\r\n\tSome studies should be linked to the late-stage tumorigenesis promoting metastasis in cancer. In addition, deregulated cellular processes such as cell proliferation, apoptosis, and differentiation as related to different tumor types should be investigated in this book. Besides tumorigenesis, spontaneous tumor regression and its potential formation mechanisms should be reviewed or researched. In addition, the role of the deregulated immunity in tumorigenesis should be explored. The drug targets and treatment alternatives in various cancer types should be described or investigated in some studies. The studies relating to the laboratory tests used as diagnostic and prognostic in cancer patients should also be presented. Consequently, this book may include but is not limited to these topics.
",isbn:null,printIsbn:null,pdfIsbn:null,doi:null,price:0,priceEur:null,priceUsd:null,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"46d3363b21f482c9a22ba72cca9ec4c0",bookSignature:"Dr. Nevim Aygun",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/6919.jpg",keywords:"Tumorigenesis,clinical significance, biological/genetic features, genomic/chromosomal instability, prognosis, prognostic factors, tumor suppressor genes, promotion of metastasis, spontaneous regression, tumor stages, tumor types/subtypes, signaling pathways, signaling networks, deregulated cellular processes, immunity, diagnosis, laboratory tests, treatment , oncogenes, primary tumor progression",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 26th 2018",dateEndSecondStepPublish:"April 16th 2018",dateEndThirdStepPublish:"June 15th 2018",dateEndFourthStepPublish:"September 3rd 2018",dateEndFifthStepPublish:"November 2nd 2018",remainingDaysToSecondStep:"3 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"195365",title:"Dr.",name:"Nevim",middleName:null,surname:"Aygun",slug:"nevim-aygun",fullName:"Nevim Aygun",profilePictureURL:"https://mts.intechopen.com/storage/users/195365/images/system/195365.jpeg",biography:"Nevim Aygun received her Medical Biology and Genetics Ph.D. in Health Sciences. She is interested in cancer, molecular biology, human genetics, cytogenetics, molecular cytogenetics, genomics, and bioinformatics. She has participated in many research projects on neuroblastoma, human gross gene deletions, non-B DNA-forming sequences, solid tumors, HCV, and leukemia, resulted in six articles, one book chapter, and numerous reports. She performed many molecular biological methods: PCR, real-time PCR, bacterial transformation, plasmid vector transfection, RNA interference, fluorescence in situ hybridization (FISH), cytogenetic, DNA sequencing, and cell culture. She also performed genomics and biostatistics analyses using some bioinformatics tools and SPSS program. She reviewed several manuscripts for some medical, genetics, and genomics journals. 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This requires extensive analysis of developing trends in scientific research in order to offer our readers relevant content. Creating the book catalogue is also based on keeping track of the most read, downloaded and highly cited chapters and books and relaunching similar topics. I am also responsible for consulting with our Scientific Advisors on which book topics to add to our catalogue and sending possible book proposal topics to them for evaluation. Once the catalogue is complete, I contact leading researchers in their respective fields and ask them to become possible Academic Editors for each book project. Once an editor is appointed, I prepare all necessary information required for them to begin their work, as well as guide them through the editorship process. I also assist editors in inviting suitable authors to contribute to a specific book project and each year, I identify and invite exceptional editors to join IntechOpen as Scientific Advisors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"56934",title:"Audiovisual Feedback Devices for Chest Compression Quality during CPR",doi:"10.5772/intechopen.70742",slug:"audiovisual-feedback-devices-for-chest-compression-quality-during-cpr",body:'\nThe sequence of actions linking a victim of out-of-hospital cardiac arrest with survival is described by the chain of survival, which consists of four independent links: early activation of the emergency medical services, early cardiopulmonary resuscitation (CPR), early defibrillation, and early advanced care. The four links of the chain of survival are important, but early CPR and early defibrillation are pivotal for a successful outcome of the patient [1]. CPR consists of cycles of chest compressions and ventilations delivered to the patient to artificially maintain a minimal flow of oxygenated blood to the vital organs, whereas defibrillation consists in the passage of electrical current through the myocardium (cardiac muscle) to terminate certain lethal arrhythmias. In out-of-hospital settings, early defibrillation is normally procured using an automated external defibrillator (AED).
