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Dr. Biswas received his Ph.D. from POSTECH, South Korea.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"194151",title:"Dr.",name:"Abhijit",middleName:null,surname:"Biswas",slug:"abhijit-biswas",fullName:"Abhijit Biswas",profilePictureURL:"https://mts.intechopen.com/storage/users/194151/images/system/194151.png",biography:"Dr. Abhijit Biswas is a research associate at the Indian Institute of Science Education and Research (IISER) Pune, in India. His research goal is to design and synthesize highest quality epitaxial heterostructures and superlattices, to play with their internal degrees of freedom to exploit the structure–property relationships, in order to find the next-generation multi-functional materials, in view of applications and of fundamental interest. 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1. Introduction
Localization of positions and detection of objects is a key aspect in today’s applications and, although the topic exists a while ago, it is still under ongoing research. The introduction of global navigation satellite systems (GNSS), particularly GPS [1], and its improvements with accuracies down to a few meters, was a huge step towards ubiquitous localization [2, 3]. This is almost valid for outdoor environments, whereas indoor localization is still a challenging issue [4, 5]. The reason for that is the demanding, dynamic indoor environment, causing severe multipath fading, leading to hard predictable propagation models - thus influencing time, power and phase measurements. However, in the past, much effort has been put into designing high accurate indoor localization systems, including technologies like ultrasonic sound, infrared light, Wi-Fi, Bluetooth, ZigBee, cellular mobile communication (GSM, UMTS), ultra-wideband and RFID just to mention a few of them. Despite all the effort, there is no outstanding technology comprising all indoor localization contingencies as every technology in use has its advantages and disadvantages regarding accuracy, availability, complexity and costs.
Due to constantly falling prices of UHF RFID tags [6] additional applications arose beside the traditional concept of radio frequency identification (RFID). Major applications include supply chain technologies [7] and logistics [8], from container level tagging even down to item level tagging [9]. Regarding the Internet of Things [10], UHF RFID has some advantages over other RFID technologies, i.e., LF and HF: UHF RFID tags are small, do not require a battery, allow high data rates and high reading ranges, whereas LF and HF cannot serve with these issues at the same time [10]. Together with the mentioned low costs, the UHF RFID technology may be available in lots of objects (walls, carpets, doors, etc.) in the future. Therefore, indoor positioning using UHF RFID technology could be one solution towards ubiquitous localization, as efforts are made to shrink the size of RFID reader ICs and to integrate them into mobile phones.
The chapter is organized as follows. Section 2 gives a brief overview of today’s wireless positioning technologies with a focus on RFID. Section 3 introduces the proposed positioning system and shows the theoretical approach along with an example. Section 4 focuses on challenges and limitations of the system and Section 5 presents results from measurements carried out underlining the principle of operation. Section 6 provides a discussion based on the results. Finally, Section 7 gives a short summary and concludes with a perspective for future work.
2. Basics and state of the art
This section provides an overview of state-of-the-art wireless positioning technologies. The section is divided into two subsections, with the first subsection describing measurement principles for positioning, whereas the second subsection has a focus on current positioning technologies based on RFID, particularly UHF RFID within the 900 MHz frequency band.
2.1. Positioning measurement principles
The first paragraph provides definitions for the terms precision, rightness and accuracy, whereas the following paragraphs describe the main positioning processes comprising lateration, angulation and fingerprinting. The last paragraph depicts the measurement techniques used for the positioning process, for instance, time of arrival, angle of arrival and received signal strength.
2.1.1. Precision, rightness and accuracy
Often, the terms “precision” and “accuracy” are used to define the same issue, namely how well a localization system or method works, e.g., the measurement error expressed in meters. However, precision and accuracy are not similar to each other. Therefore, this paragraph points out the differences and relations of the terms precision, rightness and accuracy.
Precision shows how well independent measurement values are located to each other. That means, if many measurement values are in dense proximity to each other, the measurement has a high precision; on the other hand, it does not mean that the measurement is accurate in any case. A standard term that is used to measure the precision is the standard deviation σx with
σ^x describes the estimated standard deviation of the measurement, N describes the number of measurements, x^k the measurement value at the kth measurement, the estimated mean value of the measurement values. x^ describes the random variable of the measurement process, whereas E∙ is the corresponding expectation value. In the following, the standard deviation σx is used as a measure for the precision of a positioning technique.
Rightness or trueness describes how well the measured values respectively the expectation of the estimated values x^ fit to the expectation of the true values x, i.e., a so called bias with
Bias=E{x^−x}=E{x^}−xand Bias^=x^¯−x¯E3
Bias^ is the estimated rightness of the measurement and x¯ is the mean value of the true values. The rightness is a measure for the average discrepancy between a measured and a reference value and may be described as bias or offset.
Accuracy takes both, the precision and the rightness, into account. In fact, only high accuracy may be achieved if precision and rightness is high, too. A well known definition of the accuracy is the root mean square error RMSE, which is defined as
RMSE=MSE=Ex^-x2 and RMSE^=1N∑k=1Nx^k-xk2E4
RMSE^ describes the estimated RMSE of the measurement and xk the true value at the the kth measurement.
According to [11] the first expression in Equation (4) can be transformed into
RMSE=σx2+Bias2E5
Equation (5) shows that a distorted measurement with a high precision may be more accurate than an undistorted measurement with a low precision respectively standard deviation.
Figure 1.
Example of trilateration with RFID reference tags
2.1.2. Lateration
Lateration is used to determine the position using distances to known reference points. For instance, an RFID reader may localize itself by evaluating distances to certain reference points, e.g., RFID tags, using the principle of trilateration, as shown in Figure 1. In this figure, two-dimensional (2D) positioning of P, an RFID reader, can be realized using three reference points, here reference tags. Assuming the reader is able to exactly determine its distance di∀i∈1,2,3 to each of the tags, a circle is drawn around each tag with radius equal to the measured distance di. The intercept point of the three circles with radii d1…d3 indicates the position of the reader P. If the positions of the reference tags are known, the reader may determine its position by solving the set of equations
xP-xi2+yP-yi2=di,i∈1,2,3.E6
xP;yP is the position of the reader, which shall be estimated and xi;yi∀i∈1,2,3 is the position of each of the reference points respectively tags. Solving the set of equations in (6) for three reference points yields [12, 13]:
xPyP=a1,2b1,2a1,3b1,3-1g1,2g1,3,E7
with
a1,i=2xi-x1,i∈2,3E8
b1,i=2yi-y1,i∈2,3E9
and
g1,i=d12-di2-x12+y12-xi2+yi2,i∈2,3.E10
In the case of three-dimensional (3D) positioning, a minimum of four reference points is necessary to unambiguously determine the exact position. However, due to the imperfectness of the distance measurement (noise, fading channel, etc.), there is usually no exact interception point, but rather an intersection area. Therefore, different error-minimizing algorithms can be used to make a best estimate for the position determination [14]. The accuracy of the measurements can be further increased by making use of more than the necessary minimum of reference points [15].
In RFID, generally, there exists clock synchronization between transmitter and receiver, as both components are located within the RFID reader. If, however, there is no clock synchronization between transmitter and receiver, the clock offset τoffset will lead to a constant distance error doffset within each range measurement. This additional parameter can be solved by adding one more equation (equal to one additional tag) to the minimum number of equations when there is no synchronization error:
xP-xi2+yP-yi2+doffset=di∀i∈1,2,3,4E11
As mentioned before, there should be no time offset in RFID systems. Nevertheless, constant phase shifts due to the non-constant reflection coefficient of RFID tags [16] can lead to an additional offset distance doffset, having the same effect as a time-based clock offset. The set of equations in (11) describe hyperbolas rather than circles around the reference points. Figure 2 shows the effect of an offset distance doffset and two out of four hyperbolic curves, which would intercept in position P.
Figure 2.
Example of hyperbolic lateration using RFID tags as reference points
2.1.3. Angulation
The principle of angulation rests upon the relations between angles and distances within a triangle; therefore, it is mostly common under the term triangulation. If two angles and one side of a triangle are known the remaining distances respectively the position to be determined can be calculated using the law of sines and the angle sum of a triangle. Figure 3 shows the principle used: Two antennas (Ant. #1 and Ant. #2) of an RFID reader are deployed to calculate the position of the RFID tag. This can be realized using, for instance, phase-based or direction-defined measurements. From independent angle measurements one obtains the angles α and β; the distance d0 is known in advance. Subsequently, the remaining angle γ is calculated (angle sum in triangle) and from that the missing two distances d1 and d2 from the antennas to the RFID tag (law of sines). Angulation may be used in 2D or 3D localization problems.
Figure 3.
Triangulation example using two antennas to determine the position of an RFID tag
2.1.4. Scene-based localization / fingerprinting
Scene-based localization is divided in two sequential processes, a calibration process and an operational process. The calibration process records any environmental values (optical, electrical, physical, etc.), also known as fingerprints, at several positions within a scene and stores the data in a database [17, 18]. The following operational process is thus able to determine the position by measuring the current environmental values and comparing them with the values in the database. Special algorithms estimate the position by finding the position with the minimal error [19]. Figure 4 shows a room map with different WLAN base stations showing the electrical field strength at different locations [20] used along with WLAN positioning.
Figure 4.
Electrical field strength distribution within a building to be used for WLAN positioning [20]
2.1.5. Positioning measurement techniques
After highlighting the measurement principles, this paragraph gives a brief overview over the common technologies used. Distance measurements may be based on measuring the time of flight, the signal strength and the phase between transmitted and received signal.
Time measurements include Time of Arrival (ToA) and Time Difference of Arrival (TDoA) measurements.
