Mobile Phone Price Research Articles

Comparative Evaluation of Machine Learning Models for Mobile Phone Price Prediction: Assessing Accuracy, Robustness, and Generalization Performance

These days, mobile phones are the most commonly purchased goods. Thousands of new models with improved features, designs, and specifications are released yearly. An autonomous mobile price prediction system is required to assist customers in determining whether or not they can afford these devices. Many machine learning models exhibit varying performance degrees based on their architecture and learning properties. Ten widely used classifiers were assessed in this study: Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Decision Tree (DT), Naïve Bayes (NB), Linear Discriminant Analysis (LDA), AdaBoost, and Light Gradient Boosting (LGB). The F1-score, recall, accuracy, and precision of these models were evaluated. According to the findings, the results indicated that LR, with its use of the Elastic Net parameter, outperformed the others with 96% accuracy, 97% precision, 94% recall, and 96% F1-score. Other models like XGBoost, LGB, and SVM also showed strong performance, whereas KNN had the poorest performance. The study highlights the importance of selecting the appropriate model for accurate mobile price prediction. Among all the machine learning used in this paper, the LR classifier outperforms the other state-of-the-art models because of the elastic Net parameter used for mobile phone price prediction.

Mobile phone price prediction: A comparative study among four models

As science and technology is advancing by leaps and bounds, mobile phones have become part and parcel of people's life. Because the different models of mobile phones which have different structural foundations, the prices of mobile phones are constantly fluctuating. Mobile phone prices forecasts are becoming more precise as artificial intelligence develops. This article compares various machine learning approaches, and the importance of the variables is ranked in order to determine the most accurate way to forecast the prices of mobile phones. The machine learning techniques used are linear regression (LR), random forest regressor (RFR), XGB Regressor and Support Vector Machine regressor (SVM). In order to determine which model predicts the most accurate mobile phone prices, R^2 evaluation is used. The XGB Regressor model had the greatest score (R-squared = 0.95) for prediction of mobile phone prices, compared to the other three models. In a word, with XGB Regressor methodology as a priority for future mobile phone price predicting, which can improve the accuracy of price predicting.

Intelligent Stock Price Fusion in Mobile Industries

In the tough cell phone business, guessing phone­ prices right is a key but hard job for new companies. Joining different types of info to look at stock prices may help, but we need strong ways to see how phone things and their costs tie together. This study wants to make stock price checking better in the cell phone busine­ss by using ways to join info. The work looks for strong ties between many phone things like memory, camera details, and screen size and how they affect the price. To fix this, very careful work was done to clean and fix the info. The Quadratic Discriminant Analysis rule­ was then used, along with top classifiers, for saying what will happen. Our findings demonstrate the QDA model's ability to detect subtle patterns and nonlinear correlations in the mobile phone data set. The model's resilience and predictive ability are demonstrated through visualizations such as ROC AUC and Precision-Recall curves. Comparative analyses with current approaches highlight the higher performance of the suggested data fusion approach. The use of QDA in data fusion models demonstrates its versatility in capturing complicated interactions, resulting in nuanced insights into mobile phone price factors. This study adds an improved prediction framework for mobile phone price analysis, which is critical for new enterprises looking to gain a competitive advantage in the volatile mobile industry.

Mobile Phone Price Range Prediction Based on Machine Learning Algorithms

Mobile phones with different price points on the market have different parameter configurations, which provide a lot of information related to the price. Mobile phone prices are also the focus of attention of the mobile phone market, manufacturers and consumers. This study used machine learning technology to investigate the issues in mobile phone sales, mainly the prediction of mobile phone price range and the feature contribution of the price range. The data resource includes the release price of mobile phones in the European market from 2018 to 2021. The data augmentation technologies are also applied in this study. Among the three established machine learning models, the accuracy rate of the training set of the Catboost model exceeds 80%, which is the model with the highest accuracy rate. The accuracy rate of Bagging Classifier is slightly lower than that of CatBoost, but the prediction result is the most stable. Such a result conforms to the characteristics of the algorithm itself. The conclusion of the analysis of feature contribution of numerical data to accuracy rate is that rear camera pixels, number of cameras and screen size have negligible impact on model prediction accuracy, which means feature contribution is minimal. The research results are reasonable and can provide references for merchants to set prices and for users to purchase mobile phones.

