Identifying Buying Patterns from Consumers’ Purchase History Using Big Data and Cloud Computing

Consumers can select their goods and resources in several ways, significantly affecting customer preference in the online world and raising network customers’ demands to anticipate their purchasing pattern. The current work aims to identify buying patterns from consumers’ purchase history (IBP-CPH) framework for analyzing the evolving trend of customer decision-making in the global marketplace. The project is carried out in two stages to achieve the goals. A comprehensive research analysis is conducted to evaluate the latest consumer behavior trends in the digital economy in this first stage. In the second stage, identifying buying patterns from consumers’ purchase history (IBP-CPH) framework identifies the finalized factor’s preference amounts (s). The concept of a fugitive setting requires the incoherence of information to be recorded. The results achieved in this research stated that buyers are very aware of new and sophisticated brands and brand consistency so that internet companies can keep their customers on their web platforms.

Conceptual Framework—Artificial Intelligence and Better Entrepreneurial Decision-Making: The Influence of Customer Preference, Industry Benchmark, and Employee Involvement in an Emerging Market

Purpose: Technology initiatives are now incorporated into a wide range of business domains. The objective of this paper is to explore the possible effects that Artificial intelligence systems have on entrepreneurs’ decision-making, through the mediation of customer preference and industry benchmark. Design/methodology/approach: This is a non-empirical review of the literature and the development of a conceptual model. Searches were conducted in key academic databases, such as Emerald Online Journals, Taylor and Francis Online Journals, JSTOR Online Journals, Elsevier Online Journals, IEEE Xplore, and Directory of Open Access Journals (DOAJ) for papers which focused on Artificial intelligence (AI), Entrepreneurial decision-making, Customer preference, Industry benchmarks, and Employee involvement. In total, 25 articles met the predefined criteria and were used. Findings: The study proposes that Artificial intelligence systems can facilitate better decision-making from the entrepreneurial perspective. In addition, the study demonstrates that employees, as stakeholders, can moderate the relationship between Artificial intelligence systems and better decision-making for entrepreneurs with their involvement. Moreover, the study demonstrates that customer preference and industry benchmark can mediate the relationship between Artificial intelligence systems and better entrepreneur decision-making. Research limitations/implications: The study assumes a perfect ICT environment for the smooth operation of Artificial intelligence systems. However, this might not always be the case. The study does not consider the personal disposition of entrepreneurs in terms of ICT usage and adoption. Practical implications: This study proposes that entrepreneurial decision-making is enriched in an environment of Artificial intelligence systems, which is complemented by customer preference, industry benchmark, and employee involvement. This finding provides entrepreneurs with a possible technological tool for better decision-making, highlighting the endless options offered by Artificial intelligence systems. Social Implications: The introduction of AI in the business decision-making process comes with many social issues in relation to the impact machines have on humans and society. This paper suggests how this new technology should be used without destroying society. Originality/value: This conceptual framework serves as a valuable organizational spectrum for entrepreneurial development. In addition, this study makes a valuable contribution to entrepreneurial development through Artificial intelligence systems.

Phrase Embedding and Clustering for Sub-Feature Extraction from Online Data

Recently, online user-generated data has been used as an efficient resource for customer analysis. In the product design area, various methods for analyzing customer preference for product features have been suggested. However, most of them focused on feature categories rather than product components which are crucial in practical applications. To address that limitation, this paper proposes a new methodology for extracting part-level features from online data. First, the method detects phrases in the data and filtered them using product manual documents. The filtered phrases are embedded into vectors, and then they are divided into several groups by two clustering methods. The resulting clusters are labeled by analyzing items in each cluster. Finally, cue phrases for sub-features are obtained by selecting clusters with labels representing product features. The proposed methodology was tested on smartphone review data. The result provides feature clusters containing sub-feature phrases with high accuracy. The obtained cue phrases will be used in analyzing customer preferences for part-level features and this can help product designers determine the optimal component configuration in embodiment design.

Impact of Privatization on Airlines

The purpose of this paper is to study and identify the factors such Boarding and clearance time, Ease of E-ticketing and luggage capacity and their effect on Customer Preference in the airline. This study examines the underlying forces of service quality influences on passenger’s satisfaction in aircraft transport. The study examines which dimensions have a positive influence on service quality and which dimensions have the most and least important impact on service quality in international air travel, as perceived by airline passengers, thereby ultimately targeting maximum monetary benefits. It is a qualitative research based on subjective examination of behavior, attitude & impressions. The simplification of research support is mainly based on group discussion of respondents. The data was collected through a Focused Group Discussion. Two FGD’s each consisting of 10 participants were recorded. The findings show that how the customers preferred one airline to others because of its quality, pricing and technology and frequency of flights. The participants talked much in depth about the quality and the treatment by staff inside the airplane. They also mentioned the details about the slowdown and fallout of the airline sector in India. The limitations are that it is constraint only to Mumbai. The participants lacked knowledge of the working of the airline industry as they did not travel frequently through airplanes. Keywords: Quality, Preference, Technology, Pricing, and Punctuality

Revisiting the Universal Service Obligation Scheme

In this Journal’s tradition of revisiting past papers which have relevance to today’s events, this article reminds us of the value of the paper “Better telecommunications services for all Australians.” (2015) by Reg Coutts. This paper makes five interrelated recommendations to replace the current Universal Service Obligation (USO) policy in Australia, given the NBN rollout and customer preference for mobile services anywhere anytime. Some of its recommendations were arguably taken up by the Productivity Commission’s Public Inquiry into the USO in 2016-17, and implemented by the Australian Government in the form of a new Universal Service Guarantee.