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",isbn:"978-1-80356-363-3",printIsbn:"978-1-80356-362-6",pdfIsbn:"978-1-80356-364-0",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"969d1c6315b04584c2f011e03dad69c2",bookSignature:"Dr. Mansoor Zoveidavianpoor",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11929.jpg",keywords:"Drilling Performance, Drilling Tools, Well Design, Drilling Procedure, Rotary Drilling, Directional Drilling, Measuring-While-Drilling, Smart Well Technology, Environment Protection, Geothermal Drilling, Sustainable Drilling Fluids, Carbon Sequestration",numberOfDownloads:2,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 18th 2022",dateEndSecondStepPublish:"March 18th 2022",dateEndThirdStepPublish:"May 17th 2022",dateEndFourthStepPublish:"August 5th 2022",dateEndFifthStepPublish:"October 4th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Zoveidavianpoor has over 18 years of multidisciplinary oil and gas experience, built upon his technical, operational, and management roles in the industry and academia. He is a member of the Society of Petroleum Engineers (SPE), the Energy Institute, UK and is registered as a chartered petroleum engineer. He has published more than 50 publications on International peer-reviewed Journals and conferences, has contributed to 5 textbooks, and served in many scientific committees.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"92105",title:"Dr.",name:"Mansoor",middleName:null,surname:"Zoveidavianpoor",slug:"mansoor-zoveidavianpoor",fullName:"Mansoor Zoveidavianpoor",profilePictureURL:"https://mts.intechopen.com/storage/users/92105/images/system/92105.jpg",biography:"Dr. Mansoor Zoveidavianpoor has over 24 years of experience, built upon his technical, operational, and management roles in the industry and academia. Mansoor holds a BSc degree in Geology, MSc, and Ph.D. degrees both in Petroleum Engineering. He was involved in different disciplines such as project management, geology, flow assurance, piping construction, artificial intelligence, environmental engineering, drilling and production engineering, He has lectured several courses at the University Technology Malaysia (UTM), Petroleum University of Technology (PUT), and Islamic Azad University (IAU). He is a member of the Society of Petroleum Engineers (SPE) and registered as a Chartered Petroleum Engineer at Energy Institute, and EIA subject specialist at DOE Malaysia. He has published more than 50 publications on International peer-reviewed Journals and conferences, has contributed to 5 textbooks, and served in many scientific committees. Currently, he is working as an Associate Professor at UTM and involved in several consultancies in petroleum engineering and energy transition. 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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:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"50813",title:"Update on Clinical Evidence Supporting Hemodiafiltration",doi:"10.5772/63027",slug:"update-on-clinical-evidence-supporting-hemodiafiltration",body:'\nConventional diffusion‐based dialysis modalities, namely, low‐flux and even high‐flux hemodialysis (HD), are limited in their capacity to effectively remove large uremic toxins and to improve outcomes for chronic kidney disease (CKD) patients [1]. By increasing convective solute transport, hemodiafiltration (HDF) enhances solute removal capacity over a broad range of middle‐ and large‐size uremic toxins implicated in the pathophysiology of CKD [2, 3]. In addition, convective‐based modalities have been shown to improve hemodynamic stability [4, 5] and to reduce patients’ inflammation profile—both factors implicated in CKD morbidity and mortality [6–8]. Growing clinical evidence indicates that HDF‐based modalities provide CKD patients with a number of clinical and biological benefits, including improved outcomes. Interestingly, it has recently emerged that the clinical benefits associated with HDF are positively associated with the total volume of fluid removed by ultrafiltration per session or per week [9–12]. This finding adds a new component to the conventional dialysis adequacy concept, namely, convective dose.
\nIn this chapter, we elucidate the concept of convective dose and discuss the threshold above which an improvement in CKD patient outcome can be expected. In addition, factors implicated in the achievement of an optimal convective dose in the sense of best clinical practice are reviewed and clinical evidence supporting the use of HDF today is summarized.
\nFrom a nephrologist\'s perspective, it is disappointing to observe that outcomes of end‐stage kidney disease (ESKD) patients remain poor despite significant progress in hemodialyzer performances, HD machines and monitoring devices, better understanding of uremic toxicity, and improved patient management. Even without an in‐depth analysis of root causes and reasons for renal replacement therapy (RRT) limitations, several factors are easily recognizable as contributing to this outcome. It is usually convenient to group these into two categories: “nonmodifiable factors,” such as age, gender, ethnicity, comorbidity profile, and kidney disease history, and “modifiable factors,” such as RRT modality and treatment adequacy. Changing the renal replacement treatment paradigm and improving practice patterns to ensure customized and better care are the only ways to improve the outcomes of ESKD patients.
\nIn this context, two main options are available. First, HDF is today\'s most innovative, promising, and advanced alternative to high‐flux HD. By combining diffusive and enhanced convective solute mass transfer, HDF offers the most efficient RRT today over a wide spectrum of uremic toxins including middle‐ and large‐sized solutes. Using ultrapure dialysis fluids (water and dialysis fluid), HDF constitutes a highly biocompatible system and suppresses the occurrence of microinflammation processes [13–15]. By reducing the incidence of hypotensive episodes as well as dialysis intolerance symptoms [16–18], HDF may reduce or prevent repetitive cardiovascular insults and their deleterious long‐term consequences. By giving access to an unlimited amount of substitution fluid at the cost of ultrapure dialysis fluid, online HDF provides a cost‐effective approach for optimizing and customizing HDF treatment prescription to the patient\'s metabolic needs. Provided that best clinical practices and hygienic rules are applied, HDF offers a reliable, efficient, and cost‐effective RRT tool.
\nSecond, the extension of treatment time is the more physiologic approach to improving patient outcome, even if not the most popular one. Increasing treatment time and/or frequency is the best way to facilitate sodium mass removal and thereby restore extracellular fluid volume, to ensure removal of uremic compounds with low intracorporeal mass transfer coefficients or tightly bound to albumin, and to minimize “nonphysiological” changes due to the aggressive nature of intermittent treatment schedules. These two approaches, HDF and extended treatment time, should not be considered competitive but rather complementary. Only a few studies have combined the solute mass transfer capacity of HDF with increased treatment time [19–21] and all clinical trials have confirmed the tremendous benefits of such combination on intermediary and long‐term outcomes.