Mobile Phone Price Prediction with Feature Reduction

Feature reduction can reduce data dimensionality and streamline model size, which focuses on the high relevance data and inferences the output faster. This paper aims to explore the performance and effectiveness of feature reduction methods that accompany the Multilayer Perceptron classifier in predicting the mobile phone price range. Pearson’s Correlation and Principal Components Analysis are chosen as the feature reduction techniques in the research. The experiment sorts the features in significant order with two distinct methods. The three experimental groups reduce 5 features each time and the control group has no feature selection. Then all the groups use the open dataset to train and test the accuracy and loss through MLP. The result indicates that the feature selected by the correlation coefficient facilitates the accuracy of the classification model. When PCA is implemented and only a few features get reduced, the performance improves a little bit, but when more features are eliminated there are huge negative influences. Pearson’s correlation has a better performance than PCA in this experiment, which achieves 95.8% accuracy and validate the effectiveness of the feature reduction method.

Effects of WeChat use on the subjective health of older adults.

ObjectivesIn this study, the effect of WeChat use on the subjective health of older adults was examined.MethodsUsing 2018 China Health and Retirement Longitudinal Study (CHARLS) survey data, we attempted to rule out potential endogeneity bias by employing instrumental variable regression to estimate the effects of WeChat use on the health of older individuals. Mobile phone price was chosen as the instrumental variable, and the health of older adults was measured from two aspects: self-rated health and the Center for Epidemiological Studies of Depression Scale (CES-D10).ResultsWeChat use has a significant positive impact on the subjective health of older adults and can significantly improve their self-rated health and mental health status. WeChat use has significant positive effects on the subjective health of both older men and women, but relatively younger older adults obtain more health benefits from WeChat use.ConclusionThe virtual social participation prompted by WeChat use affects the health of older adults. This finding provides a reference for promoting the active integration of older adults into digital life.

Prediction of Mobile Phone Price Class using Supervised Machine Learning Techniques

Prediction of Mobile Phone Price Class using Supervised Machine Learning Techniques

The effects of review’s mobile phone price on consumers’ purchase intention: An event-related potential study.

The effects of review’s mobile phone price on consumers’ purchase intention: An event-related potential study.

Analyzing the Behavior and Financial Status of Soccer Fans from a Mobile Phone Network Perspective: Euro 2016, a Case Study

In this study, Call Detail Records (CDRs) covering Budapest for the month of June in 2016 were analyzed. During this observation period, the 2016 UEFA European Football Championship took place, which significantly affected the habit of the residents despite the fact that not a single match was played in the city. We evaluated the fans’ behavior in Budapest during and after the Hungarian matches and found that the mobile phone network activity reflected the football fans’ behavior, demonstrating the potential of the use of mobile phone network data in a social sensing system. The Call Detail Records were enriched with mobile phone properties and used to analyze the subscribers’ devices. Applying the device information (Type Allocation Code) obtained from the activity records, the Subscriber Identity Modules (SIM), which do not operate in cell phones, were omitted from mobility analyses, allowing us to focus on the behavior of people. Mobile phone price was proposed and evaluated as a socioeconomic indicator and the correlation between the phone price and the mobility customs was found. We also found that, besides the cell phone price, the subscriber age and subscription type also had effects on users’ mobility. On the other hand, these factors did not seem to affect their interest in football.

Mobile Phone Buying Decisions among Young Adults: An Empirical Study of Influencing Factors

Owing to the novelty and dynamism of mobile phone shopping behavior of adults within the context of contemporary social, technological, and market norms necessitated the need to study this phenomenon frequently. In the same league, there is a pressing need to empirically examine the mobile shopping behavior of young adults in Pakistan. This paper examines the factors influencing mobile phone shopping behavior within the context of young adults in Pakistan. First, a questionnaire-based survey consisting of a five-point Likert scale was conducted. A total of 416 respondents provided their complete responses. Then, we employed the structural equation modeling (SEM) model using AMOS 24 to empirically test the hypothesized model. The empirical results revealed that price and attractiveness positively influence mobile phone buying behavior among young adults in Pakistan. On the other hand, Service Encounter, Convenience, Avoidance of Core Service Failure, and Response have negative but statistically insignificant influences on mobile phone buying behavior in Pakistan. This finding revealed that the mobile shopping behavior of young Pakistani adults is predominantly influenced by the price of mobile phones and their attractiveness. Therefore, it is highly recommended that companies need to offer affordable mobile phone prices. Additionally, the attractiveness of the mobile phone needs to be maintained at competitive prices.