ToA measurements directly determine the distance by using the time of flight tToA of the signal. Multiplied with the corresponding propagation speed c, the speed of light in case of electromagnetic waves, this results directly in the distance dToA between transmitter and receiver as described in Equation (12). ToA measurements can be used directly along with trilateration methods.
dToA=tToA⋅cE12
\n\t\t\t\t\t
TDoA measurements determine the time difference of a signal received at known reference points rather than measuring directly the time between transmitter and receiver. This means, that the time stamp of the signal transmitted via the object to be localized is unknown, but the time differences at the synchronized receivers are determined. In contrast to ToA, TDoA does not require any synchronization between transmitter and receiver. The reference stations must be synchronized, indeed. One positioning method using TDoA measurements is hyperbolic lateration (see Paragraph 2.1.2).
RSS (Received Signal Strength) measurements are based on the received signal strength at the receiver. Hence, there are two possible candidates to process RSS-based data. The first one is based on the propagation conditions, usually including a modified and enhanced form of Friis transmission equation
Pr=Pt+Gt+Gr+20logλ4πddB,E13
e.g., the log-distance path loss model
PLd=PLd0+10αlogdd0+XdB.E14
Equation (13) describes the free space attenuation formula depending on the distance d and wavelength λ with receiving power Pr, transmitting power Pt, receiving and transmitting antenna gains Gt and Gr together with the free space attenuation λ4πd2 in dB. Equation (14) on the other hand describes the path loss PLd depending on the distance d related to a reference path loss PLd0at distance d0. The path loss may be described as the difference of transmitted and received power in dB. α represents the path loss exponent that depends on the propagation environment, whereas X is a zero-mean Gaussian distributed random variable describing the fading effects at different locations and instants of time. If, in case of the usage of Equation (14), PLd0, α and the variance of X is known, one can calculate directly the probability for a certain distance d between transmitter and receiver. One disadvantage is that α and X are very dependent on the environment and can change significantly. The RSS measurements can be used along with lateration methods.
The second RSS-based approach is to measure in advance RSS values at certain positions within the localization area (fingerprints). The measured values are pre-processed and stored into a database. During the proper localization process, the current measurement values are compared to the values in the database and a best-fit position, based on the current values, is estimated. The advantage of using RSS values for this approach is that almost all devices come along with some kind of RSS-based output, including RFID readers. This method is used in scene-based positioning techniques.
Phase measurements can be used to provide information about speed, distance and angle. A good overview over these techniques is given in [21]. The radial velocity v of a tag is measured by evaluating the phase shift ∂φ during different moments in time ∂t as given in Equation (15).
v=-c2ω0∂φ∂tE15
with c being the propagation speed and ω0 the fixed circular frequency. The distance d between a tag and a reader can be calculated according to Equation (16) by measuring the phase shift at different frequencies.
d=-c4π∂φ∂fE16
Finally, phase measurements may be used to measure the angle θ between reader and tag (Angle of Arrival, AoA) using multiple receiving antennas. For two receiving antennas, Equation (17) describes the relation between the incoming angle θ, the phase difference φ2-φ1 at a certain carrier frequency, and the spacing a between the receiving antennas.
θ≈sin-1-cωφ2-φ1aE17
Phase measurement are used along with lateration and angulation principles to calculate the distance between transmitter and receiver respectively reader and tag.
2.2. Survey on UHF RFID-based localization systems
The following paragraphs provide a brief survey on state-of-the-art RFID localization systems within the UHF and microwave frequency band. The survey includes systems using RSS values, ToA and TDoA measurements, phase-based measurements as well as fingerprinting methods. Further surveys are provided in [22, 23, 24].
2.2.1. RSS-based direct range estimation
The SpotON system [25] is based on active RFID tags (working at 916.5 MHz) and provides a 3D ad hoc localization. RFID readers measure the signal strength of active RFID tags and a central server performs the calculation of the position within the environment. The relation between the RSS value and the position is based on the indoor channel model from Seidel and Rappaport [26]. The accuracy of the SpotON system is given with a cube of 3 m edge length, but this is dependent on the number of reference tags used. A disadvantage of the system is the long position calculation time from 10 to 20 s; an advantage is the easy to extend infrastructure and low system costs.
2.2.2. ToA-based range estimation
A 2.4 GHz RFID system based on SAW transponders is described in [27]. The SAW tags use a bandwidth of 40 MHz and reduce the echoes from the environment as the reflected tag signal is delayed due to the lower surface speed on the SAW material. The signal time on the SAW transponder is TSAW=2.2μs; so the reflections and echoes from the reader are almost faded out before the SAW-reflected signal responses back to the reader. A three-antenna system is used to perform a 2D positioning. However, the localization accuracy is strongly temperature-dependent and adds up to around 20 cm in a room with the dimension 2 m × 2 m.
2.2.3. TDoA-based range estimation
A localization system in the 5.8 GHz frequency band is described in [28]. The system is build upon active transponders and multiple base stations. One reference transponder is used as wireless synchronization source for the base stations. The system operates on the FMCW (frequency modulated continuous wave) principle (see [29]) and evaluates the time difference of a measurement transponder signal to determine the position of the measurement transponder. The position accuracy is given with 10 cm on an area of 500 m × 500 m.
2.2.4. Phase-based range estimation
The principle of FMCW is used to measure the distance to a certain object. The idea behind FMCW is to sweep a frequency band with the sweep rate α and record the phase and frequency differences. Furthermore, the transmitted signal from the reader is modulated by the transponder with a modulation frequency fmod. The usage of a modulation frequency shifts the measurement signal into a higher frequency band (by fmod), in order to suppress certain disturbances and noise within the baseband. The distance d is calculated through the frequency difference Δf and the phase difference Δφ [30], with the latter providing a high range resolution within half a wavelength of the signal. Therefore, Δf provides a coarse distance estimation and Δφ a more accurate one. Δφ alone cannot be used as direct distance estimation due to ambiguities of the phase information. According to [30] the distance to a transponder can be calculated with
dcoarse=π∙c∙Δf2∙α and dprecise=c∙Δφ4∙ω0.E18
[31] describes an FMCW-based RFID system using a transponder with an UHF front-end working at 868 MHz. The transponder IC provides a modulation frequency of fmod=300kHz and is driven by a 2.45 GHz FMCW signal with a bandwidth of 75 MHz. The system is tested on a cable-based setup and delivers an RMSE of 1 cm with cable lengths between 1 m and 9.5 m.
The system in [32] uses the phase difference observed at different frequencies to estimate the range between transponder and reader. The range estimation is performed according to Equation (16), whereas the maximum range dmax due to phase ambiguities is given with
dmax=c2B.E19
However, the choice of the bandwidth B strongly influences the system’s capabilities. A high B generates a high accuracy but a low maximum range; a low B leads to a higher range but at the expenses of a lower accuracy. Simulations at an SNR of 10 dB results in errors of 2.5 m for a frequency separation of B=1MHz, and errors of 0.1 m for a B of 26 MHz. One has to keep in mind that the separation of 26 MHz is only valid within the US frequency band for RFID that ranges from around 902 MHz to 928 MHz. The European band is smaller (865.6 MHz to 867.6 MHz) leading to a lower accuracy.
2.2.5. Scene-based range estimation
LANDMARC [33] is an extension and improvement of the SpotON system [25, 34]. The system consists of fixed RFID readers, active reference tags (landmarks) and tags to be localized. The system uses RSS values connected with the kNN (k-nearest neighbor) algorithm [35] to estimate the position. The average error of the system is given with 1 m [33].
[36] examines the localization error of the LANDMARC system using passive, instead of active RFID tags. As a result, the orientation of the tags has a major influence on the total performance of the system. Using the kNN algorithm, in 47.5 % of the cases, the error was less than 0.5 m and in 27.5 % of the cases, the error was less than 0.3 m. However, in comparison to the original LANDMARC system, the overall range is smaller due to the usage of passive RFID technology.
A system based on a particle filter is proposed in [37]. It uses two RFID readers mounted on a small mobile vehicle to localize itself using RSS values. The calibration phase is performed in a room of size 5 m × 10 m. Depending on the speed of the vehicle and the material on which the tags are located (plastics, concrete, metal) the average error is between 1.35 cm and 2.48 cm. This system is based on the mobile robot system in [38] that incorporates a SLAM algorithm [39] based on Monte Carlo methods [40].
[41] describes a positioning system using fingerprints (RSS values and read rate) to localize tagged objects. First, a rough positioning is done using antenna cells, with each antenna illuminating a different room zone. This rough classification is realized using either Bayesian filter, kernel density estimation (KDE) based measurement models, support vector machines (SVM) or LogitBoost [42]. RSS-based values and read rate is used along with the algorithms to roughly estimate the position of the tagged object. One result was that the estimations based on RSS values perform better than the estimations based on the read rate. An even more accurate positioning is realized when RSS values are used along with read rates of the transponders. Within the calibration phase, one tries to generate a high amount of reference points (fingerprints). Two algorithms are used and compared to perform within the positioning phase, a cascaded algorithm and a kNN algorithm. The cascaded algorithm runs the rough localization followed by the kNN algorithm for the high accuracy. The second algorithm resigns to use the rough position estimation. Similarly, the RSS-based fingerprints perform better than the read rates. Dependent on the environment, positioning errors between 37.9 cm and 42.1 cm may be achieved.
3. Wideband UHF RFID positioning system
This section introduces a brief motivation for the realized RFID positioning system before describing the basic structure of the system.