\nThere is a strong evidence that the quality of chest compressions is related to the chance of successful defibrillation [2–4]. Current resuscitation guidelines [1] emphasize the importance of providing chest compressions with an adequate depth (between 5 and 6 cm) and rate (between 100 and 120 compressions per minute [cpm]), completely releasing the chest between compressions and minimizing interruptions. However, several studies have shown that both professionals and laypeople often apply CPR at improper rates and depths [5, 6].
\nIn an effort to alleviate this problem, since 2010, resuscitation guidelines recommend monitoring CPR quality and using metronomes and real-time feedback systems to guide rescuers during resuscitation attempts [7]. Metronomes generate regular audible beats that help rescuers to follow the rhythm, while feedback devices are more sophisticated; they measure CPR performance in real time and provide audiovisual messages to guide the rescuer toward target depth and rate. The clinical studies conducted to date had an insufficient power to demonstrate improved survival with the use of feedback devices [8]. As a consequence, ERC guidelines 2015 recommend the use of CPR feedback devices as part of a broader system of care that should include comprehensive CPR quality improvement initiatives, rather than as an isolated intervention. There is, however, strong evidence that feedback improves chest compression quality, [9–12] which has been linked to survival from cardiac arrest [5, 8].
\nThis chapter explores new alternatives to provide feedback on the quality of chest compressions during CPR. First, we briefly describe the history of feedback devices and the different technologies used. Then, we present three methods to provide feedback on chest compression depth and rate based solely on chest acceleration. One of the methods presented particularly a high accuracy in a wide range of conditions and is further discussed in three challenging scenarios. Finally, we assessed the feasibility of using the transthoracic impedance (TI) signal acquired through defibrillation pads to provide feedback on chest compression depth and rate.
\nThe first CPR feedback devices were mechanical and used force or pressure sensors to provide feedback on chest compression depth [13]. Devices in this category include CPRplus (Kelly Medical Products, Princeton, USA), CPREzy (Health Affairs, London, England), and the more recent Cardio First Angel (Schiller, Baar, Switzerland). These systems guide the rescuer toward the target depth based on the force applied on the chest for each compression. However, stiffness of the chest is not linear [14] and varies among individuals. Tomlinson et al. [15] simultaneously measured compression force and depth in 91 adult out-of-hospital cardiac arrest patients. In the studied population, the force required to achieve 38 mm varied from 10 to 54 kg. Even if some of the devices in this category take into account the approximate size of the patient, the wide variation in chest wall elasticity and its changes with time impede an accurate estimation of compression depth from compression force.
\nTo overcome the limitations of force and pressure sensors, electronic systems based on accelerometers were developed. These devices sense the acceleration of the patient’s chest during CPR, and they process it in real time to obtain compression depth. By definition, acceleration is the first derivative of velocity with respect to time, and velocity is the first derivative of displacement. Consequently, chest displacement can be obtained from acceleration by applying double integration. However, integration is an inherently unstable process: small integration errors rapidly accumulate causing a significant drift in displacement that impedes accurate estimation of the compression depth. Figure 1 illustrates the problem of double integration of the chest acceleration with a record acquired while chest compressions were provided to a resuscitation manikin. The acceleration signal (top panel) and the reference compression depth signal obtained from a displacement sensor placed inside the manikin’s chest (bottom panel, solid line) were registered. The second panel shows the reference velocity signal computed differentiating the reference compression depth signal (solid line), and the velocity signal computed by numerically integrating the acceleration signal (dashed line). Integration errors quickly accumulate, and during the last seconds, the computed velocity presents a noticeable offset with respect to the reference signal. When numeric integration is performed again, this offset leads to big errors in the computed displacement (bottom panel, dashed line), of more than 20 cm after only 8 s in this example.
\nIntegration errors in the displacement signal after the application of direct double integration to the acceleration signal.
A possible strategy to reduce the accumulation of integration errors would be to perform the integration for small signal segments, for example, for each compression cycle. For that purpose, the offset of each chest compression should be first identified, and the integration should be reset by applying boundary conditions after each cycle, that is, setting velocity and displacement at those points to zero. Over the last decade, several mechanisms to identify the offset of chest compressions have been conceived, giving rise to complex commercial devices that incorporate additional sensors or use elaborate signal processing techniques. For example, PocketCPR (Zoll Medical, Chelmsford, USA) applies signal processing techniques to set boundary conditions and compensate integrating drift, while CPRmeter (Laerdal Medical, Stavanger, Norway) incorporates an additional force sensor. Both devices are rigid and must be placed between the chest of the patient and the rescuer’s hands during CPR to measure chest acceleration.