\nAlthough many studies associate HDF with significant improvements in ESKD patient outcomes, still some conflicting data or cost concerns have hampered general clinical and widespread acceptance of this method. Here, we attempt to reconcile facts and concerns regarding HDF. To begin, it should be stressed that a differentiated understanding of HDF should be adopted as opposed to considering HDF to be a generic term that covers all convection‐based renal replacement modalities. The differentiation necessary lies in the magnitude of the convection dose that must be delivered to improve outcomes of ESKD patients. Based on published literature and our own experience, we revisit the current definition of convective dose and its threshold value for improved patient outcome, we review the factors affecting convective dose delivery, we summarize the best clinical practices in HDF prescription, and we summarize the main studies addressing clinical outcomes.
\nAccording to the EUDIAL group recommendations, convective dose is the total volume of ultrafiltration achieved per HDF session (L/session) and summed to week (L/week), standardized to postdilution convection volume for the various other dilution modalities (i.e., pre‐, mixed, or mid‐HDF), taking differences in the fluid volumes into consideration [22]. This is an easy and clinically relevant surrogate indicator of the convective component. Knowing that only hemodialyzers containing membranes that are highly permeable to both water (Kuf>50) and solutes (sieving coefficient β2‐microglobulin [β2M] >0.6) are used for HDF, it is possible to calculate the convective component of solute clearance. An alternative proposal was to focus more on the biological effect of middle‐molecule removal using biomarkers that reflect the convective action of the HDF. Among uremic toxins, β2M, a 12‐kDa peptide, seems to be the most clinically relevant representative of the middle‐molecule uremic toxins and strongly implicated in both morbidity and mortality of CKD patients.
\nFollowing international guidelines, urea Kt/V has been established as the principle dialysis dose quantifier and is regularly used as a quality‐control tool for treatment delivery. To be valid, this approach requires certified methodology (appropriate urea sampling method and time, suitable formula) and appropriate timing of measurements (minimum of once monthly) to capture early deviation in dialysis efficacy. The ionic dialysance (iK) measurement embedded in some HD monitors is an option. This offers an interesting and cost‐effective alternative that may be performed routinely at each session providing a true continuous quality‐control tool. Using then the simplified concept of iKt, where iK stands for the average ionic dialysance measured by HD monitor and t stands for the duration of the dialysis session, one can estimate the total diffusive dose delivered per session (L/session).
\nThe effective total dialysis dose (i.e., diffusive and convective) delivered to the ESKD patient can be easily assessed. Convective dose as estimated by total ultrafiltered volume per session (L/session) could be considered as a complementary component of the diffusive dose delivered and estimated by iKt (L/session).
\nA systematic review of studies [retrospective cohorts, prospective randomized controlled trial (RCT)] exploring the relationship of convective dose and patient outcome has shown that survival benefit is observed only when a minimum threshold ultrafiltration volume has been delivered [23]. The critical ultrafiltration volume per session (or per week) required for better patient outcome is between 20 and 22 L/session for a typical European ESKD patient. More recently, it has been shown that total ultrafiltered volume per session (or per week) acted as a continuous variable mimicking a sigmoidal dose‐response curve [24].
\nThe normalization of the convective dose to the patient\'s anthropometric characteristics has been proposed to match with patient metabolic needs and also to facilitate the generalization of this relationship with different patient profiles (e.g., Asian and American). In fact, such normalization attempts using different scalings (i.e., body weight, height, body surface area, and total body water) have been adopted in different studies, including the Estudio de Supervivencia de Hemodiafiltración On‐Line (ESHOL) [25] and the individual personal data meta‐analysis that was part of the European pooled project [26]. Interestingly, none of these scaling factors have enhanced sensitivity in predicting the relative risk (RR) of mortality. Crude convection volume per session (or per week) or convection volume scaled to body surface area or total body water tend to perform best in predicting survival benefits or mortality risks where HDF is concerned.
\nBest clinical practices are essential in daily practice to achieve optimal ultrafiltration flow and the total ultrafiltered volume targeted. Schematically, three basic components need to be considered: patient‐dependent factors, prescription‐dependent factors, and technical or machine‐dependent factors.
\nThe achievement of “high” blood flows (>350 mL/min) depends on the type of vascular access (i.e., central venous catheter, arteriovenous fistula, or graft) and on the provision and maintenance of vascular access patency. Vascular access is defined as inadequate when an extracorporeal blood flow of at least 300 mL/min cannot be reached. Basically, patients treated with online HDF require a vascular access capable of delivering a consistent extracorporeal blood flow between 350 and 400 mL/min or higher. Such extracorporeal blood flows could be reached with large bore tunneled central venous catheters and with arteriovenous fistulas or grafts delivering access flows of ≥500 mL/min. High blood flow is essential to ensure that a sufficient amount of blood is processed during the treatment session. Vascular access flow performances and needle sizes assume a fundamental role in preserving the advantages of applying a high convection volume.
\nHematocrit and protocrit (the volume fraction of plasma proteins that may be calculated as the product of 0.000718 and the total protein concentration of plasma proteins in g/mL [22]) affect negatively the plasma water volume and plasma water flow. High hematocrit (e.g., resulting from anemia management and hemoglobin targets) and high total protein concentration (e.g., due to the particular nutritional and inflammatory status of the patient) enhance viscosity tremendously, reduce ultrafiltration capacity (filtration fraction), and provide unfavorable conditions for ultrafiltration flow. The additional ultrafiltration applied to achieve a given weight loss is an additive factor that may affect total ultrafiltration. Finally, a dynamic interaction between blood flow (shear rate and shear stress), blood components (hematocrit and protocrit), filtration fraction (ultrafiltration flow to blood flow ratio), and membrane surface (protein layer formation) is crucial to facilitate ultrafiltration flow. Many of these factors are taken into consideration in modern dialysis machine technology.
\nThe effective extracorporeal blood flow delivered is a fundamental determinant of all extracorporeal cleansing therapies. Among factors that determine the efficiency with which uremic solutes are removed during dialysis, extracorporeal blood flow, whether instantaneous (flow rate) or cumulative (total blood volume processed over the session), is the most critical. Treatment efficiency assessed either in terms of solute clearance or solute mass removal is then dependent on the total blood “processed” within a dialysis session.
