The covered cells in each row have been introduced as hybrid BCI systems
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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There are several techniques for measuring brain activities such as magnetoencephalogram (MEG), near infrared spectroscopy (NIRS), electrocorticogram (ECoG), functional magnetic resonance imaging (fMRI), and electroencephalography (EEG). Each technique has some advantages and disadvantages compared to other techniques. For example, in EEG the temporal resolution is high but the special resolution is low compared to fMRI. Because of low cost and portability, EEG has been largely used in both clinical and research applications [1][2][3][4].
One of the EEG research applications is in a brain computer interface (BCI) system. A BCI can provide a new way of communications for special users who cannot communicate via normal pathways. A BCI system can send commands, controlled by brain activity and distinguished by EEG signal processing. There are many features which can be extracted from EEG, for example, six brain rhythms can be distinguished in EEG based on the differences in frequency ranges; delta (1- 4 Hz), theta (4-7 Hz), alpha (8-12 Hz), mu (8-13 Hz), beta (12-30 Hz), and gamma (25-100 Hz). The delta and theta rhythms occur in high emotional conditions or in a sleep stage. The alpha rhythm happens in awake and eyes closed relax condition. The oscillation in alpha rhythm has smooth pattern. The beta rhythm pattern is desynchronized and the condition is the normal awake open eyes. The gamma rhythm can be acquired from somatosensory cortex and mu rhythm from sensorimotor cortex.
BCIs are categorized based on the EEG brain activity patterns into four different types: event–related desynchronization/synchronization (ERD/ERS) [5], steady state visual evoke potentials (SSVEP) [6][7][8], P300 component of event related potentials (ERPs) [9], and slow cortical potentials (SCPs) [6][10]. The focus of this chapter is on P300, SSVEP and hybrid P300-SSVEP BCI systems.
Compared to other modalities for BCI approaches, such as the P300-based and the SCP BCIs, SSVEP-based BCI system has the advantage of having higher accuracy and higher information transfer rate (ITR). In addition, short/no training time and fewer EEG channels are required. However, similar to other BCI modalities, most current SSVEP-based BCI techniques also face some challenges that prevent them from being accepted by the majority of the population. Two important features of each BCI system are information transfer rate and required training time. A general comparison of different BCI approaches is shown in Figure 1.
A general comparison of SCP, ERD/ERS, P300, and SSVEP with respect to their training time and information transfer rate.
The process of detecting patterns from EEG is divided into three steps [11]: signal pre-processing, feature extraction and classification. The first step is to remove noise such as artifacts or power line noise which is added to EEG. So filtering is the first step in EEG signal pre-processing. Band pass and notch filters are the most common filters utilized in EEG signal filtering.
In the next step, features that are selected in feature extraction step and the type of classifier should be chosen based on the type of BCI. For example, for P300, time domain or time-frequency domain features such as wavelets are appropriate and for SSVEP BCIs frequency domain features are more appropriate. Classifiers such as Fischer’s linear discriminant analysis (FLDA), Bayesian linear discriminant analysis (BLDA), stepwise linear discriminant analysis (SWLDA), and support vector machine (SVM) are utilized [12][13] for P300 classifications. For SSVEP feature extraction and classification, different methods such as the Fast Fourier transform (FFT), the canonical correlation analysis (CCA), stimulus-locked inter-trace correlation (SLIC), and the common special patterns (CSPs) have been used [14][15] [16].
In recent years, the BCI research projects and the number of publications in this area have been increased rapidly [17]. Different areas of research such as new feature extraction methods, new classification techniques, new BCI paradigms, or new approaches for combining different BCI types have been investigated for improving accuracy, reliability, information transfer rate, and user acceptability. Combining different BCI types called a hybrid BCI is a new trend in BCI research which is the main focus of this chapter. In the next sections, the P300 and SSVEP BCI are explained and then different approaches for building a P300-SSVEP hybrid BCI are discussed.
Event related potentials (ERPs) are the measurement of brain responses to specific cognitive, sensory or motor events. One of the main approaches towards BCI is based on ERPs. P300 is a major peak and one of the most used components of an ERP. The presentation of stimulus in an oddball paradigm can produce a positive peak in the EEG, 300 msec after onset of the stimulus. The stimulus can either be visual, auditory or somatosensory. This evoked response in EEG is called P300 component of ERP.
The spatial amplitude distribution is strongest in the occipital region of brain and is symmetric around central location Cz recorded based on the 10-20 international system [18]. The spatial amplitude distribution of 10-20 international system and the electrodes that P300 is typically recorded from are shown in the following Figure 2. In terms of temporal pattern, P300 wave amplitude is typically in the range of 2 to 5 µV with duration of 150 to 200 msec as shown in Figure 3. Considering the P300 low amplitude relative to background activities of the brain (in the rage of 50 µV), it is clear that P300 detection requires special signal processing. One of the simplest approaches is ensemble averaging EEG over multiple responses to enhance P300 amplitude to identify it while suppressing background EEG activities.
Recoding of EEG based on 10-20 system and location of the electrodes typically used for P300 detection [18].
Temporal pattern of P300 component.
P300-based BCI has been used as one of the most widely used BCI systems since 1988 [1]. New advancements in inexpensive and portable hardware made it possible to have real-life application outside of laboratory environment [17][1][20][21][22]. P300-based BCI has been used from controlling a wheelchair for helping disable people to a virtual keyboard for spelling word and interacting with computers. This type of BCI systems possesses the potential to improve the quality of life.
P300-based visual speller paradigms are attracting much attention as they could provide means to communicate letters, words, and simple commands to computer directly from the brain. In the following sections, we will review the classical speller paradigm and discuss current and future trends in this area.
Processing and successful use of P300 wave in a BCI application requires several processing steps. First of all, the recorded EEG data have to be processed to reduce the effect of noise. A feedback mechanism is required where a visible signal is presented in the monitor correlated with the recorded signal. A pattern recognition or classification algorithm has to be developed to identify the P300 wave in the recorded ERP epochs. The algorithm parameters should be adjustable to adapt according to the change of user characteristics [11][17].