\nMore recently, Physio-Control (Redmond, USA) presented TrueCPR, a solution to provide feedback on chest compression rate, depth, and chest release based on triaxial magnetic field induction. The device comprises two rigid pads: one of them is positioned between the rescuer’s hands and the chest of the patient during CPR, and the other one, longer and flatter, beneath the patient’s back. Feedback metrics are estimated from the changes in magnetic field between both pads during CPR. The main disadvantage of this device is that it is bulkier than the others and also rigid.
\nThis section briefly describes three methods to compute chest compression rate and depth and to provide CPR feedback to the rescuers using only chest acceleration. For a more detailed description of the methods, see reference [16]. The first method derives from the traditional approach; it consists in applying double integration to compute the compression depth signal. In our proposal, integration is approximated using a stable band-pass filter (BPF) that performs integration while suppressing low frequencies of the signal. The second and third methods do not require computing the compression depth signal: the second method computes velocity to calculate a compression rate and depth value for each compression, while the third one computes rate and depth from the spectral analysis of the acceleration signal (SAA). We used episodes of simulated cardiac arrest acquired using a resuscitation manikin to evaluate the accuracy of the three methods.
\nWe equipped a Resusci Anne QCPR manikin (Laerdal Medical, Norway) with a photoelectric sensor to register the reference compression depth signal. Chest compressions were delivered in the center of the manikin’s chest with a triaxial accelerometer encased in a metal box placed beneath the rescuer’s hands. The reference compression depth signal and the three axes of the acceleration were digitized and recorded using a National Instruments (Austin, USA) acquisition card connected to a laptop computer. Figure 2 shows the experimental set-up used to perform the data collection.
\nExperimental setup: Resusci Anne QCPR manikin fitted with a displacement sensor, triaxial accelerometer encased in a metallic box, acquisition card, and laptop computer.
Twenty-eight volunteers participated in the recording sessions. They were grouped in couples, and for each couple, four 10-min episodes were recorded. During each episode, volunteers alternated providing 2-min CPR series, each series involving 30 compressions with 5-s pauses in between. A total of 56 episodes were acquired. The experimental protocol was approved by the ethical committee for research involving human subjects of the University of the Basque Country (CEISH UPV/EHU).
\nThere are a number of discrete integration algorithms available, the most common one being the trapezoidal rule, because of its trade-off between simplicity and accuracy. Analytically, the implementation of this rule derives in an unstable linear system [16]. In practice, that means that small low-frequency components in the input signal generate low-frequency components in the output with amplitude that increases with time. If no technique is applied to compensate this accumulation of error in the output signal, the system could suffer a numeric overflow.
\nOur first approach consists in approximating the integration by a stable band-pass filter, designed as the series connection of a high-pass filter and the trapezoidal rule filter, which presents a low-pass response. The high-pass filter is aimed at compensating the instability of the trapezoidal rule filter for low frequencies. Figure 3 shows the magnitude of the frequency response of the band-pass filter, HBPF(f), represented by a solid line. Note that for frequencies above 0.6 Hz, the system matches the ideal response of the trapezoidal rule, depicted with a dashed line, whereas for low frequencies, it is stable (it does not tend to infinity, as opposed to the trapezoidal rule response).
\nFrequency response of the band-pass filter (solid line) compared to the ideal frequency response of the trapezoidal rule filter (dashed line).
Figure 4 illustrates the process of computing compression depth with this method. First, the acceleration signal a(t) (first panel) is processed with the band-pass filter to obtain velocity, v(t) (second panel). Then, this process is repeated with the velocity to obtain the computed compression depth signal sc(t) (third panel). Because of the suppression of the low-frequency components and the waveform distortion caused by the filtering process, sc(t) and the reference compression depth signal s(t) (fourth panel) have different waveforms. However, compression depth and rate can be easily computed by applying a peak detector to sc(t) and measuring the peak-to-peak amplitude and the distance between the peaks, respectively. Compression rate is computed as the inverse of the distance between two consecutive peaks, expressed in compressions per minute (cpm). In Figure 4, the detected compressions and their corresponding depths are depicted by vertical lines in the third and fourth panels.
\nBPF method, based on band-pass filtering.