As derived from Section 2, current passive RFID localization systems suffer either from a high effort in the calibration phase (fingerprinting) or from bandwidth limitations which hold down the system’s overall accuracy. Higher accuracies may be achieved using phase-based approaches at the expense of more complex hardware structures and necessary volume (see, for instance, phased array antennas [43]), only usable for fixed reader hardware. Therefore, an ideal passive mobile RFID positioning system should have:
no change in hardware,
high bandwidth,
direct position estimation.
The here proposed system offers high bandwidth, but with very low power, and is based on a ToA method performing direct position estimation. As a consequence, additional hardware effort is necessary to provide the generation and evaluation of the high bandwidth signals.
In the following, a brief overview of the system, particularly its principle working structure, is provided.
Assuming a scenario as given in Figure 5. The scenario consists of n tags, whereby the distance to the ith tag has to be evaluated. The RFID reader is indicated at the bottom (only the coupler with antenna in monostatic mode) with input signal xreader (into the antenna) and output signal yreader (from the antenna). s1 to sn describe the backscatter modulation factors of the transponders, i.e., the factor with which the incoming signal from the reader is reflected with (principle of backscatter). If this factor is one, the complete signal is backscattered to the reader. Indeed, data from tag to reader is transmitted by varying this factor in time with the data to be sent [10, 44]. h1 to hn describe the bidirectional channel impulse responses between reader and tags. For reasons of simplification the following equations and terms are written without using the time t, although the expressions depend on it.
Figure 5.
Scenario with passive RFID tags and reader in monostatic antenna setup
According to Figure 5 we can state (in time domain) by using the convolution *:
In a second attempt, only tag #i is set into modulation state 2, all other tags stay in modulation state 1. This can be described as one small sequence of data transmission from the ith tag to the reader (uplink). The resulting signal yreader,2 is
thus, taking the difference results into observation of the ith tag with the ith channel. By assuming that the tag’s data contain the position of the tag (i.e., a reference tag), the reader has to evaluate the ith channel, regarding the range, to estimate the distance between reader and ith tag. In a 2D scenario, three tags must be read to get the position data, and three channels to the tags must be evaluated in order to localize the reader itself. The principle is described in more detail in Section 4.
The experimental hardware architecture of the reader is shown in Figure 6.
Figure 6.
Experimental hardware architecture of the realized RFID localization system based on passive UHF RFID tags
The frequency-coupled RF signal generators generate carrier signals at the center frequency of fc=900MHz. The arbitrary waveform generator (AWG) creates the localization signal xreader (in baseband). After upconversion of the localization signal, it is filtered, amplified and emitted into the environment through the antenna. The backscattered signal from the tag, the environment, and all other tags which may be in read range is also amplified, filtered and downconverted into complex baseband. The baseband signals are low-pass filtered and sampled with an oscilloscope, as is the original transmitted localization signal. Further processing is realized in MATLAB. Exemplary signals are shown in Section 6.
3.1. Derivation of the localization principle
Based on the result of the last equation (24) in Section 3, it is necessary to evaluate the channel response hi regarding the distance between tag and reader. As stated at the beginning of Section 3 the localization should be performed using direct distance estimation, thus, the signal’s time of flight t has to be evaluated in order to determine the distance d with the help of the propagation speed c, i.e.,
d=12c⋅t,E25
\n\t\t\t\t
with c usually being the propagation speed of electromagnetic waves complying to the speed of light. The factor one-half is introduced to compensate for the double distance the signal has to travel, i.e., from the reader to the tag and back.
In order to have a high positioning accuracy the signal must be broad regarding bandwidth, but the free spectrum for RFID, especially in Europe, is too small for that application. Therefore, higher out-of-band frequencies must be used. However, due to legal regulations, high bandwidth signals must be very low power, if applied. Ultra-wideband (UWB) signals [45] are such kind of signals and regulated by the Federal Communications Commission (FCC) and its counterparts in other regions, in order not to disturb any other in-band applications. UWB signals are defined as signals with bandwidths greater than 500 MHz or 20 % of the arithmetic mean of lower and upper cutoff frequency. The bandwidth used for the proposed system is 100 MHz due to the capability of UHF RFID tags working worldwide from around 840 MHz up to 960 MHz. Based on these conditions, although the proposed system only occupies 11 % of the arithmetic mean of the cutoff frequencies, the idea is to use low-power spreading signals for the ranging process. These signals are used to calculate the channel to a specific tag and back, thus extracting the time of flight information. As the low-power signals are hard to evaluate directly, the SNR is increased by performing coherent integration [46].
3.2. Mathematical model
Drawing up on Section 3, Equation (24), one can see that it is possible to derive the channel’s impulse response upon evaluating the difference between both modulation states of the RFID transponder. Necessary for calculating the distance between tag and reader is the signal propagation time t of the up- and downlink channel. Multiplying half of t with the propagation speed results into the distance d between tag and reader (Equation (25)). As the bandwidth is limited to 100 MHz (Subsection 4.1), the pulse width is 10 ns minimum. Accordingly, a pulse width of 10 ns corresponds to a distance of around 3 m, supposing the speed of light in air is approximately 30 cm per nanosecond. Furthermore, the distance to be covered by this passive localization system is limited to the distance passive RFID tags are able to handle, which is, currently, limited to around 8 m [47]. In addition, the transmitted signal consists of more than one single pulse. These conditions lead to the fact, that the transmit signal and the receiving signal cannot be separated in time, as in ordinary RADAR applications. Another alternative is the principle of correlation, that can be used to determine the time shifted replica of the transmit pulse signal within the receiving signal [48]. The discrete correlation Rxy[τ] between two signals xt and y[t] is given with
Rxyτ=∑k=-∞+∞x[k]⋅y[τ+k]=xt•yt.E26
The correlation term shows the time-shifted replicas of the signal xt within the signal y[t]. A local maximum within the correlation term means a high correlation between xt and [t], i.e., a high linear match. The point in time of the maximum shows the time shift between xt and y[t], that is used to calculate the time between transmitted signal xt and received signal y[t].
3.3. Example
Let us derive the principle at a simplified example. Assuming the channel of the ith transponder is noise-free and multipath-free given with just
hit=a⋅δt-Tdelay⋅ejφ0,E27
with a representing the reciprocal of the attenuation, δ[t-Tdelay] the time delay Tdelay of the channel with the Dirac delta function δ[t], and an initial phase shift of φ0. Furthermore, the transponder modulation states si,1 and si,2 are given with 0 (full tag absorption) and 1 (full tag reflection). Equation (24) may now be written as
The wanted time delay Tdelay is evaluated by searching for the the maximum within the term xreader[t]•Δyreader[t]2:
Tdelay=argmaxτa2⋅Rxreadert-Tdelay2E33
By receiving Tdelay the distance d between reader and tag can be calculated by evaluating Equation (25) with
d=12⋅c⋅Tdelay.E34
Multipath fading and receiver noise corrupt and distort the distance estimation. Gaussian noise on the low-power signals can be suppressed through coherent integration at the receiver. However, the increase in SNR due to integration is at the cost of receiving time [46]. The effect of multipath fading is more severe as it distorts the measurements in a way that is non-predictable without any a priori knowledge of the channel, which is given for a localization system working without channel prediction. The deployment of high-gain (low beam width) antennas with an electronic beam former can reduce the amount of multipath fading to an acceptable level.
4. Challenges and limitations
This section reveals the limitations and challenges of the proposed UHF RFID positioning system. Theoretical calculations show an accuracy limit at around 1 cm with the given hardware and signal limitations.
4.1. Limitations
The limitation of the system regarding the accuracy can be estimated using the Cramér-Rao Lower Bound (CRLB) [49], which defines a lower bound for an unbiased estimator θ^. This means that the unbiased estimator of θ is always worse or equal to the CRLB. For an unbiased estimator θ^ the standard deviation σθ^θ is defined as [50]:
σθ^θ≥CRLBθ^(θ)E35
Estimating the time-of-flight corresponds to the following CRBL definition of the standard deviation σx of the localization, i.e., the precision [50, 51]:
σx≥c2π⋅BRMS⋅2⋅SNRE36
cdescribes the propagation speed, SNR is the signal-to-noise ratio and BRMS is the effective bandwidth of the signal used and is defined as
BRMS=∫Bf2S(f)2df/∫BS(f)2dfE37
with the Fourier transform of the signal S(f) over the signal bandwidth B.
As the CRLB states in Equation (36), possible increases in precision are possible by either increasing the effective bandwidth of the signals or increasing the signal-to-noise ratio. If the given bandwidth is fixed, only an increase in SNR results in a higher measurement precision. As stated earlier, the SNR is increased by performing coherent integration. For instance, integration over n=10,000 signals, results in an SNR increase of factor 10,000, but only in a precision increase of 10,000=100. Theoretically, it is possible to increase the SNR as high as wanted, but receiver restrictions and timeouts limit the SNR to a certain level. These restrictions, mainly due to phase and quantization noise, define the limitations or the lower bounds of the localization system to a certain precision.
The applied hardware setup delivers the following SNR values for the quantization and phase noise. Thus, the receiver has an quantization error leading to an SNR of
SNRquantization≈50dB=105,E38
and phase noise leads to an SNR of
SNRphase≈34dB=2,512.E39
The total SNR is defined as
1SNR=1SNRquantization+1SNRphase+1SNRsignal.E40
SNRsignal is the power of the signal to the Gaussian noise power at the receiver. Figure 7 shows the maximum precision σx over certain SNRsignal values and coherent integrations with a factor of n. The effective bandwidth of the signal is given with BRMS=36.66MHz. The SNR values and the effective bandwidth are derived from the receiver properties and the shape of the transmit pulse. Also, the factor one-half is considered due to half of the distance from tag to reader that reduces the precision σx in Equation (36) by a factor of 0.5.
Figure 7.