\nHigh blood flows are critical in HDF, as they have a dual action, one being to maximize the amount of solute removed and the other being to preserve membrane permeability by retarding the formation of a protein layer on the membrane (secondary layer formation). The choice of needle size matters in HDF. Following Poiseuille\'s law, needle size is a barrier to high blood flow: 15‐gauge needles (optimally 14‐gauge needles) are required to sustain blood flow of ≥350 mL/min at an acceptable pressure regimen.
\nThe choice of a specifically designed hemodiafilter is important to optimize ultrafiltration flow and prevent dysfunction or alarms occurring on the HDF monitor due to hemorheological changes in the dialyzer. Preferred hemodiafilter features, apart from being equipped with highly permeable membranes, should favor the following to reduce internal convective processes: low blood flow resistance (large lumen diameter of fibers (e.g., 200 μm), short length of dialyzer housing, and increased number of fibers per sectional surface area.
\nMethods of anticoagulation will not addressed here in detail. However, anticoagulation is required to prevent thrombosis of extracorporeal blood circuit and to ensure a safe and efficient HDF session. Different kinds of antithrombotic agents can be used systematically by intravenous (IV) injection.
\nUnfractionated heparin (UH) is administered as an IV bolus dose (30–50 IU/kg) at the start of the HDF session followed by continuous IV infusion (500–700 IU/h). The heparin dosing regimen for HDF does not differ from that in regular HD.
\nThe use of low molecular weight heparin (LMWH) is nowadays favored by many centers because of its ease of use and its better risk profile. In this case, LMWH should be preferably administered IV into the venous line and not into the arterial line to prevent significant loss during the first hemodialyzer passage.
\nThe prescription of HDF treatment time duration and frequency is usually based on the patient\'s metabolic needs, extracellular fluid management, and cardiovascular and session tolerance. Increasing both the duration and frequency will facilitate the delivery of a high volume of ultrafiltration. A pragmatic approach is to establish a suitable convective dose for a given patient and to increase the treatment time according to the limitation of effective blood flow delivery.
\nAchieving high ultrafiltration rates and targeted ultrafiltration volumes can be challenging and requires the careful management of the transmembrane pressure (TMP) according to the treatment modality selected. In attempting to achieve high ultrafiltration volumes, hemoconcentration within the filter commonly results in high TMP, triggering pressure alarms and potentially causing cell damage. Basically, the augmented protein layer formation occurring naturally at the blood‐membrane surface during the course of the HDF session fouls the membrane pores and reduces the membrane\'s hydraulic permeability. In combination with the increased oncotic pressure (total protein increase) along the fibers, this tends to reduce the ultrafiltration flow. Faced with situations of high TMP increase and/or pressure alarms, nursing staff manually reduce the substitution flow and thus reduce the chances of achieving the targeted ultrafiltration volume.
\nNew features to optimize HDF performances and achieve optimal convective doses are currently available. The adjustment of the substitution mode was the most obvious and primary focus of investigation. When postdilution is problematic, switching to pre‐, mixed, or mid‐dilution mode may be a viable alternative. In all these cases, the targeted convection volume needs to be adjusted for the dilution factor corresponding to the HDF modality chosen (e.g., x2 for predilution HDF or x1.5 for mid‐dilution HDF in manual prescription) to match efficacy with the postdilution mode.
\nNew technical features involving specific software algorithms are currently being implemented and tested in new online HDF machines. Basically, the idea is to provide an automatic ultrafiltration control system to reduce membrane fouling and ensure maximal ultrafiltration flow considering basic operational conditions of blood flow, hemorheological conditions, and prescription setting. Schematically, the system avoids excessive hemoconcentration by the continuous adaptation of the substitution flow according to changes in blood viscosity within the dialyzer as identified by signal analyses of the pressure pulses transmitted from the peristaltic blood pump. Signal analysis is conducted several times per minute, and the substitution rate is automatically adapted based on pressure pulse attenuation and cross‐membrane pressure assessment. Using such automatic control systems, it is possible to increase the ultrafiltered volume per session by 10 to 20% without harming the patient, filter, or cells.
\nAs mentioned previously, treatment time is one of the factors limiting the increase of “dialysis dose”. In the current models of delivering dialysis in dialysis units, based on shifts assigned to nurses with a ratio of one nurse to three to five patients, any further increase of treatment time has an associated cost. Out of the 5.5 to 6 h of the shift length for each single nurse, it is possible to deliver a median of 4 h of treatment to three to five patients. There are two ways to achieve a cost‐neutral increase in treatment time. The first is to reduce the time needed to prepare the dialysis equipment before initiating treatment. Faster disinfection of the dialysis machine is achievable with more simple hydraulic components and/or more effective disinfection processes. Also, the disinfection of the machine surface can be simpler, safer, and faster; this is achievable with especially designed equipment surfaces having as few discontinuities as possible. The second way is to reduce the number and complexity of nurse interventions. This approach may allow nurses to treat more patients, thus decreasing the cost of treatment for each patient per hour. Consequently, it could be possible to increase the time of the shift and the treatment time in a cost‐neutral manner. Is this possible? According to Tsobanelis et al. [27], new dialysis equipment under testing in HDF mode for fistula patients had a 24% reduction in the number of major handling steps compared to the previous dialysis machine from the same company.
\nOther aspects to be considered when targeting an increase of patient to nurse ratio are those related to safety. The risk of harm has to be reduced as much as possible, and one option is to reduce this risk by the improved design of the equipment. As an example, we can again mention the recent experience of Tsobanelis et al. [28], who reported that the tested new equipment had 27% fewer major process steps and touch points critical to hygiene compared to the current machine version. The authors found it particularly noteworthy that it was possible to avoid disconnection of the arterial line from the arterial needle with the redesigned process of blood reinfusion at the end of the session.
\nFinally, in the time of green economy, the authors also highlighted that the switch from an infusion line to an integrated infusion port reduced the volume of contaminated waste. The major source of waste relates to the disposables (bloodlines and dialyzers), so that newly designed, integrated disposables can facilitate a reduction of disposable waste, for example, of approximately 0.2 kg/session as reported by Schleser et al. [29], which, given an average of 10,000 treatments yearly delivered by a dialysis unit, translates into 2000 kg less waste. In terms of carbon footprint, one should also consider the consumption of water and energy and how this can be limited, for example, by having a more efficient water treatment system.