Figure 4 shows a typical BCI setting for speller application. Stimulus is presented by random flashing of the characters on the screen. This eventually evokes P300 wave in the recorded EEG. A signal processing technique performs the processing of P300 related information and the classifier contains the pattern recognition algorithm as described earlier [17].
A typical P300 BCI setup with visual feedback.
The classical paradigm for P300-based BCI speller was originally introduced by Farwell and Donchin in 1988 [1]. This Row-Column (RC) paradigm is the most popular speller format. It consists of 6 × 6 matrix of characters as shown in Figure 5. This matrix is presented on computer screen and the row and columns are flashed in a random order. The user is instructed to select a character by focusing on it. The flashing row or column evokes P300 response in EEG. The non-flashing rows and columns do not contribute in generating P300 [1]. Therefore, the computer can determine the desired row and column after averaging several responses. Finally, the desired character is selected.
A typical row/column paradigm [1].
It is interesting to note that P300-based BCI did not receive much attention when it was first proposed. However, recent trend is quite different where P300 BCI has emerged as one of the main BCI approaches. The researchers have focused on identifying the scopes of improvement of the traditional paradigm by introducing new ways of flashing, introducing colors, or investigating other ways to enhance the ERPs. Much focus has put on applying advanced digital signal processing techniques and classification methods in order to improve the classification results. Also, there have been several attempts to introduce new paradigms to evoke P300 potentials. Figure 6 shows such a different approach which is called single character (SC) paradigm that only single character is flashed instead of a row or column. The SC paradigm randomly flashes one character at a time with a delay between flashes [17]. The delay in SC speller is longer than the delay in RC speller. Though SC speller is slower than RC speller, SC speller can produce larger P300 amplitude [17].
Single character paradigm where each character is flashed [14][1].
Checkerboard (CB) speller is another paradigm proposed to overcome a problem associated with RC speller [17]. This drawback is arising from the distraction or inherent noise due to row/column association [17]. CB speller effectively reduces these two limitations as the characters are arranged in a checkerboard style as shown in Figure 7. CB speller also increases ITR [20].
Checkerboard paradigm [20].
The region-based (RB) paradigm was proposed by Fazel-Rezai et. al. in 2009 [21]. It is a two-level speller where the regions have to flash instead of rows and columns. In the first level, characters are placed in several regions (seven groups) as shown in Figure 8 [17][1][20][21]. The users are instructed to focus attention on a specific character in one of the seven regions. After several flashes the desired region is selected. In the second level, characters are distributed following the same rule used in the first level and each character flashes in similar order. After several flashes, the desired character is identified [21].
Region based paradigm where a set of characters in level 1 (E) are expanded in level 2 for spelling character “B” (F).
It is reported that RB speller has decreased the adjacency problem significantly [17][1][20][21]. The RB and CB paradigms show new directions in BCI speller paradigms apart from RC speller.
There has been much progress in bringing BCI technology out of lab environment to real-life applications. BCI has widely been studied in helping disable people, for example, enabling controlling a wheel chair using brain signals [22]. The other promising applications are in managing smart home environment, controlling a virtual reality environment, and next generation gaming [12].
Electrophysiological and neurophysiological studies have demonstrated increases in neural activity elicited by gazing at a stimulus [23]. Visual evoked potentials are elicited by sudden visual stimuli and the repetitive visual stimuli would lead to stable voltage oscillations pattern in EEG that is called SSVEP.
SSVEP is considered as a concept with two different definitions. Ragan [24] proposed that SSVEP is a direct response in the primary visual cortex. On the other hand, Silberstein et al. [25] assumed that the SSVEP includes indirect cortical responses via cortical-loops, from the peripheral retina, while a cognitive task is performed. SSVEP in this model has a complex amplitude and phase topography across the posterior scalp with considerable inter-subject variability. Although the main mechanism of SSVEP still is unknown, generally SSVEP is considered as a continuous visual cortical response evoked by repetitive stimuli with a constant frequency on the central retina. As a nearly sinusoidal oscillatory waveform, the SSVEP usually contains the same fundamental frequency as the stimulus and some harmonics of the fundamental frequency. For example, when the retina is excited by a visual stimulus at presentation rates ranging from 3.5 Hz to 75 Hz, the brain generates an electrical activity at the same and different frequency of the visual stimulus. The flickering stimulus of different frequency with a constant intensity can evoke the SSVEP in verity of amplitudes, ranging from (5-12Hz) as low frequencies, (12-25 Hz) as medium ones and (25-50 Hz) as high frequency bands [26]. This type of stimulus is a powerful indicator in the diagnosis of visual pathway function, visual imperceptions in patients with cerebral lesions, loss of multifocal sensitivity in patients with multiple sclerosis, and neurological abnormalities in patients with schizophrenia and other clinical diagnoses [26].
In addition to the usual clinical purpose of diagnosing visual pathway and brain mapping impairments, the SSVEP can serve as a basis for BCI. Recently, SSVEP BCI systems have gained a special place in the BCI paradigms continuum because of having a variety of different possibilities. SSVEP BCIs are useful in different applications, especially the ones that need some major requirements as follows [27]:
Large number of BCI commands is necessary (in SSVEP BCI limitations are mostly defined only by the design).
High reliability of recognition is necessary (in SSVEP BCI, patterns are clearly distinguishable by frequency).
No training (or just a short time training for classifier training) is allowed.
Self-paced performance is required.
A typical SSVEP-based BCI system uses a light-emitting diode (LED) for flickering. SSVEP responses can be measured within narrow frequency bands (e.g. around the visual stimulation frequency. Several numbers of stimuli can be implemented by using not necessarily a wide range of flickering frequencies, as the minimum detectable difference between frequencies is 0.2 Hz [27]. The occipital region is the area where this feature is generated more prominently [6]. The most wide-spread signal processing technique to extract the SSVEP responses of the brain from the raw EEG data is based on power spectral density (PSD) using FFT of a sliding data window with a fixed length. Template matching and recursive outlier rejection have also been used to show the feasibility of SSVEP BCI systems. Other methods which attempt to improve on robustness upon the FFT-based methods are autoregressive spectral analysis, and the frequency stability coefficient (SC) which has been shown to be better than power spectrum for short data windows; although training is necessary for building the SC model. Furthermore, CCA is also an efficient method for online SSVEP-BCI, as the required data window lengths are shorter than those necessary for power spectrum estimation.