Cramér-Rao Lower Bound of the localization system
As from Figure 7, it is shown that the lower bound for the standard deviation is around 1 cm. By increasing the number of coherent integrations n, the bound can be shifted to the left, which means, that the lower limit of the precision is reached for a lower SNRsignal value. For the proposed system, one measurement takes 1 μs, which increases to 1 s, if the coherent integration factor is n=1,000,000.
4.2. Challenges
Challenges this localization system is facing are mainly:
Multipath fading
Non-constant tag reflection factors which vary by frequency and power
Multipath fading due to reflections, scattering and diffraction can be suppressed by using high-gain antennas with a high-focussed beam. Hence, electronic beam steering is necessary to cover the area to detect RFID tags. Using an omni-directional antenna avoids electronic beam steering at the cost of more multipath fading. Another alternative, to minimize multipath fading is the use of a much higher bandwidth. In future, UWB technology combined with RFID could have a major effect on improvements in positioning accuracy [52, 53].
The non-constant tag reflection factors that vary over frequency and power are able to strongly deteriorate the position estimation [16], if disregarded. One solution for this problem is revealed in [54].
5. Measurement results
This section shows the obtained measurement results. The first measurements are taken in an anechoic chamber, the second measurements are taken in an office environment. Both measurements are one-dimensional measurements.
5.1. Measurement setup
The measurement setup is given as in Figure 8. It consists of the reader unit as described in Section 3, an reader antenna (Antenna #1) and an RFID tag with tag antenna (Antenna #2) followed by a HF switch for emulating the tag modulation states with impedances Z1 and Z2. For the sake of simplicity Z1 and Z2 are chosen as Short and Open, i.e., Z1=0Ω and Z2=∞Ω.
Figure 8.
Measurement setup of RFID localization system
Figure 9.
Exemplary transmit (TX) and receive (RX) pulses of the reader for Z1 and Z2, divided in real (R) and imaginary (I) signal components for an antenna to antenna distance of d=100 cm
The measurement procedure is as follows. The HF switch toggles to impedance Z1. Subsequently, the reader transmits and receives its signals as shown in Figure 9. Then, the switch toggles to Z2 and, again, the reader transmits and receives its signals. Dependent on the number of coherent integrations, this procedure is repeated up to n. Finally, the sampled signals are evaluated in MATLAB. Figure 9 displays the transmit and receive signals for a given setup (anechoic chamber at a distance of 100 cm). The upper half of the figure shows the transmit signal – based on the Barker code [+1,-1] – used for both modulation states, Z1 and Z2. The lower half of the figure indicates the received signals for Z1 and Z2, respectively. As the received signals are complex-valued, real and imaginary parts are depicted for each RX signal. As seen in Figure 9, the received signals match each other for a certain period of time, until the difference in reflection (of Z1 and Z2) emerges (beginning at around 500 ns). These signals are used to determine the time shift between TX and RX signal and thus the distance between reader and tag. Evaluations of the correlations can be found in [48, 55].
The following two subsections show the measurement results, i.e., the result of the correlation difference, for two environments. First, a measurement in an anechoic chamber (Figure 10, left), second, a measurement in an office environment (Figure 10, right).
The results of the measurement carried out in an anechoic chamber are depicted in Figure 11. The x-axis describes the real distances between the antennas, the y-axis describes the estimated distances. For normalization (cables, amplifiers, etc.) issues, the system is range-normalized to a distance of d=90 cm (measurement with lowest variance). The coherent integration factor was chosen to be n=100, i.e., each location was measured once with 100 transmit signals coherently integrated. The total RMSE error is 1.74 cm, which is the accuracy for a measurement distance from 80 cm to 280 cm. The fitting line in Figure 11 describes the regression line of the estimated distances. Hence, we can state that the system performs in the expected error ranges under very low multipath conditions.
Figure 11.
Results of the measurement carried out in anechoic chamber
Figure 12.
Results of the measurement carried out in an office environment
5.3. Office measurements
The results of the measurement carried out in the office environment are shown in Figure 12. Again, the x-axis describes the real distances between the antennas, the y-axis describes the estimated distances. The system is normalized to the distance of d=90 cm, performed in the anechoic chamber. The coherent integration factor was chosen to be n=100. The total RMSE error is 6.82 cm, which is the accuracy for a measurement distance from 70 cm to 260 cm. The fitting line describes the regression line of the estimated distances. The estimated values describe a nearly linear relation from 70 cm to 190 cm. The following estimated values are around 10 cm below the ideal line, the estimated value at the distance of 260 cm is back on track regarding the ideal line.
6. Result and discussion
The above measurements show that it is basically possible to gain range information down to accuracies of a few centimeters from the different modulation states of UHF RFID tags using wideband signals. However, there exist some simplifications, including the high-gain antennas and the tag modulation impedances given with open and short circuit (see also Subsection 5.2).
The idea behind the introduced localization system is based on the fact that current RFID-based localization systems either need a high effort in pre-calibration phases, suffer from bandwidth limitations, particularly in small frequency bands, e.g., as in Europe or need more complex hardware structures (phased array antennas) that only may be used in stationary, immobile applications. Therefore, a passive RFID-based positioning system should have ideally (Section 3) no change in hardware, high bandwidth, no pre-calibration phases and should be used in mobile applications. The suggested system includes these issues in the following way. There is no pre-calibration phase necessary as the system uses direct range estimation. This, however, is only possible due to the high bandwidth used along with low-power signals to stay within the required power spectrum densities. Changes in hardware would incorporate high bandwidth filter structures, a fast signal generator for the transmit pulses and a high accurate A/D converter for the incoming signals. Finally, it can be stated that such a localization system for mobile indoor positioning is possible, if the required hardware prerequisites are created.
7. Summary and conclusion
This chapter dealt with the concepts of localization comprising primarily UHF and microwave RFID systems. After describing the fundamental principles behind localization, a survey was given for state-of-the-art RFID localization systems. Subsequently, a novel RFID localization system using wideband signals was introduced. A theoretical derivation of the range determination was given in Section 4, whereas Section 5 revealed the limits and challenges of the proposed localization system, e.g., through evaluation of the Cramér-Rao lower bound. Finally, measurement results carried out in different environments (anechoic chamber, office) showed that the proposed system works within the former deduced limitations. The measurements showed a one-dimensional accuracy (RMSE) of 1.7 cm in the anechoic chamber, and an accuracy (RMSE) of 6.8 cm within the office environment. Tag reflection normalization and the usage of omni-directional antennas along with real-time localization are subjects to future work.
Acknowledgments
The authors would like to thank their colleagues from the Chair of Information Technology as well as from the Fraunhofer Institute for Integrated Circuits. Special thanks to our colleagues Frederik Beer, Gerd Kilian and Hendrik Lieske from the telemetry group for their valuable feedback.
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Introduction",level:"1"},{id:"sec_2",title:"2. Basics and state of the art",level:"1"},{id:"sec_2_2",title:"2.1. Positioning measurement principles",level:"2"},{id:"sec_2_3",title:"2.1.1. Precision, rightness and accuracy",level:"3"},{id:"sec_3_3",title:"2.1.2. Lateration",level:"3"},{id:"sec_4_3",title:"2.1.3. Angulation",level:"3"},{id:"sec_5_3",title:"2.1.4. Scene-based localization / fingerprinting",level:"3"},{id:"sec_6_3",title:"2.1.5. Positioning measurement techniques",level:"3"},{id:"sec_8_2",title:"2.2. Survey on UHF RFID-based localization systems",level:"2"},{id:"sec_8_3",title:"2.2.1. RSS-based direct range estimation",level:"3"},{id:"sec_9_3",title:"2.2.2. ToA-based range estimation",level:"3"},{id:"sec_10_3",title:"2.2.3. TDoA-based range estimation",level:"3"},{id:"sec_11_3",title:"2.2.4. Phase-based range estimation",level:"3"},{id:"sec_12_3",title:"2.2.5. Scene-based range estimation",level:"3"},{id:"sec_15",title:"3. Wideband UHF RFID positioning system",level:"1"},{id:"sec_15_2",title:"3.1. Derivation of the localization principle",level:"2"},{id:"sec_16_2",title:"3.2. Mathematical model",level:"2"},{id:"sec_17_2",title:"3.3. Example",level:"2"},{id:"sec_19",title:"4. Challenges and limitations",level:"1"},{id:"sec_19_2",title:"4.1. Limitations",level:"2"},{id:"sec_20_2",title:"4.2. Challenges",level:"2"},{id:"sec_22",title:"5. Measurement results",level:"1"},{id:"sec_22_2",title:"5.1. Measurement setup",level:"2"},{id:"sec_23_2",title:"5.2. Anechoic chamber measurements",level:"2"},{id:"sec_24_2",title:"5.3. Office measurements",level:"2"},{id:"sec_26",title:"6. Result and discussion",level:"1"},{id:"sec_27",title:"7. Summary and conclusion",level:"1"},{id:"sec_28",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'KaplanE. DHegartyC. JUnderstanding GPS: Principles And Applications. Artech House Mobile Communications Series. 