\nOnline HDF can no longer be considered an experimental treatment but has now developed into a mature and accepted RRT. In fact, this dialysis modality is employed for sustaining the lives of more than 160,000 ESKD patients worldwide, including 80,000 in Europe, Middle East, and Africa. Europe played a leading role in developing this therapy, where the prevalence of HDF is close to 18% (varying across countries from 0 to 100%). The annual growth rate is approximately 6% [30]. In 2013, Japan treated 31,273 patients with HDF (23,445 thereof on online HDF), representing 10% of its total dialysis population (294,605 patients; Kawanishi, personal data). The number increased by 4% between 2013 and 2014 as positively influenced by the implementation of a specific reimbursement fee in 2012. Interestingly, in Japan, the predominant HDF mode is predilution HDF (90.8% of HDF treatments).
\nThe prescription of the HDF treatment schedule is based on the usual target of providing optimal RRT to the ESKD patient. It is a composite and trade‐off between patient‐specific issues (e.g., metabolic needs, treatment tolerance and acceptance, and patient‐treatment interaction), local logistical and practical issues (e.g., facility and/or modality availability), and health regulation and reimbursement policies.
\nThe duration and frequency of sessions are based on two main components: (1) patient\'s metabolic needs and dialysis adequacy targets and (2) extracellular fluid management and cardiovascular tolerance depending on ultrafiltration rate. Increasing session length and frequency will facilitate the achievement of a high ultrafiltration volume. A pragmatic approach is to estimate a suitable convective dose for a given patient and to increase the treatment time according to the limitations present regarding effective blood flow or other hemorheological factors.
\nIn clinical practice, prescription comprises the setting of the substitution rate (substitution volume per session extended to weekly volume) and adding the required weight loss to achieve the dry weight for that patient. Based on the results of recent studies, a minimal substitution volume of 60 L/week (or 40 L/m2 body surface area) is required in individual ESKD patients to improve patient outcome. The additional ultrafiltration volume required for the correction of extracellular fluid volume excess has to be added to the substitution volume.
\nThe hemodialyzer used for HDF must contain membranes that are highly permeable for both water (Kuf>50 mL/h/mmHg) and solutes (sieving coefficient for β2M≥0.6) with adequate dialyzer surface area. Filter design should favor a low internal blood flow resistance, thereby reducing membrane fouling and minimizing backfiltration as much as possible. A simple rule of thumb commonly applied in clinical practice is to consider 1 m2/each 200 mL/min effective extracorporeal blood flow, meaning that a 2.0 m2 hemodialyzer is appropriate for a blood flow of 400 mL/min [31].
\nIn the past, the composition of the dialysis fluid differed from that of the substitution fluid. This is not the case in online HDF. Given the current definition of convective treatment adequacy, based on 23 L/session of convective dose in postdilution HDF, this equality of fluid compositions, and also the HDF mode, may affect the mass balance of electrolytes during the treatment session. Several studies dealing with the dialysate/substitution fluid prescription and focusing on sodium, calcium, and bicarbonate have been published.
\nSodium: The importance of the correct sodium balancing during dialysis, preventing sodium overload with resultant thirst and water overload, has long been recognized. This risk of sodium loading also has to be considered in high efficiency treatments coupling diffusion and high volume convection. In a 1991 evaluation of HDF sessions with convective doses ranging between 9.1 and 16.7 L/week, Pedrini et al. [32] found that sodium balance was mainly affected by the sodium concentration gradient between initial plasma water and dialysate, the sodium level in the substitution fluid, and the imposed ultrafiltration rate. In the same patients treated with similar operating conditions, significantly lower net sodium removal was observed when on predilution compared to postdilution HDF [33]. Despite the complexity of managing the multifactorial equations describing the relationship between affecting variables and sodium balance (e.g., the laboratory method for the determination of sodium and the negative charge of plasma proteins) [33], ideal modern equipment should be able to automatically address sodium balance.
\nCalcium: The target in the case of calcium is to maintain a neutral calcium balance, as an excessive calcium load has been associated with vascular calcification, whereas calcium depletion has been linked to worsening secondary hyperparathyroidism and decreased bone mass [34]. Here also, the modality of HDF affects the balance. Although calcium balance during postdilution online HDF does not differ from standard HD, it is usually recommended to increase the dialysate/substitution fluid calcium concentration by 0.25 mmol/L in predilution HDF mode in order [34]. In addition, discrepancies between expected and observed concentrations in the dialysate/substitution fluid play an important role in the case of online HDF. In a volumetric system based on conductivity, the sodium for the bicarbonate dialysate/substitution fluid comes in part from a basic component and in part from an acidic component. In cases of a decrease in dialysate sodium with concomitant increase of bicarbonate, a lower proportion of the acid component will produce a lower than expected calcium level in the dialysate/substitution fluid [35].
\nBicarbonate: a positive bicarbonate balance is targeted during the treatment session to neutralize the interdialytic accumulation of strong acid anions and to avoid starting the next session with metabolic acidosis. However, there is also a risk of postdialysis alkalosis. In a recent publication by Havlin et al. [36] analyzing 68 patients on postdilution HDF treated for 4 to 5 h with 80 to 90 mL/min of substitution fluid (19–27 L/session) with a dialysate bicarbonate concentration of 32 mmol/L, 34% of patients were acidotic at dialysis initiation, but 80% had metabolic alkalosis after dialysis. They speculated that this was due to an excessive elimination of retained and endogenous anions. According to the authors, this observation requires further investigation. In any case, several factors affect the mass balance. A significantly lower bicarbonate gain was observed in predilution HDF versus postdilution HDF [37]. As is true for all electrolytes, the difference in concentration between bicarbonate levels in the dialysate/substitution fluid and in the blood at the initiation of the session is positively correlated to the mass transfer. Therefore, to maintain the same bicarbonate balance when moving from postdilution HDF to predilution HDF, dialysate bicarbonate concentration should be increased by 2 mmol/L. In fact, in predilution HDF, bicarbonate levels of the blood entering in the dialyzer increase, enhancing the loss across the membrane and reducing the normal gain by diffusion from dialysis fluid to blood [33].
\nIn this section, intermediary treatment outcomes stand for surrogates of primary outcomes in assessing HDF safety, efficacy, and tolerance. For this purpose, we focus on cardiovascular stability and treatment tolerance, solute removal (phosphate, β2M), and inflammation, oxidative stress, and anemia.
\nIn the short term, a significant reduction in the episodes of intradialytic hypotension was observed in HDF compared to conventional HD [16]. This has been ascribed to negative thermal balance (due to the infusion of relatively cool replacement fluid), a high sodium concentration of the substitution fluid, and/or removal of vasodilating mediators [4].
