The usage of smartphones instead of simple mobile phones increases sharply in our era, especially among young people, because they do multiple tasks with single equipment. This study mainly focuses on smartphone satisfaction by combining hand measurements, smartphone users’ survey results, and hand dexterity levels of corresponding users acquired from Minnesota Manual Dexterity Test (MMDT). Structural Equation Modelling (SEM) is used as a statistical tool to discover the potential direct and indirect relations among user satisfaction, hand dimensions, and dexterity scores. Results indicates that thumb length, hand length, and dexterity level of the users have notable effects on users’ satisfaction with smartphones. Based on the results, a new approach that includes both gross motor skills and physical measurements is suggested to see hidden indirect relations with satisfaction.
The increasing need for fast communication brings along the widespread use of the latest communication technologies. New forms of communication become mobile and they tend to coalesce into a single unit which is called smartphone. Numerous brands offer various smartphone models that have different technical features, physical designs, screen types, input devices, and so forth. People prefer different models of smartphones depending on their needs but it is quite hard to anticipate how they will be pleased with their smartphone. Smartphones have various features that are used in daily life such as standard phone calls, video phone calls, various instant messaging systems, advanced audio video recording technology, rapid Internet access, and various other features. Therefore, smartphone users have different expectations and purposes of using their devices. Their expectations and purposes of using affect their habits of the usage and the level of satisfaction. In the literature, there are numerous studies that have been conducted to measure user satisfaction and uncover information about the use of these devices.
Balakrishnan and Yeow [
Zulkefly and Baharudin [
Physical design of smartphones is also a crucial point for user satisfaction. Since smartphones may require use of two hands and different fingers depending on the activity, smartphone sizes, screen, and keyboard sizes have a critical importance for the ease of usage. Many researchers conduct studies about physical design and mobile phone sizes and hand anthropometrics.
Jain and Pathmanathan [
According to most of the related studies’ results, usability of devices and anthropometric features of the users are considered as critical points for design. These are supporting points to clarify the relationship between user expectations and device attributes. However, previous studies do not take into account the human capabilities that may affect overall user satisfaction on mobile devices. Essentially, it is very crucial to know approximate manual dexterity level of the target market for designing more appropriate devices. Although user capability is considered as a component of user satisfaction in some recent studies, there is still a gap in terms of considering motor skills of the users. This study fills this gap through using Minnesota Manual Dexterity Test (MMDT) as a part of the user satisfaction research.
Manual dexterity is a measurable characteristic and it is one of the indicators of human capabilities. Some tests are available to determine dexterity of one or two hands, but not both hands. Since smartphones may require use of both hands and several fingers, it is important to consider effect of user’s manual dexterity on their satisfaction of use. Since using the smartphone is not totally the same as using conventional mobile phones, it often requires use of both hands and fingers besides thumbs. On the other hand, smartphones offer much more features related with screen size and keyboard; because of that, their dimensions are bigger. At this point, manual dexterity level of users and choosing the most appropriate smartphone model must be emphasized in terms of user satisfaction. People should decide their smartphone model considering their aims of use, hand dimensions, and manual dexterity level. This study tries to emphasize the relationships between these three aspects and satisfaction level of smartphone users.
In this study, dexterity level is considered as an indirect effect on the satisfaction, besides hand anthropometric dimensions that directly (or naturally) affect dexterity level. It is possible to use both direct and indirect effects with the help of Structural Equation Modelling (SEM) as an extension of Path Analysis (PA) [
The paper is structured as follows. Section
A multistage measurement process is designed to collect the data. The study is conducted with 36 participants. Firstly, each participant is asked to answer the questionnaire that includes questions about demographics, smartphone choices, habits, and satisfaction. Secondly, hand and finger dimensions of participants are measured. Finally, each participant performs the Minnesota Manual Dexterity Test.
All hand and finger dimensions are measured for both right and left hands. However, not all of them are used in analysis part because there are high correlations between some of these measurements. Lafayette anthropometric tapes and small anthropometer are used for measuring the hand dimensions such as hand length, hand breadth, palm length, index finger length, index finger breadth, thumb length, and thumb breadth. Hand and finger dimensions (mm) are presented in Table
Hand and finger dimensions (mm).
