. In particular, factors such as first-mover strategies, technology and design, frictionless entering, transparent information and ease of use lead to certain competitive advantages of digital platforms. Keywords: Digital platform; Success factors; Business-to-business; Business-to-consumer; Explicative case study. DOI: 10.1504/IJBFMI.2020.10023986
An interpretative structural modelling approach to Brand experience for consumer durables brands by Mukesh Govind Kharat, Rekha D. Chikhalkar, Manoj K. Jha, Manoj Kharat Abstract: The present study aims to recognise the facilitator and outcome variables of brand experience and investigate interlinks to develop a relationship among them. The variables were identified from the germane literature and brainstorming sessions with the academic and industry experts. Interpretative structural modelling (ISM) is applied to understand and model the relationship between the variables and their influence over each other. Further, the driving and dependence power is calculated to evaluate their influence. Results show that brand-related stimuli, appearance and marketing initiatives variables require a great deal of consideration due to their strategic significance. The results can be referred to as devise a marketing plan for offering a memorable and pleasant brand experience. The present study has significant implications to provide superior brand experience and strategic positioning. The study can be useful in managing and allocating the resources by understanding the strength and weakness of the studied variables. Keywords: Brand experience; Antecedents; storytelling; Appearance; Event marketing; Website features; marketing communications; customer support activities; usability; brand-related stimuli; social value; ISM,. DOI: 10.1504/IJBFMI.2020.10024053
Predicting Indian Basket Crude Prices through Machine Learning Models-A Comparative Approach by PRADIP KUMAR MITRA, CHARU BANGA Abstract: Forecasting crude price can bring some stability in the decision making process for the firms dealing with it. Crude oil is a very volatile commodity so only linear time series modelling is not sufficient to predict its price. A nonlinear model like an artificial neural network is a better choice. The paper tries to test the prediction accuracy of a conventional neural network model and deep learning model using monthly data of Indian basket price of crude oil for 18 years. A simple MLP neural network model and a deep learning model of long short-term memory are used in the present study to find accuracies in predicting the crude price. The paper finds that a simple MLP model can provide better forecasting accuracy compared to a complicated LSTM model. Keywords: Crude oil price; Forecasting; Machine learning models; Neural Network; Multilayer perceptron; Long-short term memory. DOI: 10.1504/IJBFMI.2019.10024188