Pastor et al.[28] studied the relationship between visual stimulation and SSVEP-evoked amplitudes, showing that the amplitude of SSVEPs peaks at 15 Hz, forms a lower plateau at 27 Hz, and declines further at higher frequencies (>30 Hz) as shown in Figure 9.
SSVEP amplitude with different flickering frequency [28].
In low-frequency stimulation, SSVEP detection is more accurate. In spite of its favorable detection properties, this band presents two major inconveniences [28],
According to visual perception studies, stimulation frequencies in this band are rather annoying and tiring for the subject
The risk for inducing photo epileptic seizures is higher for stimulation frequencies in the 15 – 25 Hz.
A simple solution could be in using higher stimulation frequencies. From empirical and subjective evidence, the threshold could be set to 40 Hz for low stimulation [28].
Ding et al. [23] demonstrated that a person’s attention level modulates his/her SSVEP. Since the SSVEP depends directly on the stimulation frequency of visual flickering, user’s attended target can be identified by analyzing the frequency contents in the induced SSVEP. By tagging different flickers with distinct flickering frequencies, subjects can shift their gaze to their desired flickers. These gaze targets can then be identified using the Fourier spectrum of the measured SSVEP signals. Middendorf et al. [29] designed a flight simulator controlled by two flickering lights that controlled leftwards or rightwards movement with a classification accuracy of 92%. Cheng et al. [30] implemented a SSVEP-based virtual keypad that achieved a mean ITR of 27.15 bits/min using twelve frequency-tagged flickering lights. Using two EEG electrodes positioned at the primary visual cortex, Kelly et al. [31] developed a method allowing participants to interact with a computer game.
Moreover, some visual BCIs have been developed as independent from users’ eye gaze. Allison et al. [6] investigated selective attention using overlapping stimulus to induce SSVEPs difference in an online control study. Zhang et al. [32] also modulated the SSVEP amplitude and phase response by means of shifting covert attention on two sets of random dots with distinct colors, motion direction and flickering frequencies in the same visual field. Trader et al. [33] compared the performance of the Hex-o-Spell and matrix design using covert attention. Their results demonstrated that the Hex-o-Spell is more than 50% better than those with matrix design with covert attention. This SSVEP-based BCI identifies user’s intended targets on calculated Fourier spectra. Nevertheless, the Fourier spectrum requires a time window (e.g., 1 or 2 sec) for computation to achieve sufficient frequency resolution in identifying two distinct gaze targets. Data segment with insufficient length in Fourier spectrum computation usually results in reduction of frequency resolution, which can limit the number of available targets in SSVEP-based BCI. Since BCI performance depends on accuracy and speed, a reliable method for extracting SSVEPs and recognizing gaze targets in an appropriate data segments is crucial.
It has been shown that the refreshing frequency, of a cathode ray tube (CRT) monitor can evoke a clear SSVEP. For SSVEP-based BCI development, the decoding accuracy is the most important factor, and a suitable stimulator is very crucial in this regard [34]. In previous studies, CRT flicker has been the most widely adopted stimulator, the LED flicker has only been reported in a small number of studies, and liquid crystal display (LCD) flicker has not appeared in the literature [23]. Since each of the three kinds of flicker can successfully evoke SSVEP, it is important to investigate the SSVEP differences that result from these different stimulators, and ascertain the type of flicker is most helpful in improving the accuracy of SSVEP-based BCI application. In the selection of the stimulating frequencies in a BCI application, one must ensure that the responses are as unique as possible. Thus, the stimulating frequencies are neither harmonics nor sub–harmonics from each other. From a practical point of view, the advantages of SSVEP BCI systems can be summarized as follows [34]:
User is allowed to have small eye movements.
User is capable of mild but sustained attention effort.
User’s visual system is not engaged in other activities.
Visual stimulation can be performed by usual equipment like computer display or LED panel.
Command delays of 1-3 s are allowed.
There are some obstacles for BCIs to be more applicable, such as reliability, BCI illiteracy [35], low ITR, and no satisfactory accuracy for all different subjects. In recent years, an extensive amount of work in BCI has been invested based on utilizing the combination of different types of BCI systems, or BCI and non-BCI, called hybrid BCI systems. Overcoming the limitations and disadvantages of the conventional BCI systems is the main goal of hybrid BCI. The focus and attraction toward hybrid BCI field has been extended in recent years. This is shown in Figure 10, based on the Scopus search engine [36], and the keyword ((“hybrid” AND (“BCI” OR “brain computer interface”))) and (“SSVEP” AND “P300”) and limited to “Engineering”, “Neuroscience” and “Computer Science” subject areas.
The increasing research trend in hybrid BCI area.
In general, the BCI systems can be combined in the way that, each system has separate input signal or the output of one system would be the input of the second system. The systems are called sequentially and simultaneously hybrid BCI, respectively [37]. Figure 11 shows a general block diagram of a sequential and a simultaneous hybrid BCI system. In sequential hybrid BCI, the first system mostly acts as a switch [37]. For this task, one of the appropriate options is SSVEP. SSVEP has high classification accuracy; high information transfer rate does not need training.
a) Sequential and (b) simultaneous hybrid BCI systems.
One of the main issues in this area is the optimum combination and selection of conventional BCIs. Several combinations of hybrid BCI systems have been introduced [37]. Conventional BCI systems are combined together based on the features of each system and the application of the hybrid BCI. If there are different tasks to be performed by the hybrid BCI, for each task, the more appropriate BCI can be chosen and, depending on the how the tasks are related to each other, the overall system can be combined. Some of the combinations for hybrid BCI that have been studied in recent years are shown in Table 1. Most of the studies in this area are focused on the combinations of BCI systems and few studies are on BCI and other physiological systems or devices.