Subject |
Right hand |
Left hand |
Right hand |
Left hand |
Right palm |
Left palm |
Right index |
Left index |
Right index |
Left index |
Right thumb |
Left thumb |
Right thumb |
Left thumb |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 174 | 172 | 74 | 73 | 91 | 91 | 70 | 70 | 15 | 15 | 50 | 50 | 18 | 19 |
2 | 182 | 182 | 83 | 81 | 99 | 101 | 75 | 75 | 15 | 16 | 57 | 57 | 19 | 19 |
3 | 183 | 186 | 84 | 80 | 103 | 102 | 70 | 71 | 17 | 17 | 65 | 65 | 21 | 21 |
4 | 180 | 182 | 94 | 93 | 94 | 94 | 74 | 74 | 18 | 18 | 55 | 55 | 23 | 23 |
5 | 189 | 190 | 84 | 85 | 108 | 108 | 72 | 73 | 16 | 16 | 59 | 67 | 21 | 21 |
6 | 166 | 166 | 76 | 75 | 93 | 93 | 67 | 68 | 16 | 15 | 58 | 58 | 20 | 20 |
7 | 186 | 187 | 82 | 82 | 104 | 105 | 74 | 73 | 18 | 17 | 67 | 64 | 23 | 22 |
8 | 175 | 175 | 73 | 71 | 99 | 99 | 70 | 69 | 15 | 15 | 64 | 61 | 18 | 18 |
9 | 177 | 177 | 77 | 78 | 99 | 100 | 71 | 71 | 16 | 16 | 66 | 65 | 20 | 20 |
10 | 168 | 169 | 80 | 80 | 95 | 95 | 66 | 66 | 17 | 17 | 58 | 57 | 21 | 21 |
11 | 174 | 174 | 79 | 80 | 99 | 99 | 65 | 65 | 15 | 15 | 65 | 64 | 20 | 20 |
12 | 174 | 174 | 78 | 78 | 97 | 97 | 71 | 68 | 16 | 16 | 61 | 61 | 21 | 20 |
13 | 196 | 197 | 87 | 86 | 113 | 112 | 76 | 77 | 18 | 17 | 67 | 68 | 23 | 22 |
14 | 181 | 181 | 85 | 85 | 102 | 103 | 70 | 72 | 16 | 17 | 66 | 66 | 21 | 21 |
15 | 187 | 186 | 89 | 88 | 107 | 107 | 75 | 75 | 17 | 17 | 66 | 65 | 21 | 22 |
16 | 175 | 176 | 82 | 82 | 99 | 100 | 69 | 69 | 16 | 16 | 65 | 65 | 22 | 21 |
17 | 180 | 181 | 83 | 83 | 101 | 100 | 73 | 73 | 17 | 17 | 64 | 65 | 22 | 22 |
18 | 172 | 172 | 75 | 73 | 92 | 92 | 72 | 73 | 15 | 14 | 62 | 61 | 18 | 18 |
19 | 166 | 165 | 73 | 73 | 91 | 91 | 67 | 66 | 14 | 13 | 59 | 59 | 18 | 17 |
20 | 198 | 197 | 96 | 96 | 110 | 110 | 80 | 79 | 18 | 17 | 70 | 70 | 22 | 22 |
21 | 181 | 181 | 95 | 94 | 93 | 93 | 76 | 77 | 18 | 18 | 60 | 60 | 22 | 22 |
22 | 173 | 173 | 80 | 81 | 98 | 98 | 66 | 66 | 16 | 16 | 66 | 65 | 21 | 21 |
23 | 177 | 179 | 84 | 83 | 96 | 97 | 67 | 68 | 15 | 15 | 65 | 64 | 21 | 20 |
24 | 189 | 189 | 82 | 81 | 104 | 104 | 74 | 73 | 15 | 15 | 66 | 66 | 19 | 19 |
25 | 166 | 165 | 73 | 72 | 94 | 94 | 63 | 63 | 14 | 14 | 62 | 62 | 17 | 17 |
26 | 175 | 175 | 79 | 78 | 102 | 102 | 67 | 68 | 15 | 15 | 65 | 65 | 20 | 20 |
27 | 185 | 184 | 83 | 85 | 102 | 102 | 77 | 76 | 15 | 15 | 62 | 63 | 20 | 19 |
28 | 187 | 187 | 82 | 82 | 105 | 105 | 71 | 72 | 16 | 16 | 65 | 64 | 20 | 20 |
29 | 173 | 172 | 74 | 74 | 93 | 93 | 73 | 73 | 16 | 15 | 61 | 62 | 19 | 19 |
30 | 169 | 168 | 75 | 74 | 92 | 92 | 70 | 71 | 16 | 16 | 62 | 63 | 19 | 19 |
31 | 188 | 188 | 83 | 84 | 106 | 106 | 73 | 72 | 17 | 17 | 65 | 66 | 22 | 22 |
32 | 185 | 185 | 80 | 80 | 103 | 104 | 70 | 71 | 17 | 17 | 64 | 65 | 22 | 23 |
33 | 189 | 188 | 84 | 84 | 109 | 109 | 74 | 74 | 18 | 18 | 68 | 69 | 24 | 23 |
34 | 172 | 172 | 74 | 73 | 96 | 96 | 68 | 68 | 16 | 16 | 64 | 63 | 19 | 19 |