In this second method, the compression rate and depth values are directly calculated from the velocity signal, without computing the compression depth signal. For that purpose, the band-pass filter described in the previous section is applied to the acceleration once to obtain the velocity signal. The resulting signal is quite stable and can be processed to identify the zero-crossing instants from positive to negative, which represent the onset of each compression cycle (marked by circles in the second panel of Figure 5) and the zero-crossing instants from negative to positive, which correspond to the points of maximum displacement of the chest (marked by crosses in the second panel). For each compression cycle, the compression depth is computed as the area of the velocity signal between the onset and the maximum displacement point (shadowed in the second panel of the figure). Finally, the rate of the chest compressions can be computed as the inverse of the interval between two consecutive zero-crossing instants from positive to negative. In the bottom panel of Figure 5, the computed depth values (represented by vertical lines) are drawn over the reference compression depth signal for comparison.
\nZCV method, based on the analysis of velocity.
In this third method, neither the compression depth nor the velocity signal is computed by integration. Instead of that, average compression rate and depth values are computed every 2 s by applying spectral analysis to the acceleration signal [17]. The basis of this method is the assumption that during short intervals with continuous chest compressions, the acceleration and the displacement signals are quasi-periodic. Consequently, both signals can be modeled as a periodic acceleration and a periodic depth, with a fundamental frequency that represents the average frequency of the chest compressions during the interval. We modeled each 2-s segment of the acceleration and displacement signals using the first three harmonics of their Fourier series representation, without considering the direct current component. Figure 6 illustrates the procedure followed to apply this method. We first computed the fast Fourier transform (FFT) of the windowed acceleration signal and estimated the module and phase of the three first harmonic components of the acceleration. In the example shown in the figure, the selected window is shaded in the first panel, and its FFT with the identified harmonics is shown in the second panel. Taking into account that acceleration is the second derivative of displacement, when both signals are modeled as periodic, the amplitudes and phases of the spectral components of the compression depth can be derived from the ones of the acceleration. Using these values, a periodic version of the chest displacement during the analysis window can be reconstructed. This last step is represented in the third panel of Figure 6. The reference compression depth signal is plotted using a solid line, and the reconstructed signal for the selected window is represented by a dashed line. The reconstructed signal is periodic; i.e., it has the same amplitude for all the compressions, which represent the average compression depth during the analysis window. Average compression rate for each 2-s analysis window is computed from the fundamental frequency of the acceleration, fcc.
\nSAA method, based on the spectral analysis of the acceleration.
Panel (A) of Figure 7 shows the boxplots of the error in the estimation of compression depth for each of the methods. On each box, the central mark is the median, and the edges of the box are the percentiles 25 and 75, P25 and P75, respectively. The whiskers extend to the most extreme data points not considered outliers i.e., within the ±1.5 interquartile range (IQR) interval. Differences in the errors between methods were statistically significant (p < 0.001). SAA provided the highest accuracy, while BPF and ZCV displayed a slight tendency to overestimate depth values. Median (P25-P75) unsigned percent error in depth calculation for each method was 5.9 (2.8–10.3), 6.3 (2.9–11.3), and 2.5 (1.2–4.4)%.
\nBoxplots of the global error in depth (A) and in rate (B) for the three methods.
Boxplots of the error in rate estimation are represented in panel (B) of Figure 7. For the ZCV method, errors were clearly concentrated around zero. Median (P25-P75) unsigned percent error in rate calculation was 1.7 (0.0–2.3), 0.0 (0.0–2.0), and 0.9 (0.4–1.6)% for BPF, ZCV, and SAA, respectively. Differences between methods in error in rate estimation were not statistically significant (p = 0.49).
\nThis section presents three strategies for feedback on the rate and depth of chest compressions during CPR by processing exclusively the acceleration signal and assesses their accuracy in a simulated manikin scenario.
\nThe BPF and ZCV tended to overestimate chest compression depth and presented errors above 5 mm in 25% of the compressions. The SAA method, in contrast, was very accurate and not biased, with an error above 5 mm in only about 5% of the cases.
\nPercent error in rate estimation was very low for the three methods (median of 1.7, 0.0, and 0.9% for BPF, ZCV, and SAA, respectively). Errors of BPF and ZCV methods were mainly caused by the filter transient, particularly at the beginning of each compression series. This influence was higher for the BPF method, in which the filter was applied twice.