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Microwave Theory and Techniques, IEEE Transactions on. 2004dec;521226642669'},{id:"B29",body:'StoveA. GLinear FMCW radar techniques. Radar and Signal Processing, IEE Proceedings F. 1992oct;1395343350'},{id:"B30",body:'VossiekMRoskoschRHeidePPrecise 3-D Object Position Tracking using FMCW Radar. In: Microwave Conference, 1999. 29th European. 11999234237'},{id:"B31",body:'HeidrichJBrenkDEsselJFischerGWeigelRSchwarzerSLocal positioning with passive UHF RFID transponders. In: Wireless Sensing, Local Positioning, and RFID, 2009. IMWS 2009. IEEE MTT-S International Microwave Workshop on; 200914'},{id:"B32",body:'LiXZhangYAminM. GMultifrequency-based range estimation of RFID Tags. In: RFID, 2009 IEEE International Conference on; 2009147154'},{id:"B33",body:'NiL. MLiuYLauY. CPatilA. PLANDMARC: indoor location sensing using active RFID. Wireless networks. 2004106701710'},{id:"B34",body:'HightowerJVakiliCBorrielloGWantRDesign and calibration of the spoton ad-hoc location sensing system. unpublished, August. 2001'},{id:"B35",body:'ChattopadhyayAHarishA. RAnalysis of low range Indoor Location Tracking techniques using Passive UHF RFID tags. In: Radio and Wireless Symposium, 2008 IEEE. IEEE; 2008351354'},{id:"B36",body:'ChattopadhyayAHarishA. RAnalysis of UHF passive RFID tag behavior and study of their applications in low range indoor location tracking. In: Antennas and Propagation Society International Symposium, 2007 IEEE; 200712171220'},{id:"B37",body:'ParkSLeeHSelf-recognition of Vehicle Position using UHF Passive RFID Tags. 2012'},{id:"B38",body:'HahnelDBurgardWFoxDFishkinKPhiliposeMMapping and localization with RFID technology. In: Robotics and Automation, 2004. Proceedings. ICRA’04. 2004 IEEE International Conference on. 1IEEE; 200410151020'},{id:"B39",body:'HahnelDBurgardWFoxDThrunSAn efficient fastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements. In: Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on. 12003206211vol.1.'},{id:"B40",body:'DellaertFFoxDBurgardWThrunSMonte Carlo localization for mobile robots. In: Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference on. 2199913221328vol.2.'},{id:"B41",body:'ParlakSMarsicINon-intrusive localization of passive RFID tagged objects in an indoor workplace. In: Proc. IEEE Int RFID-Technologies and Applications (RFID-TA) Conf; 2011181187'},{id:"B42",body:'FriedmanJHastieTTibshiraniRAdditive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors). The annals of statistics. 2000282337407'},{id:"B43",body:'KarmakarN. CRoyS. MIkramM. SDevelopment of Smart Antenna for RFID Reader. In: RFID, 2008 IEEE International Conference on; 20086573'},{id:"B44",body:'FinkenzellerKRFID Handbook: Fundamentals and Applications in Contactless Smart Cards, Radio Frequency Identification and Near-Field Communication. Wiley; 2010'},{id:"B45",body:'TaylorJ. DIntroduction to ultra-wideband radar systems. CRC; 1995'},{id:"B46",body:'LoefflerAGerhaeuserHA Novel Approach for UHF-RFID-Based Positioning Through Spread- Spectrum Techniques. Smart Objects: Systems, Technologies and Applications (RFID Sys Tech), 2010 European Workshop on. 2010june;110'},{id:"B47",body:'UssmuellerTBrenkDEsselJHeidrichJFischerGWeigelRA multistandard HF/ UHF-RFID-tag with integrated sensor interface and localization capability. In: RFID (RFID), 2012 IEEE International Conference on; 20126673'},{id:"B48",body:'LoefflerALocalizing passive UHF RFID tags with wideband signals. In: Microwaves, Communications, Antennas and Electronics Systems (COMCAS), 2011 IEEE International Conference on; 201116'},{id:"B49",body:'GustafssonFGunnarssonFMobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements. Signal Processing Magazine, IEEE. 2005july;2244153'},{id:"B50",body:'Fowler\n\t\t\t\t\t\t\tM. EECE 522 Estimation Theory; 2012. Available from: http://www.ws.binghamton.edu/fowler/fowler%20personal%20page/EE522.htm.'},{id:"B51",body:'Gezici\n\t\t\t\t\t\t\tS. A Survey on Wireless Position Estimation. Wireless Personal Communications. 2008;44\n\t\t\t\t\t263282 . 10.1007/s11277-007-9375-z. Available from: http://dx.doi.org/10.1007/s11277-007-9375-z.'},{id:"B52",body:'Arnitz\n\t\t\t\t\t\t\tD, Adamiuk\n\t\t\t\t\t\t\tG, Muehlmann\n\t\t\t\t\t\t\tU, Witrisal\n\t\t\t\t\t\t\tK. UWB channel sounding for ranging and positioning in passive UHF RFID. 11th COST2100 MCM. 2010;Available from: http://spsc.tu-graz.ac.at/system/files/arnitzcostmcm10.pdf.'},{id:"B53",body:'ArnitzDMuehlmannUWitrisalKUWB ranging in passive UHF RFID: proof of concept. Electronics Letters. 2010462014011402'},{id:"B54",body:'ViikariVPursulaPJaakkolaKRanging of UHF RFID Tag Using Stepped Frequency Read-Out. Sensors Journal, IEEE. 2010sept;10915351539'},{id:"B55",body:'LoefflerADispersion Effects at High Bandwidth Localization for UHF RFID Tags. ITG-Fachbericht-Smart SysTech 2012. 2012'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Andreas Loeffler",address:"loeffler@like.eei.uni-erlangen.de",affiliation:'
Chair of Information Technology (Communication Electronics) Engineering, Friedrich-Alexander-University of Erlangen-Nuremberg, Erlangen, Germany
Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany
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1. Introduction
Self-efficacy is a trait where one can make a sound judgement about his or her own decision. An individual who trusts him or herself is considered of having good self-efficacy. Therefore, self-efficacy is a crucial factor in creating a harmonious society through the demonstration of ethical behaviour. Bandura [1] argues that an individual who possesses a high level of self-efficacy can achieve personal success as well as behave positively. On the contrary, a person with a low level of self-efficacy is less successful and less confident in achieving the goals set. According to Eisenberg et al. [2], the existence of the perceived self-efficacy as a factor in social learning cognitive is related to the emphatic efficacy perception, which is also related to prosocial behaviour. Thus, self-efficacy serves as a critical factor in how far a person can involve him or herself with the feedback from other people.
However, every person has a different personality trait. The diversity of human personality becomes the determiners of prosocial behaviour. Penner et al. [3] suggest that those who have a prosocial personality have more readiness to help others and are more emphatic. Based on a study by Penner et al. [3], the Helpfulness factor serves as the predictor that explains prosocial behaviour better than the Other-oriented Empathy factor. Penner et al. [3] argumentation involves helping, which has a strong link with self-efficacy than the environmental skills mastery. This link explains that someone who is consistently involved in prosocial behaviour does so because he or she is doing something for him or herself, not only because of his or her contributions to others.
Meanwhile, according to Norenzayan and Shariff [4], a society that practices a more religious life tends to behave more prosocially due to the understanding of the concept of God. The studies on the relationship between prosocial behaviour and religion explain about the religious influence on the prosocial value in a society such as simplifying a behaviour that can benefit others [4], teaching about compassion [5], volunteering to help as well as acting non-aggressively [6] when coming into contact with the daily routine.
According to NICE Guideline on Diagnosis, Assessment and Management of Harmful Drinking and Alcohol Dependence published by the British Psychological Society and the Royal College of Psychiatrists, NICE [7], psychological interventions consist of a broad spectrum that is subjected to the underpinning theoretical models. They are classified under several approaches such as behavioural, cognitive, psychodynamic, humanistic, systemic, motivational, disease, and social and environmental. In relation to nurturing higher level of self-efficacy among individuals who suffer from low-level self-efficacy so that they can behave more prosocially, cognitive behavioural therapy is considered as a two-in-one approach to address the issues based on the assumptions that cognition has a significant influence on humans’ emotional and behavioural impediment [8]. This section centres on a brief discussion on psychological intervention, particularly cognitive behavioural therapy, which is one of the intervention approaches that are empirically supported and evidence-based to see convincing results [9].
2. Self-efficacy
Self-efficacy is a concept that can be linked to the extent of control that a person has over the situation in his or her life. A person with a high level of self-efficacy is someone who thinks positively, charismatic, courageous and persistence. Therefore, self-efficacy becomes a factor that leads to the behaviour of a person. Bandura [10] discusses the concept of self-efficacy, which also influences behaviour. Self-efficacy can be explained as a person’s trust or judgement on the faculty and competence of his or herself to carry out actions to achieve specific goals set [1, 10, 11, 12].
In other words, self-efficacy is the inherent belief of the extent of the ability to act based on the faculty within oneself. Self-efficacy can influence a person’s performance achievement [10] whether it is at a peak or otherwise. It means the level of self-efficacy is capable of influencing more excellent contribution to work performance compared to those with a low level of self-efficacy. This self-competence becomes a crucial factor in the cognitive, motivation and health aspect formation process [1]. The individuals who have a high level of self-efficacy exhibit characteristics of those among the successful and consistent in their strategic planning.
Therefore, we can see that individuals with high self-efficacy possess the determination and readiness to sacrifice for the benefit of others. They reason and can accept challenges with high determination. It is the opposite of individuals with low self-efficacy. They will avoid doing strenuous work and always see life challenges as problems that haunt their lives. These individuals are incapable of accepting the burden, and in life, they often end up in despair. They are incapable of controlling themselves with negative thoughts and blame others for what has happened in their life. According to Bandura [1], the capability to master good cognitive and thinking skills will enable the control of matters that can affect one’s life.