35 | 171 | 172 | 73 | 73 | 92 | 92 | 73 | 73 | 15 | 15 | 63 | 63 | 17 | 17 |
36 | 178 | 178 | 85 | 85 | 97 | 96 | 69 | 68 | 16 | 16 | 66 | 66 | 22 | 21 |
Minnesota Manual Dexterity (MMD) Test includes several test methods. Two of them used in this study are the placing test performed by single hand and the turning test performed by two hands. Dexterity scores are determined based on task completion duration [
Before starting the MMD test, each participant is informed about the tasks of the test and they are allowed to get familiar with the test equipment. After they performed both placing and turning tests, completion time of each task is recorded. Dexterity scores are provided in Table
Minnesota Manual Dexterity Test results.
Subject |
(1) Placing |
(2) Placing |
Placing total |
Placing percentile |
(1) Turning test |
(2) Turning |
Turning |
Turning percentile |
---|---|---|---|---|---|---|---|---|
1 | 67 | 63 | 130 | 25 | 67 | 54 | 121 | 1 |
2 | 60 | 58 | 118 | 60 | 52 | 48 | 100 | 40 |
3 | 57 | 49 | 106 | 95 | 40 | 35 | 75 | 99 |
4 | 68 | 61 | 129 | 25 | 50 | 49 | 99 | 50 |
5 | 54 | 54 | 108 | 90 | 55 | 48 | 103 | 31 |
6 | 63 | 56 | 119 | 60 | 61 | 45 | 106 | 20 |
7 | 58 | 54 | 112 | 85 | 49 | 45 | 94 | 69 |
8 | 65 | 55 | 120 | 60 | 56 | 44 | 100 | 40 |
9 | 66 | 54 | 120 | 60 | 58 | 44 | 102 | 31 |
10 | 63 | 58 | 121 | 60 | 64 | 51 | 115 | 3 |
11 | 66 | 61 | 127 | 31 | 60 | 58 | 118 | 2 |
12 | 58 | 59 | 117 | 69 | 63 | 50 | 113 | 3 |
13 | 69 | 66 | 135 | 10 | 53 | 50 | 103 | 31 |
14 | 67 | 61 | 128 | 31 | 52 | 48 | 100 | 40 |
15 | 60 | 58 | 118 | 60 | 56 | 55 | 111 | 10 |
16 | 59 | 56 | 115 | 80 | 48 | 45 | 93 | 75 |
17 | 60 | 56 | 116 | 75 | 49 | 48 | 97 | 60 |
18 | 69 | 63 | 132 | 20 | 47 | 46 | 93 | 75 |
19 | 64 | 58 | 122 | 50 | 52 | 46 | 98 | 50 |
20 | 56 | 49 | 105 | 95 | 55 | 52 | 107 | 20 |
21 | 63 | 60 | 123 | 50 | 58 | 56 | 114 | 5 |
22 | 67 | 65 | 132 | 20 | 57 | 52 | 109 | 15 |
23 | 69 | 62 | 131 | 20 | 40 | 41 | 81 | 97 |
24 | 57 | 53 | 110 | 90 | 55 | 50 | 105 | 25 |
25 | 67 | 70 | 137 | 5 | 49 | 46 | 95 | 60 |
26 | 61 | 56 | 117 | 69 | 59 | 51 | 110 | 10 |
27 | 64 | 60 | 124 | 40 | 58 | 53 | 111 | 10 |
28 | 66 | 64 | 130 | 25 | 63 | 48 | 111 | 10 |
29 | 60 | 53 | 113 | 85 | 51 | 46 | 97 | 60 |
30 | 68 | 64 | 132 | 20 | 51 | 50 | 101 | 40 |
31 | 63 | 60 | 123 | 50 | 57 | 50 | 107 | 20 |
32 | 70 | 62 | 132 | 20 | 54 | 50 | 104 | 31 |
33 | 60 | 55 | 115 | 80 | 49 | 48 | 97 | 60 |
34 | 71 | 65 | 136 | 10 | 58 | 47 | 105 | 25 |
35 | 60 | 57 | 117 | 69 | 60 | 54 | 114 | 5 |
36 | 70 | 60 | 130 | 25 | 53 | 46 | 99 | 50 |
1–5 Likert scale is used to measure satisfaction level of participants with their smartphones. Additionally, demographic information, the reasons for choosing their smartphones, daily usage preferences, usage habits, and satisfaction questions (Table
Satisfaction questions and rotated component matrix (rotation converged in 3 iterations).