\n\t\t\t\tPapers Numbers\n\t\t\t | \n\t\t\t\n\t\t\t\tBrain\n\t\t\t | \n\t\t\tNon-Brain(Non-Invasive) | \n\t\t|||||||||
\n\t\t\t\tNon-Invasive\n\t\t\t | \n\t\t\t\n\t\t\t\tInvasive\n\t\t\t | \n\t\t||||||||||
\n\t\t\t\tEEG\n\t\t\t | \n\t\t\t\n\t\t\t\tNon-EEG\n\t\t\t | \n\t\t||||||||||
P300 | \n\t\t\tSSVEP | \n\t\t\tERD | \n\t\t\tSCP | \n\t\t\tfMRI | \n\t\t\tNIRS | \n\t\t\tMEG | \n\t\t\tECoG | \n\t\t\tEOG | \n\t\t\tEMG | \n\t\t\tECG | \n\t\t|
[40] [39] [38] [41] | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t |
[42] | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t |
[43] | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t |
[44] | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t |
[13] [12] | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t |
[45] [46] [47] | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t |
The covered cells in each row have been introduced as hybrid BCI systems
In control applications, the BCI systems should be capable to cover multi-tasks. Hybrid BCIs has opened new opportunities for BCI systems to have more intense application in different areas. One of the areas in which hybrid BCI could play an important role is smart home control. There have been several studies done in this field [12][46]. The smart home or virtual environment systems are consisting of several stages and several control command types in each stage. In motor control systems, hybrid BCI shows improvement in accuracy and facilitates control tasks. Control commands, have different characteristics, and are divided to different types. Based on the characteristics, for each type, special types of BCI would be appropriate and for combination of control commands, two or more BCI types would fit. In discrete control commands, the task is the selection of one option from several options. P300 and Mu-Beta are more appropriate for this type of control commands. For series of the commands, continuous commands, ERD and SSVEP are more suitable. Also other features of the conventional BCIs should be considered, e.g., P300 is a slow responded system, but reliable. Mu-beta is fast responded, but not as efficient as P300.
The main enhancement that has been made by hybrid BCI is improvement in the applicability of BCI systems. As presenting two or more BCI type to the user, in the simultaneously combination, the user has the chance to get more efficient respond through utilizing the BCI type that is more appropriate for him or her. It also can decrease the fatigue, as the user can shift to another BCI option. It is shown that the accuracy is improved in the hybrid condition [42][43].
In [39], the hybrid BCI is introduced for Functional Electrical Stimulation (FES) control. Two tasks were considered to be implemented by the BCI systems; selecting one object among three objects and movement imagination to trigger FES. The high true positive rate (TPR) for SSVEP and ERD shows the capability of hybrid BCI for implementing several tasks that can be used in various control fields. SSVEP switch was introduced for smart home control [12]. The selection of control options, displayed on the screen is based on P300 BCI and SSVEP is operated as toggle switch. SSVEP was introduced as a switch for P300-based system [13]
Another BCI that can be introduced as a brain switch is ERS. In [41], post-imagery beta ERS-based brain switch was introduced for activating and deactivating the process of opening and closing the orthosis hand which was operated using SSVEP BCI.
More studies have focused on the simultaneous combinations of the conventional BCIs. As for one task, two or more BCI type are presented at the same time, the difficulty and complexity of performing the task is increased but, on the other hand, the accuracy in most of the cases increases for majority of the users. In addition, the fatigue may decrease, as users can switch between the BCI types that may be more comfortable for them. Another parameter is BCI illiteracy that can be decrease as users have the opportunity of accessing multi approaches [35]. The task of tracking the hint arrow was presented by SSVEP and ERD, and the accuracy was improved in the hybrid condition [38].
In another type of hybrid, more than one source of measurement is presented for one BCI type, for example, EEG and NIRS were acquired simultaneously for the ERD-based BCI [43] EEG and ECG were fused for motor imagery (MI) based BCI system[48]. EEG and EMG were utilized as hybrid in [44]. In this hybrid BCI, improvement in accuracy was shown. In some application areas, the tasks may be divided to two or more parts and each part is implemented by one BCI or non-BCI system. In this way, based on the features of the task, the system is selected. For example, in [42] control commands were divided to two parts and were implemented by EEG and electrooculography (EOG).
One of the issues in hybrid BCIs is that the system may be feasible but not optimum in all features. The hybrid BCI may improve the performance or accuracy but not compared to each of conventional BCI systems. For example, in the simultaneous combination of SSVEP and ERD, as in the conventional SSVEP, the accuracy is enhanced compared to ERD-based BCI system but not a lot changes compared to SSVEP.
P300 and SSVEP BCI were introduced as hybrid in an asynchronous BCI system in [13]. It seems that P300 and SSVEP combination works well as the stimuli for evoking both patterns can be shown on one screen simultaneously. The P300 paradigm considered in this study is a 6x6 speller matrix based on the original P300 row/column paradigm introduced by Farwell and Donchin [19]. Only one frequency is allocated for SSVEP paradigm. Background color was flashed with the frequency slightly less than 18 Hz. This facilitates the SSVEP detection. During the classification, P300 and SSVEP signals are separated by a band pass filter. The SSVEP is utilized as a control state (CS) detection, in the way that, when the user is gazing at the screen, the SSVEP is detected and it is assumed that the user intends to send a command. The system detects P300 target selection and CS simultaneously.