\nMost current CPR feedback devices rely on accelerometry and double integration to estimate chest compression depth. Manufacturers have designed different solutions for the instability problem, often protected by patent rights, based on either using additional pressure or force sensors to detect the onset of each compression cycle, or on advanced filtering techniques requiring reference signals. All these solutions lead to complex devices, limiting their widespread use in the practice, especially for bystanders and first responders to a cardiac arrest.
\nThe methods discussed in this section are based solely on accelerometry and could lead to simpler, flexible, and cheaper devices. For its simplicity and accuracy, the method based on the spectral analysis of the acceleration might be a good candidate for implementation. To further test this method in challenging scenarios, we conducted three additional studies to evaluate the accuracy of the method: (1) when chest compressions were provided to a patient laying on a soft surface, (2) when the feedback device was attached to the rescuer’s back of the hand, or to the wrist, or to the forearm, instead of being placed in the usual position between the chest and the rescuer’s hands, and (3) when CPR was performed in a moving vehicle, particularly in a moving long-distance train.
\nWhen the patient is lying on a mattress or on any soft surface, accelerometer feedback devices overestimate chest compression depth, [18] as the calculated depth corresponds to the total displacement of the chest, that is, the sternal-spinal displacement plus the mattress displacement. This would lead to erroneous feedback, which could contribute to the delivery of shallow chest compressions. We proposed a solution based on two accelerometers incorporating the spectral method. One is placed on the chest to measure the total displacement of the chest, while the other one is placed at the back of the patient and measures the mattress compression distance. The difference between both measurements will correspond to the actual compression depth. This method presented a high accuracy. Detailed results are presented in reference [19].
\nCurrent positioning of CPR feedback devices may cause soft-tissue damage to the patient or to the rescuer, along with wrist discomfort. We analyzed the accuracy of the spectral method when the accelerometer was placed in alternative positions that reduce discomfort: the rescuer’s back of the hand, the wrist, and the forearm. We compared these results with those obtained in the traditional position and concluded that positioning the device at the back of the hand was the optimal sensor position. Fixed to the wrist or to the forearm, the sensor was subjected to swinging movements or hands separation from the chest, which caused a large overestimation of compression depth. Readers are encouraged to consult reference [20] for further details.
\nFinally, we studied the performance of the spectral method when tested in a moving long-distance train. Currently, defibrillators are increasingly being installed in public transportation settings, in an effort to provide an early response to sudden cardiac arrest. Early CPR should be also administered in such scenarios, and the CPR feedback devices could increase CPR quality, but to date how the movement of the vehicles affects accelerometer-based devices has not been sufficiently studied. We tested the spectral method in a long distance train with a manikin setup and compared the results with those obtained in static conditions. Errors in depth estimation tended higher in the train, but no statistical differences were found. Rate estimation was very accurate. Our conclusion was that, as the spectral method does not consider frequency components of the acceleration out of the range of chest compressions (1–10 Hz), movement did not affect performance [21].
\nIn conclusion, the spectral method was accurate to compute chest compression depth and rate in a wide set of conditions and could be used to develop a new CPR feedback device. However, the method is not capable of detecting inadequate rescuer’s leaning between compressions. Leaning decreases the blood flow throughout the heart and can decrease venous return and cardiac output. Guidelines recommend minimizing leaning, but human studies show that a majority of rescuers often lean during CPR and do not allow the chest to recoil fully between compressions. This is the current major drawback of any attempt to derive feedback only from accelerometers. For this reason, some commercial accelerometer-based devices use force sensors to provide feedback on this quality parameter.
\nMost defibrillators, particularly the simplest devices, acquire only the ECG and the TI signal through the defibrillation pads. A straightforward solution for monitoring and providing feedback on the quality of chest compressions could be using the already available signals in current defibrillators. TI measures the resistance of the thorax to current flow. It is calculated by passing an alternate current (usually 2–3 mA at 20–30 kHz) through the tissue, measuring the voltage drop, and calculating the impedance using the Ohm’s law. TI is used to check if defibrillation pads are correctly attached to the patient and to adjust the energy of the defibrillation pulse.
\nBaseline TI is approximately 70–80 Ω in adults, but changes in tissue composition due to redistribution and movement of fluids cause fluctuations on the TI. For example, blood circulation and respiration (or ventilation) generate oscillations of different amplitudes in the TI. In addition, chest compressions during CPR cause a disturbance in the electrode-skin interface, inducing artifacts on the TI. With each compression, the TI fluctuates around the baseline impedance with amplitude varying from 0.15 Ω to several Ohms. Figure 8 shows a segment of the compression depth and the TI signals recorded during CPR. In the example, two series of 15 compressions were provided, with pauses for two ventilations in between. The oscillations in the TI signal reflect compressions and ventilations. In general, the waveform of the fluctuations induced by chest compressions is very variable between patients and even along each resuscitation episode.