Bandura [1] differentiates between efficacy expectations and outcome expectations (Figure 1). Bandura [1] places self-efficacy as efficacy expectations. Efficacy expectations involve the perception towards the ability of the self in determining the expected actions. Meanwhile, outcome expectations serve as self-estimation regarding the behaviours performed that will result in specific outcomes. Through efficacy expectations, an individual is capable of evaluating an action performed whether it is good, bad, precise or otherwise. Efficacy expectations portray the ideal self-evaluation that can be achieved, whereas the outcome expectations achievement depends on an individual’s endurance in ensuring that the wish is fulfilled [1]. This mean, an individual can have realistic efficacy expectations, which is hoped can be materialised; however, the outcome expectations will become unrealistic if the hope is too high from the actual expected results.
Figure 1.
Diagram of the representation of the differences between efficacy expectations and outcome expectations. Source: Bandura [1].
Therefore, an individual with high-efficacy expectations is confident in being able to carry out actions that are suitable for the situational demands. Meanwhile, the realistic outcome expectations that are aligned with the faculty of the self will cause the individual to work hard and is capable of achieving the goals set. Self-efficacy becomes the mediator that can determine individuals’ behaviour, whereas the individuals play the role of controlling actions through self-control assessment. The self-control mechanism enables a person to set realistic outcome expectations that are aligned with their ability and faculty. Self-efficacy is closely linked to excellence in achieving good performance, which is assessed as cognitive determinants. Muhammad Awais [13] argues about self-efficacy that it is capable of providing the measurement regarding one’s personality aspect that involves motivation and trust. Meanwhile, according to Eklund et al. [14], individuals who have a high level of self-efficacy have better prosocial value and are more popular.
3. Prosocial behaviour
Helpfulness trait has become the practice of today’s society. The need to offer help is not limited to those we know but also strangers. Similarly, the caring attitude towards other people’s welfare in a sincere manner that hopes for nothing in return has become a social practice within societal life environment. These traits form a prosocial value that leads to prosocial behaviour. According to Hastings et al. [15], prosocial behaviour is a form of behaviour that is voluntarily performed in the attempt to benefit others. It is a response that is performed proactively and reactively towards other people’s needs in the attempt to promote healthier social life. Therefore, volunteering activities should be nurtured as a society’s responsibility towards a healthy and prosperous life. It can also encourage social responsibility in shaping prosocial attitude among the local community.
Prosocial behaviour is a positive behaviour that is the opposite of the antisocial behaviour as the negative behaviour. Prosocial behaviour relies on the belief that an individual is part of a local community in which they need mutual help, support and love for one another [16, 17]. Therefore, an individual who practices prosocial behaviour that the community encourages will feel the sense of acceptance from his or her community group. It is because humans prefer giving help to the members of their group compared to strangers. Thus, the cultural difference can also cause some of the individuals having less tendency towards helping and putting their trust in strangers.
As the consequences, collective culture exists, which leads to egocentrism within a society. An egocentric society cares less for those who are outside of their group. This condition causes the antisocial behaviour and reduces the tendency of prosocial behaviour in the society. Fiedler et al. [18] argue about this situation where cultural differences can influence prosocial behaviour. In-group favouritism sees groups that share a similar identity as more straightforward to be helped compared to the out-group. According to Carlo and Randall [19], prosocial behaviour has two central concepts, which are the readiness to help and altruism.
Similarly, prosocial behaviour introduced by Penner et al. [20] through the development of Prosocial Personality Battery (PSB) to discuss two fundamental factor structures; Other-oriented Empathy and Helpfulness. The first factor; Other-oriented Empathy explains the cognitive and affective domain through the dimension of social responsibility, mutual moral reasoning, other-oriented reasoning, empathic concern and also perspective taking. Meanwhile, the second factor; Helpfulness relates to behavioural tendency through self-reported altruism and personal distress [3, 20, 21, 22].
Among the key aspects that have become the essence of focus in the discussion regarding prosocial behaviour are the causes and motives behind the formation of prosocial behaviour. The questions that arose through the discussion by Baron et al. [16], among others, to look at the motives, about how far an individual is involved in helping the needy and the situational and personal factors that cause the exposure of prosocial behaviour to a person. A low level of empathy as well as prosocial behaviour is said to contribute to the rejection of prosocial behaviour [17]. This condition shows that prosocial behaviour is linked to the voluntary actions to help others.
At the same time, individuals who provide the helping awareness are those with a high level of empathy. The present emotional, personal and situational influences are among the pulling factors of the existence of prosocial behaviour. The readiness to help among individuals largely depends on the existence of emotional effects, which involved the empathy aspect. When an individual possesses empathy, he or she will place him or herself in other people’s shoes. Other people’s misfortunes become the encouragement to the readiness to help among them. The help is as an effort to share emotions.
However, according to Carlo and Randall [19], a crowd situational factor makes an individual to have less tendency to behave prosocially. This condition shows that the sense of responsibility and readiness to help will decrease when they assume that other people can help. Each one hopes that there will be someone else who will help and take the responsibility. In today’s globalisation challenges, this situation commonly occurs, which creates pluralistic ignorance when the crowd sees things that are unrelated to them. For example, the circumstances and mishaps within a society like wars, murders, robberies, deaths and missing persons received a cold shoulder and ignored on the social media. It is even more so if the circumstances befall on strangers. This scenario also leads to the news posts and shares on the social media that are lacking in empathy. Therefore, we must encourage the society, especially the Y generation to always behave prosocially in life. The dissemination of the caring and helpfulness culture within the identity of a society must be done especially concerning emergency circumstances. The society should be educated with awareness and with commitment in a good social environment to continue the prosocial behaviour.
Prosocial development is also associated with behaviourism and social learning theory [2] founded by Albert Bandura and his colleagues. Eisenberg et al. [2] argue using the social learning theory by presenting internal cognitive process as the critical role in behaviour influence. Therefore, impersonation is a critical process to achieve the socialisation standard of an individual’s moral behaviour. Meanwhile, altruism is closely related to prosocial behaviour that can be described as an essential motive by voluntarily helping others without expecting anything in return [16, 20]. Altruism supports the prosocial concept as a motivational drive to unselfish behaviour to help others with sincerity and honesty. Therefore, an altruistic attitude is an attitude that prioritises the interests of others without expecting rewards in return.
From the aspect of readiness to help, the study of Carlo and Randall [19] states that compares to women, men are more fearless in offering help whether to those they know or strangers. The tendency to behave prosocially among men is higher from the aspect of exhibiting heroism, whereas women tend to help in the aspect of care, education and commitment. The arguments by Carlo and Randall [19] justify the different traits and characters between men and women. These gender differences produce different actions. Men tend to exhibit masculine actions such as daring to take the risk, being protective and having physical strength. On the contrary, women tend to exhibit more feminine actions such as loving and caring in giving a long-term commitment. Therefore, the gender differences resulted in different prosocial behaviours between men and women.
The studies on prosocial behaviour have also attracted the attention of the media scholars and researchers such as Gentile et al. [23], Wilson [24], Strasburger et al. [25] and Yates [26]. According to Wilson [23], if the media such as television and films expose children to the antisocial behaviours such as aggressiveness, then the same media should also be able to expose them to prosocial behaviour. Therefore, by giving instructions and controls especially to children regarding the choice of the media program, that should be able to help them become a more critical consumer towards the media content. It can encourage prosocial benefits from the time they spend in front of the television screen and other media devices such as films and the Internet.
Based on the previous studies, there were many studies on prosocial behaviours that are linked to media roles [24, 25, 26, 27]. However, the group of people that the researchers often choose to be the subjects linked to prosocial behaviours are children and adolescent [23, 24, 25, 28, 29]. According to Dumova [27] based on social cognitive theory, children who are exposed to prosocial models and educational media content are more influenced by such prosocial behaviour during the interaction with their peers.
Strasburger et al. [25] state that researchers who are studying about prosocial learning through media often place the assumption where the media characters that portray traits such as kindness, mutual cooperation, responsible and unselfishness give the examples for children, especially for them to learn and imitate such behaviour. The study by Ostrov et al. [28] proves the existence of the link between media exposure and prosocial behaviour. In a longitudinal study conducted over 2 years, Ostrov et al. [28] examined the patterns and patents of change and development on the same sample that consisted of early kindergarten children, their parents and principals.
Ostrov et al. [28] study on the media exposure role is seen as for whether contributing to aggression as well as prosocial behaviour. The study on television programs as an interactive model contributes to positive learning through prosocial behaviour [26, 27]. Similarly, the television drama acts as ‘facilitator’ in forming prosocial behaviour [30]. Hence, in addition to having a link and relationship with violence and aggression affecting the behaviour of the audience, the television should also play its role in exposing the audience to prosocial behaviour. It can give benefits to the audience and the society.
The study by Udornpim and Singhal [31] highlights a character in a Japanese soap opera named ‘Oshin’ as a media role model among its audience in Thailand. In the outcomes of the study, Udornpim and Singhal [31] found that ‘Oshin’ presents a character who has traits such as perseverance, strong-willed and think positively as well as a willingness to help her family members who need aids. In the study, Udornpim and Singhal [31] have discussed 23 prosocial values, which among those are tolerance, independence, responsibility, mutual helpfulness, caring, forgiveness, gratefulness, diligence, high aspiration and love. The ‘Oshin’ character becomes a positive role model for the audience and illustrates how they could learn about prosocial values through the drama. Wallbank’s [32] study also highlights the media model ‘Robin Hood’ in the discussion on the comparison between the antisocial and prosocial behaviour.
Meanwhile, Muller and Donnerstein [29] conducted two experimental studies on prosocial behaviour by screening an arousal film form and by using the excitation transfer model. The study by Muller and Donnerstein [29] on the subjects consisting of college students (male) aims to see the outcomes either from the aspect of positive and neutral behaviour. The subjects watched either the arousing erotic film or the controlled film that does not have the arousing erotic elements. The findings show that the arousal film can increase the subjects’ punitive behaviour. Whereas, the subjects who watched the film that is absent from the arousing erotic elements show a more positive attitude [29]. Meanwhile, the correlation studies conducted by Gentile et al. [23] show a positive association between prosocial behaviour with prosocial gaming exposure.