Question codes | Question explanations | Component | |
---|---|---|---|
Physical | General | ||
M15 |
|
.628 | |
M16 |
|
.549 | |
M17 |
|
.630 | |
M18 |
|
.707 | |
M19 |
|
.707 | |
M20 |
|
.637 | |
M21 |
|
.913 | |
M23 |
|
.857 | |
M24 |
|
.937 | |
M25 |
|
.888 | |
M26 |
|
.691 |
Extraction method: principal component analysis.
Rotation method: Varimax with Kaiser normalization.
The survey is conducted with 36 participants, the average age is 23 ranging between 19 and 34, and half of the participants are female. The main characteristics of the data are as follows: the average monthly income is 1007 (±796) TL (Turkish Lira), while the average family income is 4000 (±2153) TL. 83% of them are right-handed. The smartphones are used mostly for phone calls and instant messaging programs such as
The objectives of smartphone usage and the satisfaction questions are asked with 5 Likert points with
Data collection part also includes hand/finger measurements and Minnesota Manual Dexterity Test results mentioned in Sections
By using
In the analysis part, it is aimed at keeping the number of variables limited, because of the sample size constraint. Additionally, since variables are highly correlated, two indicators for each factor are preferred. One is the physical satisfaction (
Path Analysis (PA), introduced by Wright [
It is widely known that regression and correlation analysis are used to show relations between variables, but they are not quite enough to explain direct and indirect effects together. Pedhazur [
Equations in Structural Equation Modelling (SEM) are known to be
Path diagram and path coefficients are two main tools for PA and also for SEM. Path diagram is the visual representation of the total effects of explanatory variables, and it consists of observed variables (rectangles) and latent variables (circles) connected by single-headed and double-headed arrows. It is mandatory to use double-headed arrows between exogenous variables which are assumed to have the variance explained by causes outside of the model. Conversely, endogenous variables’ variances are assumed to be explained by exogenous variables and other endogenous variables.
Figure
PA diagram example.
Figure
SEM diagram example.
Model definition equations are main tools of SEM to see the relationship between observed and unobserved variables. Following Figure
Before finalizing the model setting, a set of plausible models are tried. Maruyama’s quote [
In this study, it is not so easy to increase the sample size because of the expense of data collection process. Additionally, since there are high correlations between satisfaction indicators, two main indicators for satisfaction for both latent variables are used.
According to satisfaction indicators and turning/placing percentile scores, four different models are implemented (Table
Model comparisons.