\nSegment of compression depth and TI signals during CPR. Artifact induced by chest compressions and fluctuation induced by ventilations is clearly visible in the TI signal.
The aim of this section is to explore the feasibility of using TI signal to provide feedback on the rate and depth of chest compressions.
\nSeveral researchers have investigated the use of TI signal for gathering information on the quality of chest compressions. Some studies focused on detecting the instants of the chest compressions in the TI signals to derive compression rate. Others have studied the relationship between compression depth and the amplitude of the TI fluctuations.
\nThe commercial program Codestat (Physio-Control, Redmond, USA) incorporates an automated chest compression and ventilation detector based primarily on the analysis of the TI. The program annotates compression positions and derives the quality parameters compression rate and chest compression fraction (the percentage of time during which chest compressions are provided). Different filtering options allow the user to highlight chest compressions oscillations or ventilation oscillations. Other authors used the TI signal to automatically detect chest compressions in order to estimate the instantaneous compression rate [22]. They found a high correlation between the instantaneous rate computed from the TI and from the compression depth signal. The TI was used also to detect pauses in chest compressions [23] and could be used to measure chest compression fraction.
\nA comprehensive study that aimed to determine the feasibility of a generic algorithm for feedback on chest compression rate using the TI signal recorded through the defibrillation pads was recently published [24]. Out-of-hospital cardiac arrest episodes were collected equally from three different emergency services and different defibrillator models. The algorithm for computing compression rate was based on the spectral analysis of the TI signal. The gold standard was obtained from reference signals such as the force or the ECG. This approach was accurate under different device front ends and a wide range of conditions, proving the generality of the results. The availability of feedback on the rate of chest compressions could have a significant impact on the quality of CPR, especially in basic life-support emergency systems.
\nRegarding the relationship between chest compression depth and the amplitude of the fluctuations induced in the TI, contradictory results have been found in the literature. An experimental study conducted with swine reported higher amplitudes in the TI oscillations for higher compression depths [25]. Another study using porcine models reported high correlations between TI and systolic blood pressure, end-tidal CO2, cardiac output, and carotid flow [26]. Two clinical studies suggested the potential of TI to identify adequate chest compression depth in patients under cardiac arrest [27, 28]. However, none of those studies included any objective measurement of the actual compression depth; i.e., no gold standard was used to validate the hypothesis. In subsequent studies in which a reference compression depth was included, contradictory results were found, and limited details were provided on the methods and the data analyzed [29, 30]. Finally, a prospective, experimental study with swine by Zhang et al. [31] reported a high correlation between TI and both the compression depth and the coronary perfusion. They found significant differences in the TI fluctuation amplitude between adequate and shallow chest compressions, and a strong linear relationship between TI amplitude and compression depth. Authors concluded that changes in the TI had the potential to serve as an indicator of the quality of chest compressions. Nevertheless, they acknowledged that further research was required to extrapolate these conclusions to humans.
\nWe present a study aimed to go further into this remaining question regarding TI signal and its application to provide feedback on chest compression quality: Is there a relationship between chest compression depth and TI in humans?
\nThe aim of this study was to analyze the relationship between TI fluctuations and compression depth during out-of-hospital cardiac arrest episodes. First, we analyzed the overall correlation between three morphologic features of the TI and the compression depth. Second, we evaluated the influence of the patient by computing this correlation independently for each patient. Third, we studied the influence of the rescuer, by isolating series of chest compressions corresponding to a unique rescuer-patient pair. Finally, we tried to replicate the experiments by Zhang et al., focusing on the correlation analysis with series of optimal and suboptimal chest compressions, and we assessed the discrimination power of the TI signals to distinguish between shallow and nonshallow chest compressions.