\nSeveral controlled studies have confirmed enhanced clearance and mass removal of β2M with HDF (30–40% higher than high‐flux HD) accompanied by a 10 to 20% decline in circulating blood β2M concentrations [38, 39]. It must be reminded that reduction of circulating predialysis β2M takes time, as plasma levels reflect the equilibrium between production and elimination rates. Thus, 3 to 4 weeks are required to achieve a new steady‐state and a serum concentration change [40]. Recently, it has been calculated in a large cohort of incident ESKD patients that each additional 10 L convection volume was associated with a 0.8 mg/L reduction of β2M [24].
\nPhosphate mass removal and serum phosphate is a major concern in ESKD patients. RRT accounts for 60 to 70% of the total amount of phosphate removed to restore weekly phosphate mass balance. The other 30 to 40% needs to be eliminated by feces through the combined action of diet and phosphate binders. Although still a matter of controversy, high efficiency HDF has been shown to enhance the phosphate mass removed by 15 to 20% [41] with a subsequent predialysis serum phosphate level reduction of 6%. The percentage of patients reaching target pretreatment serum phosphorus levels with HDF was reported to increase from 64 to 74% in the Convective Transport Study (CONTRAST) [42].
\nHigher clearances of a number of other uremic compounds have also been documented with HDF: complement factor D (a proinflammatory mediator), leptin (16 kDa; involved in loss of appetite), FGF23 (30 kDa, implicated in metabolic bone disorders and vascular calcification), various cytokines, circulating advanced glycosylation end products (AGEs), and AGE precursors [43, 44].
\nInflammation and oxidative stress profiles tend to be improved in patients treated by HDF. Several prospective studies have shown that levels of C‐reactive protein (CRP) and other sensitive biomarkers of inflammation (e.g., interleukin‐6) and/or proinflammatory cells are reduced. In this field, the Rischio cardiovascolare nei pazienti afferenti all\'area vasta in dialisi (RISCAVID) study is certainly one of the more convincing studies, being conducted in a large cohort of dialysis patients [7, 45]. A meta‐analysis has recently reemphasized that the regular use of ultrapure dialysis fluid was the main driving force for such benefits [14].
\nThe erythropoiesis‐stimulating agent (ESA) dose could be reduced in HDF, as reported in several clinical studies and summarized in a systematic review [46]. The benefit was attributed to the combined effects of the higher removal of middle‐sized toxins (erythropoietic inhibitor substances) and reduced inflammation due to the use of higher‐quality water and dialysis fluid [47, 48]. However, this effect was not confirmed in a recent meta‐analysis [49].
\nSeveral large cohort studies have indicated that the extended use of high‐flux membranes and convective therapies has a beneficial impact on the development of β2M amyloidosis in the long term, reducing the incidence of carpal tunnel syndrome and other related manifestations [50, 51]. This beneficial effect probably results from the regular use of ultrapure water and biocompatible materials, reducing inflammation, combined with convective modalities that enhance β2M removal [52].
\nIn this section, endpoint outcomes are hard primary outcomes in assessing HDF long‐term efficacy. Consequently, the focus here is on mortality (all‐cause and cardiovascular) and morbidity (hospitalization, dialysis‐related pathology).
\nLocatelli et al. [50] conducted a retrospective observational study (Lombardy registry) of 6444 patients with ESKD who started RRT on HD, HDF, or hemofiltration (HF) between 1983 and 1995. A total of 1082 patients were treated with HDF or HF (first choice in the case of 188); the median follow‐up time was 29.7 months. Interestingly, after adjustment for age, gender, and comorbidities (including diabetes), the RR for carpal tunnel surgery and mortality was 42% (statistically significant) and 10% lower (not statistically significant) in patients treated with HDF or HF.
\nIn 2006, Canaud et al. [53] reported results of a prospective, nonrandomized observational study from the Dialysis Outcomes and Practice Patterns Study (DOPPS) of 2165 patients followed between 1998 and 2001 in five European countries. Patients were stratified into four groups: low‐flux HD (n=1366), high‐flux HD (n=546), low‐efficiency HDF (n=156; substitution volume 5–14.9 L/treatment), and high‐efficiency HDF (n=97; substitution volumes 15–24.9 L/treatment). Patient characteristics (including age and sex), 14 comorbidities, and time on dialysis were similar in each group. High‐efficiency HDF patients had lower crude mortality rates than low‐flux HD patients. After Cox regression analysis with adjustment, high‐efficiency HDF patients had a 35% significantly lower mortality risk than those receiving low‐flux HD.
\nAlso in 2006, Jirka et al. published the results of an observational study of 2564 ESKD patients (394 on HDF) treated in Fresenius Medical Care clinics and followed for 12 months. Data were collected in the European Clinical Database (EuCliD) [54]. In this patient cohort, all‐cause mortality was reduced by 43% and unadjusted mortality was reduced by 35% in patients treated with HDF compared to HD. Convection volume was not reported.
\nIn 2008, Panichi et al. [7] reported the results of a prospective observational study performed in the northwestern part of Tuscany that included 757 ESKD patients (RISCAVID study) who were followed for 30 months. Treatment with low‐ or high‐flux HD (n=424) was compared to treatment with HDF using substitution fluid delivered in bags (130 patients on low‐volume HDF with acetate‐free biofiltration (AFB) and 74 patients on HDF with convection volumes of 10–15 L/treatment) and treatment with online HDF (129 patients on HDF with convection volumes of 22–25 L/session). Cox proportional hazards regression analysis showed that online HDF and bag HDF patients had a significantly better survival than HD patients, having a 22% reduced risk of all‐cause mortality after adjustment.
\nOne year later, Vilar et al. [55] reported the results of an observational study of 858 incident ESKD patients in the United Kingdom followed over 18 years. Patients treated with online HDF (n=232) received 79% of treatments with this modality and a mean filtration volume of 14.9 L/session. The control group was treated exclusively with high‐flux HD (n=626) and pure dialysis fluid. The mortality risk was significantly reduced in patients receiving predominantly HDF [hazard ratio (HR) 0.45; p<0.001] after adjustment for age, gender, body mass index, and comorbidities.