Main models | Model 1 | Model 2 | Model 3 | Model 4 | ||||
---|---|---|---|---|---|---|---|---|
Model versions | A ( |
B | A ( |
B | A (+) | B | A (+) | B |
Chi-square | 8.937 | 0.5 | 8.937 | 0.172 | 41.578 | 5.716 | 41.578 | 2.903 |
Degrees of freedom | 3 | 2 | 3 | 2 | 3 | 2 | 3 | 2 |
Probability level | 0.03 | 0.779 | 0.3 | 0.918 | 0 | 0.057 | 0 | 0.234 |
RMSEA | 0.238 | 0 | 0.238 | 0 | 0.606 | 0.230 | 0.606 | 0.114 |
AIC | 22.937 | 26.5 | 22.937 | 26.172 | 55.578 | 31.716 | 55.578 | 28.903 |
A: without mediator and correlations of errors.
B: default model (with mediator and correlations of errors).
(
The first and the base model (Model 1-A, Table
Another absolute fit statistic is root mean square error of approximation (RMSEA) which is sensitive to the estimated number of parameters in the model [
One of the main benefits of SEM is that it allows researchers to work with indirect effects of variables in addition to direct variables. These indirect effects show the effects between two variables that are mediated by one or more intervening variables (mediators) [
It is noteworthy that Chi-square values and RMSEA get smaller values for all models with mediators. Additionally, SEM with mediators provides better results for general satisfaction indicator which is considered in Model 3 and Model 4. It is observed that, including a mediator to the models with
The SEM diagrams of default version of four models (Model 1-B, Model 2-B, Model 3-B, and Model 4-B) with mediators and their comparisons are detailed in this section. The first theoretical model (Figure
SEM diagram of Model 1-B.
SEM diagram of Model 2-B.
SEM diagram of Model 3-B.
SEM diagram of Model 4-B.
In Figures
Smartphones are one of the most popular devices that are being used in daily life and requiring intensive human-machine interaction. Most of the consumers make their smartphone choices based on a few criteria. Usually, some technical features of smartphones and attractive appearance are being considered. This study attracts attention to the relationship between human attributes and satisfaction level of smartphones. Besides anthropometric dimensions, dexterity levels of participants are taken into account as a new approach and possible effects on smartphone satisfaction are inspected. Since motor skills of users are considered for the first time in this study, relevant findings provide a new viewpoint for smartphone satisfaction studies.
One of the main aims of this study is to investigate the satisfaction factors while selecting and using smartphones. For that purpose, first, hand anthropometric measurements of the participants were collected and then a multistage experimental study was conducted. Minnesota Manual Dexterity Test was utilized to make participants perform one-hand and two-hand dexterity tasks. A survey was conducted to assess satisfaction measurements. Based on survey results, it was possible to group the response under two main factors (physical satisfaction and general satisfaction). The factors in concern were hand anthropometric measurements and manual dexterity levels. Therefore, effects of these factors were investigated through detailed statistical analyses.
Another aim of this study is to assess the effects of placing percentile scale and turning percentile scale as mediators. Therefore, SEM is used as a statistical tool that is an extension of multiple regression models. After finalizing data collection, several models were implemented with two different types of indicators of satisfaction, which were determined with factor analysis, for different types of dexterity test results. Measured variables were selected as exogenous variables, while two variables were selected to generate latent variables for two different satisfaction indicators. Even though the direct effects of hand and thumb lengths were smaller on general satisfaction than physical satisfaction, it was possible to obtain the indirect effects of these lengths on general satisfaction with the help of SEM.
SEM indicates that standardized regression coefficients (Figures
As a conclusion, it can be stated that dexterity level and hand anthropometrics of the users affect smartphone satisfaction either directly or latently. Analyses results indicate a critical importance of considering user attributes regarding dexterity and hand anthropometrics for both designers and consumers in the smartphone market.
Creating a more personal preference profile for users is an ongoing and natural extension of this study. Finding scientific evidence related to user-based factors for smartphone preferences would assist designers to invest more user friendly devices for their clients, especially in our era in which smartphones are tremendously popular and the sector of them are competitive indeed for designers and manufacturers. Future studies may consider different age groups. Further, other mobile devices used in daily life that require human-machine interaction may be studied. On the other hand, it is being planned developing a smartphone application to measure dexterity levels of potential users which will be applied prior to buying decision to help estimate prospective satisfaction level of them on the relevant smartphone.
The authors declare that there is no conflict of interests regarding the publication of this paper.