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The aim of this paper is to explore the price plans offered by Thai mobile operators and analyse the role of demand characteristics in the development of new price plans. The paper also shows how demand affects a firm's degree of innovativeness in terms of the number of new price plans.

The empirical qualitative analysis is based on an original data set from several secondary data sources and includes all the price plans offered in the history of the Thai mobile communications market between 2002 and 2010.

The results show that mobile operators have introduced several innovative price plans to attract and retain their consumers. Although a greater number of price plans can increase competition among operators, some have complex combinations that may lead to confusion for consumers.

A price comparison programme should therefore be implemented by the telecom regulator to ensure that consumers receive correct and complete information about the price plans.

Most studies, by far, have not extensively discussed this mobile communications market in detail and the effect of innovation on competition between firms in the mobile communications industry, in particular the development of innovation in developing countries.

This paper presents approaches to determine a network-based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The purpose of this study is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad hoc and subjective prices.

A data mining approach with machine learning methods is used to estimate a price range based on the profile characteristics of 3D printing service suppliers. The model considers factors such as supplier experience, supplier capabilities, customer reviews and ratings from past orders and scale of operations, among others, to estimate a price range for suppliers’ services. Data were gathered from existing marketplace websites, which were then used to train and test the model.

The model demonstrates an accuracy of 65 per cent for US-based suppliers and 59 per cent for Europe-based suppliers to classify a supplier’s 3D printer listing in one of the seven price categories. The improvement over baseline accuracy of 25 per cent demonstrates that machine learning-based methods are promising for network-based pricing in manufacturing marketplaces

Conventional methodologies for pricing services through activity-based costing are inefficient in strategically priced 3-D printing service offering in a connected marketplace. As opposed to arbitrarily determining prices, this work proposes an approach to determine prices through data mining methods to estimate competitive prices. Such tools can be built into online marketplaces to help independent service bureaus to determine service price rates.

Several research papers related to electricity price forecasting have been reported in the leading journals in last 20 years. The purpose of this paper is to present a comprehensive survey and comparison of these techniques.

The present article provides an overview of the statistical short‐term price forecasting (STPF) models. The basic theory of these models, their further classification and their suitability to STPF has been discussed. Quantitative evaluation of the performance of these models in the framework of accuracy achieved and computation time taken has been performed. Some important observations of the literature survey and key issues regarding STPF methodologies are analyzed.

It has been observed that price forecasting accuracy of the reported models in day‐ahead markets is better as compared to that in real time markets. From a comparative analysis perspective, there is no hard evidence of out‐performance of one model over all other models on a consistent basis for a very long period. In some of the studies, linear models like dynamic regression and transfer function have shown superior performance as compared to non‐linear models like artificial neural networks (ANNs). On the other hand, recent variations in ANNs by employing wavelet transformation, fuzzy logic and genetic algorithm have shown considerable improvement in forecasting accuracy. However more complex models need further comparative analysis.

This paper is intended to supplement the recent survey papers, in which the researchers have restricted the scope to a bibliographical survey. Whereas, in this work, after providing detailed classification and chronological evolution of the STPF techniques, a comparative summary of various price‐forecasting techniques, across different electricity markets, is presented.

This study aims to determine whether, by means of the application of genetic algorithms (GA) through the traditional technical analysis (TA) using moving average convergence/divergence (MACD), is possible to achieve higher yields than those that would be obtained using technical analysis investment strategies following a traditional approach (TA) and the buy and hold (B&H) strategy.

The study was carried out based on the daily price records of the NASDAQ financial asset during 2013–2017. TA approach was carried out under graphical analysis applying the standard MACD. GA approach took place by chromosome encoding, fitness evaluation and genetic operators. Traditional genetic operators (i.e. crossover and mutation) were adopted as based on the chromosome customization and fitness evaluation. The chromosome encoding stage used MACD to represent the genes of each chromosome to encode the parameters of MACD in a chromosome. For each chromosome, buy and sell indexes of the strategy were considered. Fitness evaluation served to defining the evaluation strategy of the chromosomes in the population according to the fitness function using the returns gained in each chromosome.

The paper provides empirical-theoretical insights about the effectiveness of GA to overcome the investment strategies based on MACD and B&H by achieving 5 and 11% higher returns per year, respectively. GA-based approach was additionally capable of improving the return-to-risk ratio of the investment.

Limitations deal with the fact that the study was carried out on US markets conditions and data which hamper its application in some extend to markets with not as much development.

The findings suggest that not only skilled but also amateur investors may opt for investment strategies based on GA aiming at refining profitable financial signals to their advantage.