For SSVEP detection, the mean power spectral density (PSD) in the narrow band near the desired frequency and the PSD in the wider range near the desired frequency were utilized in an objective function (these values were subtracted from each other and divided over the PSD value from the wide band) and the function value was compared to a specified threshold. During the data acquisition, the channels for acquiring EEG signal were not fixed for all subjects. For P300 classification, FLDA or BLDA was utilized [14][15]. The experiment was presented as offline and online test. Ten subjects participated in the experiment. Subjects had training runs. In offline test, forty characters were presented for detection, divided to four groups. For better evaluation of SSVEP effect, two groups with and two groups without SSVEP were presented. In control state, subjects were instructed to count the number of time they distinguish the highlighted character. In non-control state (NCS), subjects were instructed to do a mental task like multiplication of two numbers and relax with closed eyes. For four out of five subjects, the accuracy was improved inconsiderably during the presence of SSVEP and P300 detection was not determinate. Between ten characters detection, there was a break and the time of the break depends on the time subjects pressed a keyboard button, and an auditory cue alerted about the finish of NCS time. The average classification accuracy of 96.5% and control state detection accuracy of 88% with the ITR of 20 bits/min were achieved during the offline test. The online test was presented under the semi synchronous condition. The experiment was consisted of blocks with 5 rounds, for detecting each character. SSVEP detection for at least three out of five runs showed the control state detection by the subject and P300 was detected during the control state. If the control state was not detected, the ‘=’ character was shown on the screen. The break time and the auditory alert was the same as offline test. The average control state detection accuracy of 88.15%, the classification accuracy of 94.44% and the ITR of 19.05 bits/min were achieved during the online test. P300 and SSVEP combination was also introduced to control smart home environment in [12]. P300-based BCI was used for controlling the virtual smart home environment and SSVEP was implemented as a switch for the P300 BCI operation. Results from this experiment show that P300 is suitable for discrete control commands and SSVEP is suitable for continuous control signals. The hybrid BCI achieved high accuracy and reliability in all subjects. In this chapter, P300, SSVEP and the hybrid P300 and SSVEP BCI systems were reviewed. The new trend and direction in BCI systems is to use new approaches in stimulating brain patterns such as hybrid BCIs while keeping the system complexity low and user acceptability high.
Today, information has become the main component of what we produce, do, buy, and consume. Having an economic value in almost all products and services that meet the needs of today’s societies, it has been now obligatory for individuals and organizations to obtain information technologies and to actively use them in both work and social life domains. Hence, in the current information age, where information is seen as power, this situation has made it imperative for organizations to become increasingly information-based and to benefit from information technologies in many processes and activities.
The intensive use of information technologies in many functions and processes has also required some changes in organizations [1]. This is due to the fact that information technologies, unlike traditional technologies, do not only change the technical fields but also affect the communication channels, decision-making functions and mechanisms, control, etc. [2]. Consequently, one of the most striking developments is on organizational structures that are becoming increasingly flattened and horizontal. Relatedly, information technologies have begun to take over the role of middle management, which supports decision-making processes of senior management and has reduced the importance of this level [3, 4, 5]. Similarly, while information technologies enable managers to obtain faster, more accurate, and more information [6, 7, 8], it also provides lower-level managers with more information about the general situation of the organization, the nature of current problems, and important organizational matters [9, 10, 11, 12].
Moreover, information technologies also have an important potential in determining whether organizations have a mechanical or an organic structure [13]. Within the mechanical organizational structures, people do not have much autonomy, and behaviors expected from employees are being careful and obedience to upper authority and respect for traditions. In such organizations, predictability, consistency, and stability are desirable phenomena. In contrast, people in organic structures have more freedom in shaping and controlling their activities, and being enthusiastic, creative, and taking risks have important places among the desired behaviors [14].
Accordingly, information technologies begin to influence the cultural values of the organization over time, through these transformations they create on organizational structures, processes, and operations. In other words, the fact that organizational structures are mechanical or organic causes the formation of diverse cultural values in organizations [15]. Therefore, the desired cultural values in mechanical organizations are quite different from those in organic structures [1, 16, 17]. In this context, this chapter deals with the influences of information technologies on cultural characteristics of organizations along with the reflections of the use of these technologies on organizational structures and their functioning.
When we look at studies on the relations between organizational culture and information technologies, we generally see the studies on the effects of culture on technology adaptation or use [18, 19, 20, 21], as well as on the effects of certain specific information technologies and applications (e.g., e-mail use, group support practices, etc.) on some aspects of any organizational culture [22, 23, 24, 25, 26, 27, 28, 29, 30, 31]. However, the number of studies that consider the use of information technologies as a “whole” and that address “why” and “how” its effects on organizational culture occurred is still limited. And so, this chapter aims to examine and discuss the overall effects of the usage and intensity of information technologies established in organizations on the cultural life within.
In this context, the chapter plan is as follows: Firstly, the basic concepts related to information and information technologies are included. Emphasis is placed on the meaning differences between knowledge and information, and their connections to information technologies are tried to be explained briefly. Secondly, the effects of information technologies on organizational structure are given particular attention. The reason for this is that as a system of values, beliefs, assumptions, and practices [32], organizational culture encompasses many features closely related to structures of organizations. Thirdly, possible links between organizational structure and organizational culture are included. Fourthly, important theoretical approaches and studies on the relationships between information technologies and organizational culture are provided. Finally, by deepening a bit more and by emphasizing key points, some important arguments are discussed.
In the literature, the concepts of information and knowledge are sometimes expressed by a single term, “information.” However, although the concepts of knowledge and information are intertwined, they are two different concepts that have different meanings and describe different phenomena. The reason for this is that knowledge is also included in the concept of information as it is transformed into a commodity when it begins to be processed, stored, and shared by information technologies.
Becoming the basic elements of today’s economic, social, and cultural systems, information is obtained in a certain hierarchy. The images are at the beginning of the process, and the process is completed with a hierarchical staging in the form of data, information, and knowledge, respectively [33]. Image is located in the first step of the process. Humans copy the picture of any object and event they previously perceived by sensory organs. When faced with a similar phenomenon in the later stages of life, these pictures in the mind are redesigned. We call these pictures of realities occurring in the human mind as images [33]. The next stage, the data, contains symbols that represent events and their properties. For this reason, data are expressed as figures and/or facts without content and interpretation [34]. Information that constitutes the next stage of the process and is mixed with knowledge and used interchangeably is expressed as a reporting of one system’s own status to another system [33]. In information, associated data are combined for a specific purpose. Therefore, we can explain information as meaningful data [35]. Knowledge, on the other hand, is defined as personalized information that allows people to fully and accurately grasp what is happening around them and manifests itself in the form of thoughts, insights, intuition, ideas, lessons learned, practices, and experiences [36]. According to Kautz and Thaysen [37] who stated that knowledge is found only in the people’s minds, knowledge is, therefore, a subjective formation. In other words, knowledge is the form of information enriched with interpretation, analysis, and context [38]. However, here, it should be emphasized again by highlighting a very important issue that knowledge is also accepted as information when this knowledge begins to be processed, stored, shared, and used over information technologies. Therefore, after this, when talking about information, one should consider not only the information created by the data brought together in a meaningful way but also the knowledge shared and used over information technologies.