The formation of prosocial behaviour is also associated with self-efficacy of individuals involved. Individual domination over self-efficacy can contribute to the differences in how he or she behaves. Carlo et al. [33] study measures prosocial behaviour based on social cognitive theory through the role of self-efficacy to prove the characteristics of the adolescent respondents in showing prosocial behaviour.
Carlo et al. [33] study found that there were differences in the types of prosocial behaviour among early and middle adolescents. Whereas, through the prosocial agency [34], humans can act as agents who set goals and behave following their personal and standard values that have been outlined based on their capability. It shows the contribution of self-efficacy and values to prosocial behaviour as discussed by Caprara and Steca [35].
4. Prosocial values and self-efficacy of religious society
The religious doctrine can be understood as a belief in God or magical and divine power such as deities [36, 37]. Preston et al. [37] and Hardy and Carlo [5] link religion to values and morals that exist within a society, while Saroglou et al. [6], as well as Norenzayan and Shariff [4], argue that religion is a part of a culture that set the values for people who practise religious life with prosocial standards. The definition of the concept of value depends on a few aspects, which originate from different fields such as political science, social sciences or religion. Whereas, Rokeach [38] sees the link between value with attitude and belief. The concept of value refers to the belief and attitude of an individual to translate it into a form of behaviour [38] whether the process produces something better or otherwise.
Whereas, the value from the Islamic point of view sees the relationship with Allah as the path of life in deciding [39], which includes ethical (akhlak) values (behaviour, beliefs and religion) and morality [40]. The appropriateness of every decision is based on the lifetime value that centres on the Islamic shariah. Syed Muhammad Naquib [39] also associates the concept of value with civility (adab). Adab in Islam centres on ethics, norms and morality.
Social and moral values are crucial as they are the root of well-being and harmony for a religious society. Noble values within a religion explain life that revolves around helping each other and living in peacefulness. Hence, religious prosociality values are in proximity with the noble values taught to a religious society [4]. There are studies on the prosocial behaviour that are viewed from the religious perspective, which becomes the catalyst for a high prosocial tendency within a society [4, 5, 6, 41]. Meanwhile, the study by Hardy and Carlo [5] proves that religiosity (the level of religious life) has a significant positive relationship with social behaviour predictor factors; kindness, compliant and altruistic values (prioritising other people’s interests).
However, according to Malhotra [41], the link between religion and behaviour is closely related to the context of religious norms, especially for Christians who consider Sunday as the day to meet God. Therefore, most Christians behave more prosocially when the behaviour is linked to religious norms through religious importance on specific days (religious salience). The factor of religious belief is the driving force for a society to behave prosocially. According to Batara et al. [42], a society where its religious value is high is more willing to help others. Whereas, Sasaki et al. [43] argue that individuals with high religious value are better in their prosocial behaviour. However, according to Sasaki et al. [43], the religious influence on prosocial behaviour differs regarding an individual’s acceptance. Even though religious factors contribute to prosocial behaviour, but undeniably there are other factors in the development of prosocial behaviour.
Prosocial behaviour is also aligned with the teachings of Islam that aim to encourage the society to practise noble values in life [44]. Islam as a religion that promotes peace and helpfulness accepts prosocial value as a decent value. It is because a religious society places a high emphasis on good religious values such as helpfulness, love and forgiveness. For Muslims, religion refers to Islam as ad-deen that can be explained as the submission to Allah as a way of life through the reinforcement of faith (aqidah) of the oneness of Allah, worships (ibadah), dealings (muamalah) and Islamic ethics (akhlaq).
In the same way with self-efficacy as a personal belief that influences an individual to behave in a certain way. According to Sania and Amena [45], self-efficacy has a significant association with religious belief. It means individuals who have self-efficacy awareness will be able to control better the actions and situations they meet. In the context of a society that holds onto a religious belief, self-efficacy fulfils the personal needs of an individual who believes and trusts in his or her capability. This belief leads to the ability and the faculty of the individual in assuming the responsibilities given unto him or her. The success in carrying out the responsibilities leads to outstanding human characteristics. A successful being is a balanced person between the physical and internal aspects of the self, which include self-confidence, goodwill and humbleness as well as consistently positive about success. However, according to Noornajihan [46], self-efficacy from the Islamic perspective is unlimited to only the belief that exists within the individual’s self but also associated with the relationship with the Almighty Creator. Every Muslim believes in the oneness and the power of Allah as the Almighty Creator in the occurrence of everything.
5. Cognitive behaviour therapy: a psychological intervention influencing the formation of prosocial behaviour
Cognitive behavioural therapy (CBT) is a universal expression that originated from the cognitive model of affective disorders, which comprises of many approaches that are based on the postulation that cognition significantly influences emotion and behavioural impediment, and it also offers a wide range of evidence-based cognitive and behavioural techniques and therapies [7, 8]. Moghaddam and Dawson [8] added that despite the general assumption that cognition controls emotion and behaviour, the approaches in CBT have different emphasis, to name a few such as level of analysis (for example, situation-based versus person-based), levels of cognition (for example, prompt thoughts versus underlying central viewpoints) and problem-specificity (for instance, trans-diagnostic versus disorder-specific). Therefore, CBT involves a concerted effort between clients and therapists using a shared configuration to attain the treatment targets [7].
This concerted effort is idiographic in nature as it centres on customised therapy approach that best fits the patients’ individual’s conditions and needs. The customised intervention is built based on case formulation. According to Persons [9], case formulation is an empirically supported therapy (EST) in providing psychological treatment that is evidence-based that allows flexibility in addressing the patients’ unique needs and also functions as guidelines for therapists in the decision-making process, especially in clinical treatment. Persons [9] further explained that an all-inclusive case formulation logically and coherently binds all of the following elements, which are (1) description of patient’s symptoms, disorders and problems, (2) suggestion of hypotheses regarding the mechanisms that cause the disorders and problems and (3) suggestion of the latest outcomes of the present disorders and problems as well as the roots of the mechanisms. In cognitive behavioural therapy, a therapist begins a case formulation by obtaining a diagnosis through the collection of assessment facts. He or she then structures the diagnosis into the individualised formulation of the case and uses it to facilitate the task of designing a therapeutic strategy. Patient’s consent is necessary before the treatment begins, and once it starts, the formulation will serve as decision-making guidelines for the therapist. This collaborative treatment enables the therapist to collect critical data in observing the treatment progress. It also allows the therapist to make the necessary adjustment [9].
Negative social exchanges refer to the unwelcome and unsympathetic exchanges among members of a social network [47]. The examples of the negative social exchanges are neglect, rejection, insensitivity, interference and unwanted advice (Brooks and Dunkel Schetter, 2011; Newsom et al., 2005; Rook, 1998 in Silva et al., 2016 [47]). It is an ironic social exchange where members of a social network assume that they are behaving helpfully, but in the actual sense, they are not (Oku and Keith, 1998 in Silva et al., 2016 [47]). Research indicates that negative social exchange negatively influences health [47], therefore about self-efficacy and development of prosocial behaviour, the negative social exchange could pose a threat in promoting higher level self-efficacy among people with low of self-efficacy and behaving antisocially.
6. Conclusions
The concept of self-efficacy explains the attitude of individuals who believe in themselves. It becomes the catalyst for the rational cognitive process in shaping the behaviour of an individual. Hence, self-efficacy that is inherent in each person can shape a reasonable and mature prosocial value in a society. If a society has a high self-efficacy, then the condition of the society to change to a better state is also high. There is, however, a challenge in having positive-minded individuals with high self-efficacy. In dealing with the reality of life, humans are often tested with different trials and tribulations whether they are personal, family, academic or workplace problems. People with good self-efficacy accept the trials and tribulations as a challenge. However, if an individual accepts the pitfalls negatively, then they will not put efforts to overcome them. Even more so, they will see the problems as difficulties that decrease their motivation.
Academic scholars often relate the relationship between the level of self-efficacy with positivism that contributes to the formation of individuals’ prosocial behaviour. Individuals who have a decent self-efficacy level will see the world with utmost positivism. They can translate such positivism through the proper behaviour that is inherent in themselves. They have positive ways of thinking that project a fair view among those who are around them. Thus, the readiness to help and love for each other exists as the essential elements in prosocial behaviour.
This condition is also associated with the role of religion that encourages its society to practice tolerance and kindness to others. A society that is educated with positive actions such as being helpful with one another, loving as well as having an emphatic soul, interacts easier within a peaceful and harmonious atmosphere. A society that has a prosocial personality tends to behave positively more easily. Therefore, the efforts to help based on the concern towards other people become the motivational factor that is closely associated with prosocial behaviour. Prosocial behaviour is a positive behaviour that can contribute towards better societal change. A decent societal community is those who are complementing each other, living peacefully without disagreement as well as respecting different views among them. All the religions in this world promote peace and safety, which supports prosocial value. The attitudes such as helpfulness, respect and love are the requisite of religious society that leads towards the reinforcement of prosocial behaviour through self-efficacy.
Thus, having the data of patient’s negative social exchanges at the start of the treatment allows a therapist to customise the intervention through the formation of a precise and unique psychological intervention that is sensitive towards the patient’s most fragile areas and the ones that need to be addressed promptly. This idiographic customisation allows a better understanding of the kind of negative social exchanges that could hinder the attainment of a higher level of self-efficacy. The recognition will help the therapist to strategically facilitate the alteration of the patient’s thought process into becoming more harmonious, which resulted in better self-efficacy level that later helps an individual behaves more prosocially.