\nThe data set used in this study was collected by Tualatin Valley Fire & Rescue (TVF&R), a first response advanced life-support fire agency serving 11 incorporated cities in Oregon, USA. It comprised 623 out-of-hospital cardiac arrest episodes recorded during CPR. The compression depth and TI signals were available for 189 of the 623 episodes. We extracted 60 episodes containing both signals concurrently, with a minimum of 1000 chest compressions per episode. Only chest compressions included in series of at least 10 compressions were considered, yielding a total of 11,667,9 chest compressions. Then, we extracted intervals where the single-rescuer-single-patient pattern was guaranteed. Interruptions in compressions longer than 1.5 s were identified as a possible change of rescuer. We gathered 75 series of this type.
\nCompression depth signal was first processed to compute the maximum depth for each chest compression, Dmax. The instants when Dmax was achieved were computed using a negative peak detector with a static threshold of 15 mm. The cycle of each chest compression was then identified using these instants both in the compression depth and in the TI signals. This procedure is illustrated in Figure 9, where each cycle is delimited with vertical dotted lines. TI signal was band-pass filtered to remove baseline and fluctuations caused by ventilations and high-frequency noise. To characterize TI fluctuations, we defined three TI waveform features computed for each chest compression:
Peak-to-peak amplitude, Zppi: difference between the maximum and the minimum values of the ith TI cycle.
Area, Ai: area of the TI during the ith compression cycle.
Curve length, Ci: length of the curve of the TI signal in the ith compression cycle.
Two examples of the features extracted from the TI signal. The maximum depth is represented in the compression depth signal (top) and the TI features in the TI signal (bottom). Compression cycles are delimited by vertical dotted lines.
Figure 9 illustrates two examples with the extracted features depicted in the compression depth (top) and in the filtered TI signal (bottom). Panel (A) shows quite sinusoidal TI fluctuations, and panel (B) shows a more irregular TI waveform. This is why we computed area and curve length in addition to the peak-to-peak value of TI, as this single feature cannot discriminate between regular and irregular fluctuations. In order to smooth the values of the computed features, the average value of each parameter was computed every 5 s.
\nThe linear relationship between Dmax and the TI features was tested for the whole population, for each patient independently, and for series of compressions provided by a single rescuer on a single patient. Pearson’s correlation coefficient r was computed for each analysis. Univariate linear regression was used to model the relationship between Dmax and the TI features.
\nIn order to avoid potential variability introduced by the rescuer, we analyzed the relationship between Dmax and Zpp in a single-rescuer-single-patient pattern. Series with a minimum standard deviation of 7 mm in Dmax were considered. To avoid bias, a single series per patient was selected, the one with the highest standard deviation. A total of nine series were extracted. Univariate linear regression was used to predict Dmax using Zpp, and r coefficient was computed for each series and jointly for the whole set.
\nIn order to replicate the procedure by Zhang et al. in their swine model [31], we selected series with optimal and suboptimal series of chest compressions. A series was suboptimal when at least 75% of the compressions were below 38 mm, and optimal when at least 75% of the compressions were above 50 mm. A total of 12 series (one per patient) were selected. They were jointly analyzed computing r and applying univariate linear regression.
\nFinally, we assessed the discrimination power of the three TI features to classify each 5-s window as shallow (below 38 mm) or nonshallow (above 43 mm) according to the criteria stated by 2005 resuscitation guidelines (valid at the time episodes were collected). We applied a multivariate logistic regression model for the classifier. We split the 60 episodes into a training (40) and a test set (20). The power of the classifier was evaluated in terms of the area under the curve (AUC), and of the sensitivity and specificity in the diagnosis of shallow chest compressions.
\nFigure 10 shows the scatterplots of Dmax against each of the TI features for the whole population and the model fitted in each case. In all cases, there was a high dispersion around the regression line. The value of r was 0.34, 0.36, and 0.37 for Zpp, A, and C, respectively. However, the analysis within patients yielded a median (IQR) correlation coefficient r of 0.40 (0.24–0.66) for Zpp, 0.43 (0.26–0.66) for A, and 0.47 (0.25–0.68) for C.
\nScatterplots of Dmax with respect to TI features for the whole population. For each scatterplot, the fitted regression line and the value of r are depicted.
For the nine series in which the single-patient-single-rescuer pattern was maintained, the individual analysis of each series yielded a median r of 0.81 (0.51–0.83). However, when all of them were considered jointly, r decreased to 0.47.
\nIn the analysis parallel to the one conducted by Zhang et al., we considered the set of twelve optimal and suboptimal series. For the optimal group, Dmax was 57 (54–63) mm and Zpp was 3.0 (2.5–3.7) Ω. For the suboptimal group, Dmax was 32 (30–34) mm, and Zpp was 0.9 (0.6–1.5) Ω. We obtained a correlation coefficient of 0.87, quite similar to the 0.89 reported by Zhang et al.