\nImamovic et al. reported the Balkan experience in 2014. In this cohort study of 442 incident patients, the risk of death for HDF‐treated patients relative to high‐flux HD patients was 0.87 (nonsignificant) for low‐volume HDF and 0.29 (highly significant) for high‐volume HDF. After adjustment for covariates, the HR for patients on low‐volume HDF remained statistically not significant compared to high‐flux HD (HR 0.84; nonsignificant), whereas patients on high‐volume HDF had a significantly lower HR (0.29; nonsignificant) than high‐flux HD. In the time‐dependent analysis, the mortality risk was not lower in high‐volume HDF compared to high‐flux HD (HR 0.48; nonsignificant), but this may be because 44% of the patients changed treatment modality during follow‐up [56].
\nIn 2015, Siriopol et al. published a report on the Romanian experience with HDF. In this study, the group of 221 prevalent patients treated with online HDF (mean convection volume was 22.2 L) was propensity score matched to a group of 431 patients treated with HD [57]. Online HDF was associated with a reduced mortality risk (HR 0.62; statistically significant). A second cohort consisting of 265 incident patients on HDF were matched with 530 patients treated with HD. The mortality risk was significantly lower in patients treated with HDF (HR 0.22; highly statistically significant).
\nIn another study, Canaud et al. reported results based on the EuCliD database involving 1590 incident patients in 12 European countries. The patient groups receiving high volume HDF (≥21 L/session) were propensity score matched to the group receiving HD. Patients were followed for 2 years (HD group) or 1.6 years (high‐volume HDF group) [58]. In this study, a nonsignificant survival advantage of HDF was found (HR 0.88; nonsignificant). Using inverse probability of censoring weighting (IPCW) to take bias due to the large amount of modality crossover during the follow‐up time into consideration (7% HDF patients switched to HD; 55% HD patients switched to high‐volume HDF), a statistically significant survival advantage of high‐volume HDF was found (odds ratio 0.501; highly significant).
\nCanaud et al. [24] recently published the results of a dose‐finding study exploring the optimal convection volume required to observe an increase in patient survival. This was a retrospective analysis involving 2293 incident HDF patients whose treatments were documented in the EuCliD database. Advanced statistical tools, including cubic spline analyses, were applied for the determination of the range of convection volume over which a survival benefit was observed. The relative survival rate of online HDF patients, adjusted for age, gender, comorbidities, vascular access, albumin, CRP, and dialysis dose, was found to increase at approximately 55L/week and to stay increased up to approximately 75L/week. Similar analysis of predialysis β2M concentrations found a nearly linear decrease in marker concentration, as convection volume increased from 40 to 75L/week. The analysis of log CRP levels showed a decrease over the same convection volume range.
\nMercadal et al. reported a study using data from the French national Renal Epidemiology and Information Network (REIN) registry to assess the effects of HDF on mortality in the total population of incident dialysis patients (treatments were performed between 01/01/2008 and 31/12/2011 and patients were followed up to the end of 2012) [59]. Analyses were performed at both patient and facility levels. Here, 5526 out of 28,407 ESKD patients used HDF for a median of 1.2 years and 2254 of them used HDF exclusively. All‐cause and cardiovascular mortality associated with HDF use were significantly reduced (HR 0.84 and 0.73, respectively). In patients treated exclusively with HDF, the beneficial effects on all‐cause and cardiovascular mortality were more pronounced (HR 0.77 and 0.66, respectively). At the facility level, increasing the percentage of patients using HDF from 0 to 100% reduced the HR for all‐cause and cardiovascular mortality (HR 0.87 and 0.72, respectively). The authors concluded that, irrespective of whether analyzed at patient or facility level, HDF treatment was associated with better survival.
\nFour prospective controlled or randomized studies comparing HDF and standard HD have been reported in the past which were not designed (short term <12 months) or sufficiently powered (<100 patients) to assess mortality differences between modalities [25, 38, 60, 61]. These will not be discussed in this chapter.
\nThe Dutch CONTRAST was performed in 29 centers in The Netherlands (n=26), Canada (n=2), and Norway (n=1) [62]. Here, 714 patients were randomized between treatment with low‐flux HD and online postdilution HDF between 2004 and 2009, both with ultrapure dialysate. The primary endpoint was all‐cause mortality, and the main secondary endpoint was a composite of fatal and nonfatal cardiovascular events. Of 358 HDF patients, 121 discontinued treatment during the study due to transplantation, switch to another center or therapy, or other reasons. Of 356 HD patients, 118 patients discontinued the allocated treatment. The mean follow‐up was 36 months (range 5–79 months); during this period, 269 deaths occurred in 2170 person‐years. All‐cause mortality (HR 0.95; nonsignificant) as well as cardiovascular events (fatal and nonfatal; HR 1.07; nonsignificant) were not affected by treatment modality. Although the target convection volume was set at 24 L/treatment, the mean volume achieved was 20.7 L/session and only one third of centers achieved at least 24 L/session. Post hoc analyses based on convection volume delivered (tertiles) showed a significantly lower mortality in patients receiving the highest convection volume (>21.95 L/session). In this subgroup, mortality risk was reduced by 39% compared to HD (HR 0.61; significant), which remained after extensive adjustments.
\nThe Turkish Hemodiafiltration Study with 782 patients was conducted between 2007 and 2010 in 10 centers; patients were randomized to treatment with online postdilution HDF or high‐flux HD [63]. Patients with central venous catheters, poor blood flow, and significant residual urine output were excluded. The primary endpoint was a composite of all‐cause mortality and first nonfatal cardiovascular event. Of 391 patients treated with HDF, 110 discontinued the study (28%), including 40 (10%) who terminated early due to vascular access problems. Of 391 patients randomized to HD, 90 patients (23%) dropped out. The mean follow‐up time was 23 months (range 1–38 months). The mean substitution volume was 17.2 L/session and the mean intradialytic weight gain was 2.4 L/treatment, corresponding to a mean total convection volume of 19.6 L/treatment. The RR of death and first cardiovascular event was 0.82 (nonsignificant), and the RR for all‐cause and cardiovascular mortality was 0.79 (nonsignificant) and 0.72 (nonsignificant), respectively. In a post hoc analysis, patients who achieved a convection volume above the median of 17.4 L/session had a significant lower risk of all‐cause and cardiovascular mortality (RR 0.54 and 0.29, respectively; both highly significant). This association remained after extensive adjustments for age, gender, comorbidity, and practice patterns.