Acknowledgments
This research was funded by Universiti Kebangsaan Malaysia (UKM) through Research Grant University (GUP-2018-011).
Conflict of interest
I have no conflict of interest and yes I have read and understood the guidelines on copyright.
\n',keywords:"prosocial, behaviour, self-efficacy, religion, community",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/65917.pdf",chapterXML:"https://mts.intechopen.com/source/xml/65917.xml",downloadPdfUrl:"/chapter/pdf-download/65917",previewPdfUrl:"/chapter/pdf-preview/65917",totalDownloads:413,totalViews:0,totalCrossrefCites:1,dateSubmitted:"June 26th 2018",dateReviewed:"October 11th 2018",datePrePublished:"March 1st 2019",datePublished:"July 8th 2020",dateFinished:null,readingETA:"0",abstract:"Prosocial value can be associated with the positive behaviours that are commonly practised in the socio-surrounding of a civilised community, for example, the action of helping, loving and developing empathy with each other. The formation of the prosocial behaviour also has a connection with one’s self-efficacy. The individual’s mastery of his or her self-efficacy can contribute to the differences in how he or she is behaving. All of the ethical values in every religion explain life as revolving around helping one another and living peacefully in society. The effort of helping based on the concerns towards other people could be the motivational factors that are closely related to the prosocial behaviour. Having positive personality traits and then backed with a sound religious belief ingrain the necessary moral compass that guides a person into behaving in ways that are considered society-friendly. Meanwhile, media roles depending on the content are capable of propagating decent values among the larger mass. Generally speaking, these factors are sufficient in educating and nurturing normal individuals to attain better self-efficacy. However, on an earnest ground considering individuals who suffer from a low level of self-efficacy, which resulted in antisocial behaviour, a more structured and empirical psychological intervention needs to be administered.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/65917",risUrl:"/chapter/ris/65917",signatures:"Rosmawati Mohamad Rasit and Siti Zuhrah Che Ab Razab",book:{id:"8317",title:"Cognitive Behavioral Therapy",subtitle:"Theories and Applications",fullTitle:"Cognitive Behavioral Therapy - Theories and Applications",slug:"cognitive-behavioral-therapy-theories-and-applications",publishedDate:"July 8th 2020",bookSignature:"Sandro Misciagna",coverURL:"https://cdn.intechopen.com/books/images_new/8317.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"103586",title:null,name:"Sandro",middleName:null,surname:"Misciagna",slug:"sandro-misciagna",fullName:"Sandro Misciagna"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"264889",title:"Dr.",name:"Rosmawati",middleName:null,surname:"Mohamad Rasit",fullName:"Rosmawati Mohamad Rasit",slug:"rosmawati-mohamad-rasit",email:"rosmawati@ukm.edu.my",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Self-efficacy",level:"1"},{id:"sec_3",title:"3. Prosocial behaviour",level:"1"},{id:"sec_4",title:"4. Prosocial values and self-efficacy of religious society",level:"1"},{id:"sec_5",title:"5. Cognitive behaviour therapy: a psychological intervention influencing the formation of prosocial behaviour",level:"1"},{id:"sec_6",title:"6. Conclusions",level:"1"},{id:"sec_7",title:"Acknowledgments",level:"1"},{id:"sec_10",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Bandura A. Self-efficacy: Toward a unifying theory of behavioral change. Psychology Review. 1977;84(2):191-215'},{id:"B2",body:'Eisenberg N, Fabes RA, Spinrad Tracy L. Prosocial development. In: Damon W, Lerner RM, Eisenberg N, editors. Handbook of Child Psychology Social, Emotional dan Personality Development. 6th ed. New Jersey: John Wiley and Sons, Inc; 2006. pp. 646-718'},{id:"B3",body:'Penner LA, Dovidio JF, Piliavin JA, Schroeder DA. 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Relationship between self-efficacy a type personality and leader effectiveness. International Journal of Engineeering Research and Management. 2018;5(1):33-38'},{id:"B14",body:'Eklund J, Loeb C, Hansen EM, Anderson-Wallin AC. Who cares about others? Empathic self-efficacy as an antecedents to pro-social behaviour. Current Research in Social Psychology. 2012;20:31-41. Available from: https://uiowa.edu/crisp/sites/uiowa.edu.crisp/files/20.3.pdf'},{id:"B15",body:'Hastings PD, Utendale WT, Sullivan C. The socialization of prososial development. In: Grusec JE, Hastings PD, editors. Handbook of Socialization: Theory and Research. New York: Guilford Publications; 2007. pp. 638-661'},{id:"B16",body:'Baron RA, Branscombe NR, Byrne D. Social Psychology. 12th ed. USA: Pearson Education; 2009'},{id:"B17",body:'Twenge JM, Ciarocco NJ, Baumeister RF, Nathan DeWall C, Bartels JM. Social exclusion decreases prosocial behavior. 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Modeling strategies for prosocial television: A review. In: Kertas Kerja Dibentangkan di Open Paper Competition AEJMC Siutheast Colloqium; 4-6 Mac. Lexington, Kentucky. 1999'},{id:"B27",body:'Dumova T. Prosocial learning. International Journal of Learning. 2006;2(9):183-194. Available from: http://www.learning-journal.com'},{id:"B28",body:'Ostrov JM, Gentile DA, Crick NR. Media exposure, aggression and prosocial behaviour during early childhood: A longitudinal study. Social Development. 2006;15:612-627'},{id:"B29",body:'Muller CW, Donnerstein E. Film-facilitated arousal and prosocial behavior. Journal of Experimental Social Psychology. 1981;17:31-41'},{id:"B30",body:'Baran SJ, Chase LJ, Courtright JA. Television drama as a facilitator of prosocial behavior: “The Waltons”. Journal of Broadcasting. 1979;23(3):277-284'},{id:"B31",body:'Udornpim K, Singhal A. Oshin, a pro-social media role model, in Thailand. Keio Communication Review. 1999;(21):3-21'},{id:"B32",body:'Wallbank J. Antisocial and prosocial behavior among contemporary Robon hoods. Personality and Individual Differences. 1985;6(1):11-19'},{id:"B33",body:'Carlo G, Hausmann A, Christiansen S, Randall BA. Sociocognitive and behavioral correlates of a measure of prosocial tendencies for adolescent. Journal of Early Adolescence. 2003;23(1):107-134'},{id:"B34",body:'Bandura A. The evolution of social cognitive theory. In: Smith KG, Hitt MA, editors. Great Minds in Management. Oxford: Oxford University Press; 2005. pp. 9-35. Available from: http://www.des.emory.edu/mfp/Bandura2005.pdf'},{id:"B35",body:'Caprara GV, Steca P. Prosocial agency: The contribution of values and self-efficacy beliefs to prosocial behavior across ages. Journal of Social and Clinical Psychology. 2007;26(2):218-239'},{id:"B36",body:'Macionis JJ, Plummer K. Sociology a Global Introduction. 3th ed. Harlow: Pearson Education Limited; 2005'},{id:"B37",body:'Preston JL, Ritter RS, Hernandez JI. Principles of religious prosociality: A review and reformulation. Social and Personality Psychology Compass. 2010;4(8):574-590'},{id:"B38",body:'Rokeach M. The Nature of Human Values. New York: The Free Press; 1973'},{id:"B39",body:'Syed Muhammad Naquib A. Prolegomena to the Metaphysics of Islam: An Exposition of the Fundamental Elements of the Worldview of Islam. Kuala Lumpur: International Institute of Islamic Thought and Civilization; 1995'},{id:"B40",body:'Sawai RP, Abdullah S, Baharudin DF, Ismail N. Penerapan nilai-nilai Islam melalui penggunaan media dalam pendidikan. In: Mustafa MZ, Rahim AM, Ahmad ZA, Ramle NA, editors. Representasi Islam dalam Media. Nilai: Universiti Sains Islam Malaysia; 2011. pp. 53-61'},{id:"B41",body:'Malhotra D. (When) are religious people nicer? Religious salience and the “Sunday effect” on pro-social behavior. Judgment and Decision making. 2010;5(5):138-143. Available from: http://journal.sjdm.org/10/10216/jdm10216.html'},{id:"B42",body:'Batara JBL, Franco PS, Quiachon MAM, Sembrero d, Dianelle Rose M. Effects of religious priming concepts on prosocial behavior towards ingroup and outgroup. Europe’s Journal of Psychology. 2016;12(4):635-644'},{id:"B43",body:'Sasaki JY, Kim H, Mojaverian T, Kelly LDS, Park IY, Janusonis S. Religion priming differentially increases prososial behavior among variants of dopamine D4 receptor (DRD4) genre. Social Cognitive and Affective Neuroscience. 2013;8:209-215'},{id:"B44",body:'Mohamad Rasit R. Structural equation modelling (SEM) analysis of influence of religious films on pro-social behaviour of audience. Asian Social Science. 2015;11(18):42-48'},{id:"B45",body:'Shah SS, Zehra AA. Religious faith, flourishing and self-efficacy in young adults: A correlational study. Global Journal of Arts, Humanities and Social Sciences. 2015;3(10):34-45'},{id:"B46",body:'Noornajihan J. Efikasi Kendiri: Perbandingan antara Islam dan Barat. Global Journal Al-Thaqafah. 2014;4(2):89-98'},{id:"B47",body:'Silva NM, Henrie JA, Patrick JH. Personality, negative social exchanges, and physical health among bereaved adults. Health Psychology Open. 2016;(1):-14. DOI: 10.1177/2055102916637877'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Rosmawati Mohamad Rasit",address:"rosmawati@ukm.edu.my",affiliation:'
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