On the other hand, information technologies, used as the most important tool of generating value today, are defined as the technologies that enable processes such as recording and storing data, producing information through certain operational processes, and accessing, storing, and transmitting this produced information effectively and efficiently [39, 40, 41, 42, 43, 44, 45, 46]. The term information technologies is used to cover computer and electronic communication technologies, as they are now inseparably intertwined in literature and everyday use and are generally used in this way [47]. In this context, data processing systems, management information systems (MIS), office automation systems, executive support systems, expert systems, intranet and extranet, electronic mail (e-mail), group applications (groupware), database management systems, decision support systems, artificial intelligence, and telecommunication systems can be given as examples of information technologies [33, 48, 49].
Towards the end of the twentieth century, the rapid changes with the impact of developments in information technologies led to the emergence of customer satisfaction-based, learning, knowledge-based, and constantly changing organizations [50]. The fact that organizations have become considerably information-based and benefit from information technologies intensively in their activities and processes has made also the changes in their organizational structures mandatory [1]. Accordingly, the effects of information technologies on organizational structure will be summarized under the subtitles of differentiation, centralization, and standardization/formalization, which are the three main components of organizational structure [15].
Differentiation within an organization occurs in three ways: Specialization/division of labor, horizontal and vertical differentiation, and hierarchy and size [15]. Specialization refers to the amount of different expertise or types of work [51, 52]. Specialization generally increases the number of subunits and makes it harder to understand the larger structure that people contribute to with their skills and expertise [53]. Information technologies have the potential to reduce this tendency by providing more access to information and experts at this point. In this way, access to information resources provides synergy [54].
Vertical and horizontal differentiation refers to the amount of hierarchical levels in an organization [55]. Information technologies, with the support of problem solving and decision-making, lead to the emergence of more flattened organizational structures as they require fewer levels within the hierarchy [56]. Since information technologies give employees in lower positions more autonomy to harmonize their activities, this can allow them to find and try better methods while performing their work. In this context, we can increasingly see that organizational structures have become horizontal and strengthened and that virtual organizations have begun to emerge as the most cost-effective structure [17].
In terms of hierarchy and size, Heinze and Stuart [4] argue that the mid-level management staff is unnecessary, increases bureaucracy, reduces efficiency, and has no function in organizations any more. Since most of the tasks performed by mid-level executives can be fulfilled by computers, both less costly and faster, information technology has begun to take over the role of mid-level management, which supports the decision-making process of senior management [5]. Sharing the same opinion, Fulk and DeSanctis [57] also stated that the largely witnessed situation in modern organizational designs is the reduction of intermediate-level managers and administrative support.
Centralization points to the extent to which decision-making power within an organization is scattered or centered [58]. Due to increasing local and global competition, many companies have started to leave their strategic decision-making task further down the organization to benefit from the expert people with more precise and timely local knowledge [10]. Information technologies affect these efforts directly in two ways. Firstly, information technologies increase local knowledge by contributing to obtaining closer information about market trends, opportunities, and customers. Secondly, information technologies can create synergies for organizations because, thanks to information technologies, communication and coordination between distributed decision makers, central planners, and senior managers can be realized more effectively and efficiently [59].
However, whether information technologies will lead to centralization or decentralization is a very controversial question. Regarding centralization, it enables managers to acquire faster, more accurate, and more information, reduces uncertainty, and allows them to make decisions that they cannot make before [6, 7, 8]. Conversely, by the use of other forms of information technologies (e.g., electronic bulletin boards), decentralization provides more information to lower- and mid-level managers about the general situation of the organization and the nature of current matters and problems [9, 10, 11, 12]. Raymond et al. [60] argued that because information technologies facilitate the use and transmission of information by all levels and units in the organization, it enables top management, which is the decision authority, to be disabled in certain areas and the decentralization of control. Thach and Woodman [61] maintained that this is due to the fact that as a result of sharing information at lower levels with the help of information technologies, this power of senior management has decreased to a certain extent, and the knowledge and participation of the staff in organizational matters have increased.
The literature shows that information technologies allow both centralization and decentralization. Researchers are in the agreement that information technologies make it possible for organizational managers to leave their decision-making power to a large part of the hierarchical levels without compromising the quality and timeliness of the decision [62, 63]. Keen [64] combined the concepts of centralization and decentralization and used the term “federated organization” in which organizations do not have to choose either because information technologies simultaneously allow centralization-decentralization [64, 65].
Formalization is the process of detailing how activities are coordinated for organizational purposes in order for employees and organizational units to respond routinely to recurring situations [51, 66]. Formalization involves rules, instructions, shared values, and norms [67]. In fact, formalization is based on the objective of more efficiency and less uncertainty [13].
Information technologies provide the ability to reduce the negative effects of formalization by facilitating the documenting and retrieving of information on organizational occurrences and endeavors that make behaviors and processes more consistent through formalization [63]. The more information technologies assist in reducing search times and preventing downtime, the more the administrative cost of formalization decreases and the productivity increases, which ultimately benefits the path to innovation [68].
Different organizational structures lead to the development of different cultural values [15]. The fact that the structure which an organization has established to control its activities and is defined as a formal system consisting of duties and authority relations is mechanical or organic causes the emergence of completely different cultural values, rules, and norms [69]. While mechanical structures are vertical, highly centralized, and almost everything in them are standardized, organic structures are horizontal, decentralized, and based on mutual adaptation [14]. People feel relatively less autonomous in vertical and centralized organizations, and being careful, obeying the upper authority, and respecting traditions are among the desired behaviors. Therefore, in a mechanical organizational structure, there are cultural values where predictability and stability are important [69]. In contrast, in horizontal and decentralized organizations, people can freely choose their own activities and control them. Creativity, courage, and risk-taking are given importance as desired behaviors. Therefore, organic structures contribute to the formation of cultures that value innovation and flexibility [15].