\nThe Catalonian Hemodiafiltration Study (ESHOL) included 906 Spanish dialysis patients in 27 units who were randomized to online postdilution HDF (n=456) and HD (n=450) [64]. In the HD group, 8% of the patients were treated with low‐flux membranes and 92% were treated with high‐flux membranes. The mean follow‐up time was 23 months. Here, 355 patients dropped out of the study for various reasons and were censored at the time of loss (36% in the HD group and 42% in the HDF group). Centers involved in the study received a short training course on how to achieve the targeted convection volume. The median convection volume in HDF treated patients was 22.9 to 23.9 L/treatment. Here, 207 events were observed in 1730 patient‐years. A significant 30% decrease in all‐cause mortality and 33% decrease in cardiovascular mortality were observed in the HDF group. Interestingly, a post hoc analysis based on convection volume showed that the highest convection volume tertile (>25.4 L/treatment) was associated with a lower mortality risk (HR 0.55; highly significant) compared to HD patients.
\nSeveral meta‐analyses comparing conventional HD and convective‐based therapies have been published over the last decade.
\nRabindranath et al. from the Cochrane group performed two analyses in 2005 and 2006. The latter included 20 trials (657 patients). Mortality results were available only in 4 trials (336 patients) and different therapies were mixed (AFB, HF, and HDF). The authors found no difference in mortality risk for patients treated with convective‐based therapies [26, 65]. This systematic review was severely criticized due to its poor methodology [66, 67].
\nSusantitaphong et al. [49] compared convective‐based therapies to standard low‐flux HD. HF, HDF, AFB, and high‐flux HD were all included in the convective therapy group. This meta‐analysis aggregated data of 12,182 patients. Convective therapies resulted in a nonsignificant decrease in all‐cause mortality (RR 0.88) and all‐cause hospitalization (RR 0.91); a significant decrease in therapy‐related hypotension (RR 0.55) and cardiovascular mortality (RR 0.84) was reported. The authors concluded that convective therapies were associated with improved clearance of uremic solutes, but the potential long‐term benefits of specific convective modalities could not be confirmed.
\nWang et al. [68] conducted a systematic review and meta‐analysis that included 16 trials and 3220 patients treated with convective‐based therapies (HDF and HF) and with standard HD (low- and high-flux). Convection volume was not considered as a confounder in this analysis. On the one hand, the authors concluded that convective modalities did not significantly reduce the risk of cardiovascular events (RR 0.85) or all‐cause mortality (RR 0.83). On the other hand, they noted that convective modalities reduced intradialytic symptomatic hypotension (RR 0.49) and reduced serum β2M levels (‐5.95 mg/L).
\nMostovaya et al. [69] compared exclusively HDF to HD (low- and high-flux) including 2402 patients. The meta‐analysis identified six RCTs. The convective arm consisted exclusively of HDF patients treated with different HDF modes (a mixture of postdilution, mid‐dilution, and predilution HDF, and of online HDF and HDF with bags) and achieving a specified minimum convection volume. All‐cause and cardiovascular mortality were reduced with HDF compared to HD (RR 0.8 and 0.73, respectively).
\nNistor et al. [70, 71] from the Cochrane group updated the previous systematic review of 2005 and compared HD (low- and high-flux) to convective‐based modalities (HF, AFB, bag HDF, and online HDF) without considering convection volume. Thirty‐five trials (4039 patients) were included in this meta‐analysis and the effects on mortality were estimated. The convective group consisted of 1648 patients, but 227 of them were treated with low convection volumes. Within the limitations of the review (e.g., studies reviewed were partially old and referred to a diverse mixture of HDF modalities), the authors concluded that convective therapies had no significant effect on reducing all‐cause mortality (RR 0.87), cardiovascular mortality (RR 0.75), and intradialytic hypotension (RR 0.72) but had uncertain effects on nonfatal cardiovascular events (RR 1.14) and hospitalization (RR 1.21).
\nAn alternative to the aggregated data meta‐analysis approach is to perform meta‐analysis of individual participant data in which the raw “individual level data” for each study are obtained and analyzed. The term “individual participant data” relates to the data recorded for each participant in a study. Individual participant data sets of four randomized trials were pooled and used to compare online HDF to HD. The four studies aggregated 2793 patients and were designed to examine the effects of HDF on mortality endpoints. Bias by informative censoring of patients was resolved. HRs comparing the effect of online HDF versus HD on all‐cause and cause‐specific mortality were calculated using Cox proportional hazard regression models.
\nIn the first part of this individual participant data meta‐analysis, Davenport et al. [72] analyzed the relationship between convection volume and patient outcomes. After a median follow‐up time of 2.5 years, 769 of the 2793 participants had died (292 cardiovascular deaths). Convection volumes were either not standardized or standardized to weight, body mass index, body surface area, and total body water. Data were analyzed by multivariable Cox proportional hazards modeling from 2793 patients. All‐cause mortality was reduced when the convective dose was unstandardized or standardized to body surface area or total body water; corresponding HRs were 0.65 (0.51–0.82), 0.74 (0.58–0.93), and 0.71 (0.56–0.93). Standardization by body weight or body mass index was not associated with significant survival advantages. Higher convection volumes were generally associated with greater survival benefit with online HDF, but results varied across the different ways of standardization for body size. Further studies should take body size into account when evaluating the impact of convection volume on mortality endpoints.
\nIn the second part of this analysis, Peters et al. [73] also investigated the effects of convection volume on patient outcomes. HRs comparing the effect of online HDF versus HD on all‐cause and cause‐specific mortality were calculated using Cox proportional hazards regression models. The relationship between convection volume and the study outcomes was examined by delivered convection volume standardized to body surface area. Online HDF reduced the risk of all‐cause mortality by 14% and cardiovascular mortality by 23%. There was no evidence for a differential effect in predefined convection volume subgroups. The largest survival benefit was for patients receiving the highest delivered convection volume (>23 L/1.73 m2 body surface area per session), with a multivariable‐adjusted HR of 0.78 (95% confidence interval 0.62–0.98) for all‐cause mortality and 0.69 for cardiovascular disease mortality. This pooled individual participant analysis indicates that online HDF reduces the risk of mortality in ESKD patients. This effect holds across a variety of important convection volume subgroups of patients and is most pronounced for those receiving a higher convection volume normalized to body surface area.