Mechanisms for mobile phone use in empowerment: A critical realist study of people with disabilities in Nigeria

AbstractInformation and Communication Technologies (ICTs) are gradually becoming important artifacts for people with disabilities (PWDs). This paper investigates the use of mobile phones in the daily activities of PWDs in sub‐Saharan Africa. The study argues that mobile phone use empowers PWDs, but little is known about what contextual factors and under what underlying mechanisms such empowerment occurs. By adopting Thapa and Omland's (2018) critical realism methodology, we aim to understand the underlying mechanisms, structures and conditions that shape empowerment through mobile phone use by PWDs in Nigeria. The Capability Approach (CA) was engaged as the theoretical framework. Semi‐structured interviews and focus group discussions with twenty‐four PWDs were used as the primary source of data. Our findings reveal that contextual factors such as personal, social and environmental factors enable/hinder empowerment for PWDs. Similarly, mechanisms such as accessibility and acceptability, technology knowledge, social media and mobile phone features are found to be the main underlying mechanisms triggering the events we observed. However, we argue that these mechanisms are enacted from other enabling structures such as the ubiquity of mobile phones, caregivers and contextual conditions like the personal innovativeness in mobile phone use and the low price of mobile phones. Our findings contribute to the critical realism methodology, policy and theory.

THE EFFECT OF PRODUCT PRICE AND PRODUCT QUALITY ON PURCHASING DECISIONS FOR SAMSUNG BRAND HANDPHONES AMONG STIE AAS SURAKARTA STUDENTS

This study aims to analyze the mobile phone price towards the decision to purchase Samsung mobile phone among STIE AAS Surakarta students and to analyze the quality of the Samsung mobile phone product against the decision to buy a Samsung mobile phone among STIE AAS Surakarta students. The populationS in this study were all consumers or users of Samsung mobile phones among STIE AAS students. The sample in this study was Samsung mobile phone users as many as 100 respondents. The results of this study indicate that the t test results are known to significantly influence the price variable purchasing decisions. The results of the F test together with the price variable (X1) and product quality (X2) have a joint effect on purchasing decisions. R2 analysis results obtained adjusted R square (R2) of 0.0627 means that variations in changes in purchasing decision variables can be explained by nutrient variables (X1) and product quality (X2) have a positive effect on purchasing decisions by 62.8%. While, the remaining Values Of 37.2% is explained by other variables outside the model.
 
 Keywords: Price, product quality, buying decision, mobile phone.

The Mysterious Cross-Country Dispersion in Mobile Phone Price Trends

Mobile phones have been central to ICT innovation since the introduction of the smartphone and constant-quality prices are a barometer of their economic impact. Official consumer price indices (CPIs) indicate that impact differs wildly across countries: For the 2008-2018 period, average annual rates of mobile phone inflation range from no change to a 25 percent decline among 12 key countries examined in this paper. Although evidence indicates certain fundamental factors are at play, mis-measurement may lead the spread in rates to be overstated. Examination of methods employed in CPI calculation, including quality adjustment and index formulas, illuminates but does not resolve the mystery.

The Mysterious Cross-Country Dispersion in Mobile Phone Price Trends

Mobile phones have been central to ICT innovation since the introduction of the smartphone and constant-quality prices are a barometer of their economic impact. Official consumer price indices (CPIs) indicate that impact differs wildly across countries: for the 2008–18 period, average annual rates of mobile phone inflation range from no change to a 25 per cent decline among 12 key countries examined in this paper. Although evidence indicates certain fundamental factors are at play, mis-measurement may lead the spread in rates to be overstated. Examination of methods employed in CPI calculation, including quality adjustment and index formulas, illuminates but does not resolve the mystery.

Female perception towards buying decisions of mobile phones

Indian Telecom industry is one of the fastest growing industries in the world. Irrespective of age or gender, the usage of mobile phones is continuously increasing at a rapid pace. Mobile nowadays, is not only a source of communication, but this is the mode of getting information and carrying out transactions. This paper explores and analyzes the motivational factors towards the perception of buying mobile phone devices among female consumers in Delhi-NCR, India. An attempt has been made to analyze the attributes such as price of mobile phones, Ads endorsed by celebrity, brand loyalty, change in looks/style over technology and family/friends recommendations which motivates the buying behavior in purchase of mobile phone. A well-structured five-point scale questionnaire has been prepared administered to 300 female respondents to obtain primary data. The study indicates that females in Delhi-NCR region mostly prefer recommendations of friend/family without any major financial constraints. The findings from the study may assist the mobile phone manufacturers to decide and utilize their marketing strategies for the promotion of their mobile phones among female consumers.