Organizational structure is also important for the development of cultural values that support integration and coordination. In a structure with stable task and role relations, sharing of rules and norms is more since there will be no communication problems and the information flow will be fast [70]. In organizations where the sharing of cultural values, norms, and rules is at a high level, the level of performance also increases [15]. Particularly in team or matrix structures where face-to-face communication is intense, the sharing of these cultural values and common reactions to the problems develop more rapidly [9].
Whether an organization is centralized or not causes different cultural values to emerge. In decentralized structures, authority is divided into subordinate levels, and an environment is created for the formation of cultural values in which creativity and innovation are rewarded [13]. Employees are allowed to use the organization’s resources and work in projects that they want, by spending some of their time in these projects, thus contributing to the production of innovative and creative products and services [15]. The structures of such organizations constitute the cultural values that give their employees the message “as long as it is in the interest of the organization, it is okay to do things in an innovative and the way you want.”
Conversely, in some organizations, it may be more important for employees not to decide on their own and all activities to be followed and controlled by their superiors. In such cases, a centralized structure is preferred to create cultural values that will ensure accountability and obedience [71]. Through norms and rules, all employees are expected to behave honestly and consistently and inform their superiors about wrongs or mistakes, because this is the only acceptable form of behavior within these structures [72].
Since working on the factors that determine the consequences of the adoption and use of information technologies, researchers have focused on people’s beliefs, values, assumptions, and codes of conduct. As a result, they have given names to this research field such as “socio-technical systems,” “social system,” “social structure,” and most recently “culture” [73]. For example, Markus and Robey [23] using “social elements” and Barley [26] using “social system” or “social structure” tried to explain this phenomenon. When examined more closely, it is seen that the details that these authors emphasize while depicting the case are the assumptions, beliefs, and values that exist in common among the group members, and this corresponds to the definition of organizational culture.
Research examining the relationships between information technologies and values, beliefs, and norms belonging to a particular group has gone through certain stages and used rich and complex research models to explain the relationships in each of these stages [74]. In the first studies on information technology applications, it has been suggested that information technologies cause changes in various organizational phenomena including structural features and thus have certain effects on organizations [74]. For instance, in some studies on adoption of groupware software, several researchers have used this deterministic approach to describe how groupware use affects communication and collaboration among employees and their productivity [27, 28]. These studies assume that certain results will certainly emerge after the adoption of information technologies, without considering the motives or activities that shape the use of information technologies by managers and employees. Like much more deterministic studies, these authors often assumed that information technologies would have predetermined influences on the adoption of information technologies, regardless of the environment in which information technologies were applied, how they were applied, and the users’ specific behaviors and particular purposes.
The second group of views concerning the relationships between organizational culture and information technologies includes the fact that information technologies are seen as a tool that can be used for any change that managers desire to make in organizational practices [22]. In studies in this approach, researchers believe that there is a wide range of possibilities to identify changes in organizational culture, structure, processes, and performance [22, 75]. Researchers from this tradition presume that with the right choice of information technologies and appropriate system design, managers can achieve whatever goals they desire.
These works were mostly adopted in the 1980s and reflect a perspective that managers think can manipulate organizational culture in the way they want. Often called “management and control,” “a functional or instrumental approach” to organizational culture, this methodology has caused serious debate in the literature [76]. This approach attributes great powers to the management level in this regard, which conflicts with anthropologists’ views that culture cannot be consciously controlled and goes much deeper to understand it [76]. Robey and Azevido [77] also do not accept the rational thought on the assumption that culture can be manipulated directly in this way.
Studies with this rational perspective in the information technology literature assume that managers can use information technologies as a leverage to make changes in the norms of behavior, strategy, structure, and performance among members within the organization. For example, in studies on group support systems (GSS), we find managers’ beliefs that they can use collaborative technologies to create a more cooperative organizational culture. This perspective was not accepted by Karsten [78] and some experimental research on GSS [30, 79]. Organizational necessity is no longer accepted, as it is viewed by information technology researchers as an overly simple approach [23, 80].
Researchers who take another approach suggest that information technologies and organizational culture can interact with each other to produce various results [22, 23]. These results can be in the form of adoption and effective use of information technologies (if there is a harmony between organizational culture and information technologies) or user reluctance, refusal, or sabotage (if no fit). Researchers who have been working on information systems since the 1980s have focused on understanding information technology features and functionality that cause effective or problematic information technology applications and the interaction between users’ values, assumptions, and other elements of organizational culture. In this regard, Romm et al. [81] argued that many forms of information technologies comprise cultural assumptions embedded within themselves and these assumptions may conflict with existing values of a particular organization. The authors argued that these embedded assumptions present information technologies as a “cultural boundary” and that a cultural analysis should be made to predict compliance or incompatibility. The authors in this approach warn managers to think of organizational culture as a binding limitation in information technology applications. In a warning by Pliskin et al. [76], managers are advised not to try to change the culture of the organization. Regarding this issue, Orlikowski [30] cites Lotus Notes (a group software) application at Alpha Corporation, a consultancy company. In this example, this system, which was established by the CEO of the company only with the benefits to be obtained, did not create the expected effects, became unsuccessful, and disappointed due to reasons such as no cultural analysis and inadequate training. Employees responded to the use of Notes with resistance and refrained from using it. The reason for this was that the employees in this organization, which had a competitive culture where information was seen as a power, avoided sharing information with others. As a result, this incompatibility between the collaborative culture that Notes had in itself and the competitive culture of the organization in question had failed this application of information technologies.
In a different approach, it is stated that information technologies and culture are not fixed and they are more flexible in terms of change [23, 75]. Managers in this approach may set specific goals for the use of information technologies, but actual results of the use of information technologies are not deterministic, and results cannot be predicted or controlled even under the best conditions [23]. The effects of information technologies are not deterministic because technology has interpretable flexibility considering that it can have different meanings for different employees. Similar technology can be interpreted in a different way by distinct people, based on certain assumptions, beliefs, and values. Robey and coauthors [24, 25], for instance, showed that it would be an empty attempt for organizational managers to try to intentionally manipulate the effects of these technologies, since there are many ways that diverse employees can configure a particular technology in different social environments.
