Effect of Smartphone Choice, Customer Satisfaction and Reason to Change Smartphone on Smartphone Repurchase
Bibliographic record
Abstract
Advances in Smartphone technology make the growth of Smartphone users among the people more rapidly. The high growth of Smartphone users makes Smartphone manufacturers compete with each other in understanding market needs. Every competing company actually has the same goal, namely how to make the products that are made can be well received by consumers. In accordance with sales data of smart cellphones (Smartphones) that have been released by International Data Corporation (IDC) from the first quarter of 2017 to the third quarter (Q3) - 2018 experienced sales fluctuations. Smartphone sales in 2018 fell by 5.9%. Similarly, the number of smartphone shipments in Indonesia in the third quarter (Q3) -2018 reached 8.6 million units, up 18% annually. But from quarter to quarter it decreased by 9%. Within 1-2 years Indonesian people like to switch smartphones, more than 56% of respondents replace their smartphones with new ones. This is consistent with data from the MARS research institute conducting a survey of 290 respondents in the Jakarta, Bogor, Depok, Tangerang and Bekasi areas. Within a period of more than 2 years, there were 20.6% of Smartphone users making smartphone replacements. The fastest duration that users do is within 3 months, done as much as 2, 4%. Every new smartphone launched, the interest of the people of Indonesia will certainly remain high. Consumer behavior when buying any product including smartphones not only concerns his own behavior, but a combination of the behavior of others who help or support the purchasing process that can work as an initiator, influencer, and decision maker and the level of involvement of all these people may differ in each purchase. There are many variables that influence consumer behavior including, age, sex, personal motivation, needs, attitudes and values, personality characteristics, socio-economic and cultural background, professional status to social influences such as family, friends, colleagues and society as a whole. This research was conducted to determine the effect of Smartphone choice, Customer Satisfaction, Reason To Change Smartphone on Smartphone Repurchase. The methodology of this research is to collect data in the form of questionnaires distributed online to respondents who are consumers of Smartphone users, the method used in this study is a quantitative method by collecting a sample of approximately 400 respondents. Based on the results of data analysis found that variables consisting of Smartphone choice, Customer Satisfaction and Reason To Change Smartphone and a positive and significant effect on Repurchase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".