\nFinally, when analyzing the power of each feature to classify 5-s windows as shallow or nonshallow, we found significant differences between groups, but a high overlap between distributions. The logistic regression classifier showed sensitivity, specificity, and AUC of 89%, 49%, and 0.8 for the test set.
\nOur study included a set of out-of-hospital cardiac arrest episodes with a wide variety of patients and rescuers. The results obtained from the analysis of 14,424 values for each feature showed very low correlation with Dmax (r < 0.38 in any case). Prediction of chest compression depth with any of the TI features was highly unreliable. For instance, for any given Zpp value, the probability of error in the prediction of Dmax is high because of the wide range of corresponding Dmax values.
\nThe variability of the results between patients was also high. Sex, chest size, body mass, and pads position have been reported to affect TI baseline, and TI fluctuations during ventilations are correlated with the thoracic fat and thoracic circumference. Our results showed also a great dispersion with respect to the regression line between Dmax and Zpp from one patient to another. Although, for some patients, little dispersion and high correlation values could be observed along the episode, different tendencies were also found within each episode, showing the influence of different rescuers. In these cases, a single regression model will hardly fit all the values.
\nWith a single rescuer, the dispersion of each series decreased, and linearity between Dmax and Zpp increased notably. Nevertheless, interpatient factors such as chest/electrodes characteristics of the nine patients caused a low correlation when all the series were considered jointly. This emphasizes the inability to define a confident global linear fit.
\nFinally, we could replicate the high linearity observed between depth and TI amplitude reported by Zhang et al. in the animal model. We also found significant differences between the optimal and the suboptimal groups, but we also found that for a given value of Zpp, Dmax varied widely. For a proper interpretation of the apparent observed linearity, we should consider the limitations of the analysis. On the one hand, considering only optimal and suboptimal chest compressions shows a biased picture of human out-of-hospital CPR. When the complete range of compression depths is considered, the correlation coefficient drops to 0.34. On the other hand, the set of patients and rescuers was small (12 patients/12 rescuers in our study, 14 animals/2 rescuers in the study by Zhang et al.). When we included a greater variability (60 patients and 2 to 6 rescuers), higher dispersion was observed and correlation coefficient decreased substantially.
\nIn summary, TI signal can be a feasible indicator for CPR quality parameters such as chest compression rate, chest compression fraction or chest compression pauses. Unfortunately, in this study, we proved that TI is unreliable to predict the key quality parameter of chest compression depth. Nevertheless, we further analyzed, from a practical perspective, the power to discriminate shallow from nonshallow chest compressions, in an effort to achieve a quality feedback method. We tried to discriminate chest compressions <38 mm from those >43 mm. Each TI feature showed different distributions between the two categories, but high overlap between them. The results of the logistic regression classifier allowed us to conclude that it is not possible to safely identify shallow chest compressions using the TI signal.
\nDuring CPR, the quality of chest compressions is related to patient’s survival. Feedback devices guide the rescuers toward target compression depth and rate, and contribute to increase the CPR quality. This chapter explored new alternatives to provide feedback on the quality of chest compressions during CPR. Two strategies were studied: the use of the chest acceleration, which can be acquired using an extra pad placed on the chest of the patient during CPR and the use of the transthoracic impedance (TI) signal, which is acquired by current defibrillators through defibrillation pads. Chest acceleration can be used to accurately compute chest compression rate and depth in a wide range of conditions. TI, in contrast, can be used to accurately compute chest compression rate, but not to identify too shallow chest compressions. The development of simpler feedback devices could contribute to their widespread use and to increase the CPR quality.
\nThis book chapter derives from the thesis work Feedback systems for the quality of chest compressions during cardiopulmonary resuscitation carried out by author Digna M. González-Otero, under the supervision of co-authors Jesus Ruiz and Sofía Ruiz de Gauna. Several parts of this work have been published in indexed journals or presented at international conferences.
\nThis research received financial support from the Spanish Government through the project TEC2012-31144 and from the Basque Government through the grant no. BFI-2011-166 and through the project IT1087-16.
\nAuthors thank all volunteers participating in the manikin study and the TVF&R emergency medical services providers for collecting the out-of-hospital cardiac arrest data.
\nToday, 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.
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