Gopal and Prasad [31] also achieved similar results in their work on group support system (GSS), claiming that for researchers seeking fixed laws or regulations on how information technologies affect user behaviors, this would be an impossible goal to pursue. Conversely, the results of using information technologies depend on the symbolic meanings that information technologies have for a particular user. This work of Gopal and Prasad [31] expresses similar results with the work of Barley [26] and Robey and Sahay [25]. The authors stated that the symbolic meanings of certain technologies for users affect their perceptions of information technologies and their specific behaviors.
In the light of the above-mentioned approaches, arguments, and important studies in the literature, it will be useful to discuss some important points by deepening a little more and by emphasizing the key features related to the concepts of information, information technologies, and organizational culture.
First, organizational culture is a complex phenomenon that develops and changes in a historical process [32, 82, 83]. Thus, although it might seem like a plain and simple concept, organizational culture includes many subdimensions and processes. When considered as a complex pattern of these interactions of many factors with each other, it is also a difficult process to identify the direct and indirect effects of information technologies on organizational culture within this cluster of relationships and interactions. Moreover, culture is not a phenomenon that changes and develops in a short time and is therefore open to manipulations of managers. On the contrary, from this point of view, it is not possible to easily achieve control over cultural changes, and it is necessary to go much deeper [76]. So, it is not rational to expect that the rapid developments and changes in information technologies will cause changes in cultural characteristics at the same speed. In this sense, it could be inaccurate to seek direct relationships between two phenomena in question, whose rates of change are quite different.
Second, for cultural changes, there must also be changes in the basic assumptions, beliefs, and values on which the culture is built [84]. It would be misleading to expect little or intensive use of information technologies to cause changes in these rooted assumptions. For the desired changes in these basic assumptions, beliefs, and values, it is necessary to design the structure accordingly, to recruit employees who are qualified for the targeted culture, and to set ethical values and property rights to employees in accordance with this culture [15]. In this sense, information technologies may only catalyze the contribution of organizational structure to organizational culture.
Third, there are many and different types of hardware and software that fall under the scope of information technologies. It is not logical to accept all of them as homogeneous technologies in all aspects (with the same functions and features, similar usage areas, standard conditions they are applied, similar intentions, and behaviors of all users), and it can be, therefore, misleading to carry out research under a single “IT” concept from this perspective. The reason for this is that, as stated in the sections above, cultural features of each information technology application or product embedded in it might be different. The interactions between the cultural characteristics of the environment in which information technologies are applied and the unique cultural contents of information technologies may cause different results on the culture of the organization.
Fourth, contrary to what is believed, some of cultural features that we anticipate to support information technology applications and products may be interpreted otherwise by diverse people contingent on different assumptions, beliefs, and values. In fact, Robey et al. [24, 25] showed that managers cannot control the effects of these technologies, since different users can configure a particular technology in numerous ways in different social environments. Also, Gopal and Prasad [31] argued that this would be an impossible achievement for researchers looking for fixed laws or regulations on how information technologies affect user behaviors.
Fifth, information technologies were defined above as technologies that enable processing, storage, and sharing of information. The key concept in this definition is “knowledge-based” information and not the technology itself. Therefore, what makes information technologies essential and important is the information itself. According to the definition of knowledge, the most significant characteristic that differentiates it from information is its being a product of the human mind [37]. Because knowledge is the interpretation of information and expresses the value produced from it, qualifying information technologies as good-bad, useful-useless, and necessary-unnecessary can be a meaningless evaluation. So, the basic thing that creates value-added for organizations is not the technology used but the information itself, which is processed, stored, and shared on this technology. In this context, even if it is the latest, most advanced, and most expensive technology in the world, if the organization does not have a qualified human resource capable of producing knowledge that will create value-added, an appropriate organizational structure and culture that will activate this creative potential, and a management approach, all investments in these technologies will also be wasted.
This chapter has aimed to examine the impacts of information technologies on organizations’ cultures, and for this purpose, a special emphasis is given to the concept of “organizational structure” within the theoretical framework presented above. The most important reason for this is that relevant literature shows that organizational culture and organizational structure are in a very close relationship. Indeed, when the question items in the Denison organizational culture scale [85], which is the most frequently used in the literature, are examined, it is possible to see that most of these items point to many features of organizational structure concerning centralization, formalization, and differentiation dimensions. Therefore, it is a very rational approach to expect that information technologies can have direct and indirect effects on organizational cultures based on the influences of information technologies on structures of organizations. However, it should be underlined that different and controversial approaches and findings in the literature mentioned above on the relations between information technologies and organizational culture generate question marks in the minds as well.
In this regard, it is already quite difficult to draw a clear picture of the impacts of information technologies on cultural characteristics of organizations. The number of studies on the subject in the literature is still very limited. Accordingly, it is necessary to underline the great need for interdisciplinary studies in this field. But still, this study argues that the main factor that determines the actual impact and value of information technologies, which have become an integral part of human life in today’s world, is the information itself rather than technology, and it should be kept in mind that information technologies can only function as a means or tool in this knowledge-based social, economic, and cultural life. In other words, the determinant of the benefits, meaning, and importance of information technologies might be the conditions created by organizational factors such as cultural environment and organizational structure where knowledge is created, developed, and used and human resources have become the most important capital element and source of wealth.
The author declares no conflict of interest.
IntechOpen implements a robust policy to minimize and deal with instances of fraud or misconduct. As part of our general commitment to transparency and openness, and in order to maintain high scientific standards, we have a well-defined editorial policy regarding Retractions and Corrections.
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\\n\\nA Correction will be issued by the Academic Editor when:
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\\n\\n4. FINAL REMARKS
\\n\\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\\n\\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
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\n\nA Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
\n\nA formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\n\nPublishing of a Retraction Notice will adhere to the following guidelines:
\n\n1.2. REMOVALS AND CANCELLATIONS
\n\n2. STATEMENTS OF CONCERN
\n\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\n\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
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\n\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n3.2. CORRIGENDUM
\n\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n4. FINAL REMARKS
\n\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\n\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\n\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
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