This paper looks at machine learning as an up-to-date tool with great potential for increasing effectiveness in profits when applied into TA investment approaches using MACD in well-developed stock markets.

The recent advancements in smartphone technology and social media platforms have increased the popularity of artificial intelligence (AI) color cosmetics. Meanwhile, China is a lucrative market for various foreign beauty products and technological innovations. This research aims to investigate the adoption of AI color cosmetics applications and their electronic word-of-mouth (e-WOM) intention among Chinese social media influencers. Several key concepts have been proposed in this research, namely body esteem, price sensitivity, social media addiction and actual purchase.

An online questionnaire design was used in this research. A combination of purposive sampling and snowball sampling of AI color cosmetics users who are also social media influencers in China yields 221 respondents. To analyze the data, this research employs Structural Equation Modelling (SEM) method via SPSS and AMOS software. A 2-step approach, Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), is implemented to prove the hypotheses and generate the results.

1) Social media addiction is a positive predictor of AI color cosmetics usage, (2) AI color cosmetics usage is a positive predictor of actual purchase, (3) actual purchase is a positive predictor of e-WOM intention and lastly, (4) there is a full mediation effect of actual purchase.

This research draws on the uses and gratification (U&G) theory to investigate how specific user characteristics affect Chinese social media influencers' adoption of AI color cosmetics, as well as how this may affect their decision to purchase branded color cosmetics and their e-WOM.

This opinion piece reviews the forces driving the growth of public relations measurement between the mid‐1980s and the present day. Looking to the future, it discusses the increasing importance of reputation as a key intangible corporate asset and discusses the emerging efforts to measure reputation and the effectiveness of public relations in building reputation.

The purpose of this paper is to assess the impact on mobile demand of an asymmetric regulation policy introduced in the year 2009 in Colombia. It aims to do this by estimating demand models for mobile services during the 2005 to 2011 period.

The economic analysis uses two‐stage least squares and ordinary least squares (OLS) econometric techniques. The paper models minutes used in Colombia as a function of prices, income, a time trend and a regulation dummy variable. The study controls for endogeneity issues in the price variable by using two instruments: Colombia's exchange rate COP – USD and the Producer Price Index.

The paper finds a price elasticity of demand and an income elasticity of approximately ‐0.66 and 0.30, respectively, within the range of previous findings in the literature. The study estimates that the introduction of the asymmetric regulation on the incumbent mobile's on‐net and off‐net prices reduced demand for mobile services and caused a loss in consumer surplus of approximately USD 108 million.

The paper presents the first empirical analysis of a regulatory policy affecting prices at the retail level on consumer's welfare in the mobile sector in Colombia. It advises policy‐makers in the telecommunication sector to use caution when regulating mobile markets' prices because the costs of this regulation can be significant.

This paper aims to explore netizen’s opinions on cryptocurrency under the lens of emotion theory and lexicon sentiments analysis via machine learning.

An automated Web-scrapping via RStudio is performed to collect the data of 15,000 tweets on cryptocurrency. Sentiment lexicon analysis is done via machine learning to evaluate the emotion score of the sample. The types of emotion tested are anger, anticipation, disgust, fear, joy, sadness, surprise, trust and the two primary sentiments, i.e. negative and positive.

The supervised machine learning discovers a total score of 53,077 sentiments from the sampled 15,000 tweets. This score is from the artificial intelligence evaluation of eight emotions, i.e. anger (2%), anticipation (18%), disgust (1%), fear (3%), joy (15%), sadness (3%), surprise (7%), trust (15%) and the two sentiments, i.e. negative (4%) and positive (33%). The result indicates that the sample primarily contains positive sentiments. This finding is theoretically significant to measure the emotion theory on the sampled tweets that can best explain the social implications of the cryptocurrency phenomenon.

This work is limited to evaluate the sampled tweets’ sentiment scores to explain the social implication of cryptocurrency.

The finding is necessary to explain the recent phenomenon of cryptocurrency. The positive sentiment may describe the increase in investment in the decentralised finance market. Meanwhile, the anticipation emotion may illustrate the public’s reaction to the bubble prices of cryptocurrencies.

Previous studies find that the social signals, e.g. word-of-mouth, netizens’ opinions, among others, affect the cryptocurrencies’ movement prices. This paper helps explain the social implications of such dynamic of pricing via sentiment analysis.

This study contributes to theoretically explain the implications of the cryptocurrency phenomenon under the emotion theory. Specifically, this study shows how supervised machine learning can measure the emotion theory from data tweets to explain the implications of cryptocurrencies.