Getting Smart About Phones: New Price Indexes and the Allocation of Spending Between Devices and Services Plans in Personal Consumption Expenditures

This paper addresses two measurement issues for mobile phones. First, we develop a new mobile phone price index using hedonic quality-adjusted prices for smartphones and a matched-model index for feature phones. Our index falls at an average annual rate of 17 percent during 2010-2018, close to the rate of decline in the price index used in the GDP Accounts. Given relatively flat average prices over this period, our index points to substantial quality improvement. Second, we propose a methodology to disentangle purchases of phones and wireless services when they are bundled together as part of a long-term service contract. Getting the allocation right is especially important for real PCE because the price deflators for phones and wireless services exhibit very different trends. Our adjusted estimates suggest that real PCE spending currently captured in the category Cellular Phone Services increased 4 percentage points faster than is reflected in published data.

A Study on the Purchase Behavior of Buying Children Mobile Phones

This study is aimed to explore what factors may affect parents when they want to buy mobile phones for children, and discuss the motivation of parents on buying mobile phones for children. After understanding the purchase factors and the purchase motivation of parents, this study can attempt to know further about parents’ purchasing behavior and their purchase decision-making. The researcher of this study used in-depth interviews to interview with forty respondents which contained ten pairs of parents and children who already have a mobile phone and ten pairs of parents and children who do not have a mobile phone now. Based on the results of interviews, the primary purchase factor is the price of mobile phones. Parents think that the price of a mobile phone has to be within reasonable limits. The secondary purchase factor is the function of mobile phones. Parents hope that the functions for children to use can be as simple as possible. Besides, parents also hope that there can be some limit and learning functions for children to use. The main motivation of parents on buying mobile phones for children is the convenience of contact. Parents think that when children really have the need to use a mobile phone, they will consider buying one for children.

Investigating the Factors That Determine Tertiary Students Brand Choice of Mobile Phones in Northern Ghana

This paper investigated the factors affecting students brand choice for mobile phones among university students at the University for Development Studies, Navrongo Campus in the Upper East region of Ghana. The research was a cross-sectional design with 300 students. The students were chosen using stratified random sampling. Statistical Package for Social Sciences version 20 was used to analyze the data. The results show that three major factors directly influencing students choice of brand of mobile phones in this study were user-friendliness, quality and price. Among all the mobile phone brands, the most used brands by the students were Nokia and Samsung. Nokia and Samsung together constituted 56 percent of phones used by the respondents. However, for future use, 45 percent are planning to use Nokia while 61 percent for Samsung. The price of mobile phones ranged between GH₵ 50.00 and GH₵ 301.00. Fashion and durability were factors that correlated and have relationships with the decision to buy a brand of mobile phone. This is one of the few studies in consumer behavior to assess preferences in a consumption context of mobile phone industry. The findings are useful for mobile phone dealers and manufacturers to incorporate these preferences by diversifying the types of mobile phones with different ranges of corresponding prices to meet consumers’ needs and income levels.

Consumers’ Repurchase Intention Towards a Mobile Phone Brand in Qatar: An Exploratory Study Utilizing Theory of Reasoned Action Framework

The aim of this study is to analyze the influencing factors on consumers’ repurchase intention towards a brand of a mobile phone. The theory of reasoned action has been used in this study to measure consumer repurchase intention. This theory considers consumer attitude towards action and subjective norms as influencing factors on consumer purchase intention. In this study, past behaviour is also added to the model to measure consumers’ repurchase intention. The exploratory study was conducted in the State of Qatar and the results showed that peoples’ attitudes toward action, subjective norms and past behaviour tend to have a significant influence on their repurchase intention. However, the analysis reveals that there is a negative relationship between consumers’ repurchase intention and mobile phone price, size and consumers’ past behaviour. Results of this study can be used by the marketing managers to devise strategies to attract consumers by understanding the factors that affect repurchase intentions.

Hedonic Price Analysis on Mobile Phone Market

Using collected 190 samples, this paper studied Chinese mobile phone market with hedonic price analysis. Through calculating hedonic price of heterogeneous goods, this paper summed up the positive and negative factors that influence the price of mobile phones. This paper found that network type and GPS are negative effects on mobile phones’ prices, which is little different from the real life situation; And the price will become more and more cheaper with time; In addition, the price of mobile phones with additional features is correspondingly higher.