\nOnline HDF can no longer be considered an experimental treatment; it is a mature RRT that is applied daily to sustain the lives of more than 160,000 ESKD patients worldwide, including 80,000 in Europe, Middle East, and Africa. Europe has played a leading role in developing this therapy, where the prevalence of HDF is close to 18% with variations across countries from 0 to 100%.
\nInterestingly, it has recently been also shown that clinical benefits associated with HDF were directly correlated with the total ultrafiltered volume delivered, either per session or per week. This finding adds a new component, namely convective dose, that needs to be integrated into our conventional dialysis adequacy concept. In addition, accumulating evidence from both retrospective and prospective RCT studies confirm the clinical safety and sustainability of HDF therapy and support its superiority over conventional HD in terms of morbidity and mortality.
\nIn line with these facts, the remaining and crucial challenges today are to implement best clinical practices to achieve the optimal convective dose required, to define which subset of ESKD patients should benefit most, and to evaluate new tools facilitating and fine‐tuning HDF prescription according to ESKD patient needs (e.g., electrolyte balancing and quantification and homeostasis).
\nConflict of interest: Bernard Canaud, Aileen Grassmann, Laura Scatizzi, and Daniele Marcelli are employees of Fresenius Medical Care.
\nCompetitive advantage is a major determinant of firm survival and sustained profitability and perhaps the most decisive factor that enables emerging market multinational enterprises (EMNEs) to catch up with MNEs from developed countries. In the existing research which delved into competitive advantage in emerging market countries, foreign direct investment (FDI) is considered the most important way to enhance EMNE competitive advantage and technological catch-up [1, 2, 3]. Luo and Tung [4] pointed out that, due to weaker innovation capabilities and knowledge of resource reserves found in their home countries, EMNEs use FDI as a ‘springboard’ by adding strategic assets to establish a competitive advantage and achieve corporate growth. M&As undertaken by EMNEs have increased significantly over time, especially China’s cross-border ones. According to Thomson Reuters and Price Water House Coopers, in 2016 Chinese firms undertook as many as 923 cross-border M&A transactions (excluding M&As in Hong Kong SAR and Macao) representing a 142% increase from 2015.
One of the most active fields of innovation, and also one of the fiercest areas of technological competition globally, is represented by the new generation of information and communications technology (ICT) firms which focus on cloud computing, big data, and artificial intelligence. The ICT industry has incurred substantial FDI especially across emerging economies [5, 6]. The ICT industry is also characterised by significant technological dynamism as a high proportion of firms engage in internationalisation to obtain strategic assets outside of the firm boundaries [7]. The Deloitte’s 2017 China TMT Industry Overseas M&A Report shows that between 2012 and 2016, the growth rate of cross-border M&A transactions in the Chinese technology, media, and telecom (TMT) industry reached 20%. In contrast, their worldwide growth rate reached 8% in the same period. China has become the most active M&A initiator in the TMT industry. As one of the ten key areas of China’s manufacturing, the new generation of information technology plays a pivotal role in China’s implementation of innovation-driven development strategies and high-quality foreign cooperation. However, since 2017, cross-border M&As of China’s ICT industry has faced more stringent scrutiny by the United States and EU countries. The U.S. government identifies ICT as a critical technology industry, especially in the 5G and semiconductor arena. Foreign direct investment in these industries has been subject to strict security scrutiny. For example, in February 2017, the Chinese state-owned enterprise (SOE) named National Integrated Circuit -Industry Investment -Fund Co., Ltd. acquired Xcerra, an American semiconductor testing equipment manufacturer. This acquisition was quickly objected to by the Committee on Foreign Investment in the United States (CFIUS), on national security grounds. Xcerra and the Chinese acquirer signed the termination agreement on the same day. Therefore, in the current international investment environment, it is necessary to reconsider whether cross-border M&As are the best way for Chinese ICT enterprises to enhance their core competitiveness. A case in point is whether or not the background of state-owned enterprises enhances or hinder the enterprises’ use of external strategic resources to enhance their competitive advantage.
Recent studies have focused on the adaptation of internalisation theory for EMNEs [3, 8]. Such theory states that foreign investments undertaken by EMNEs in the absence of firm-specific-advantages (FSAs) are made to acquire FSAs and the intangibles of foreign firms [9]. Therefore, the goal of foreign investment is not to exploit FSAs but to acquire them [10]. However, little empirical research has been conducted to examine the actual effect and impact path of cross-border M&As on FSAs of EMNEs. In that context, internalisation theory proposes that FSAs can either be transferable (non-location-bound) such as technological knowledge, or specific to a local context (location-bound) such as local market knowledge and access to local networks [11, 12]. Some studies believe that firms from emerging economies such as China, adopt cross-border M&As to gain strategic resources and then redeploy and integrate them to establish a competitive advantage which can be transferred in the global market [2, 4]. Other scholars pointed out that EMNEs undertake cross-border M&As in order to strengthen their strategic assets in their home countries and seek local—rather than global—expansion by leveraging cheap labour, natural resources, and institutional advantages of their home countries [13]. Benefiting from the rapid economic growth of their home countries, EMNEs can expand into local markets and gain considerable economic returns through cross-border M&As [14]. However, many EMNEs have fallen into a pattern of continuous integration and strengthening of external and regional bundling assets, giving them leverage in their home markets [15]. For example, upon acquiring Volvo, Geely Automobile achieved a rapid revenue growth at the expense of Volvo’s strong R&D capabilities and China’s huge auto market, low labour costs, and policy support. However, Geely’s overseas sales’ share has been declining, with a 2016 domestic sales ratio of 97.2%. Strategic assets acquired through CBAs can only establish an EMNE’s competitive advantage if they are attached to home market supports such as natural resource endowments, labour force, market scale, and local culture [16].
Although the existing literature favours the impact of cross-border M&As, mixed results have been found concerning the impact on different types of competitive advantage. To examine the inconsistency of current views, this paper distinguishes and defines two types of competitive advantage of EMNEs according to their boundedness to its home country. On the one hand, home-country-bound competitive advantages are defined as a location-bound FSA which is highly dependent on the home market and cannot transferred to other locations and therefore limits the capability of EMNEs to become global firms, e.g. home market scale [14], political connection [17]. On the other hand, non-location-bound competitive advantages are defined as those that can be transferred within the enterprise itself and can increase leverage in the global market without the boundness of location, e.g., global brand, and technology knowledge [18]. As opposed to home-country-bound competitive advantages, non-location-bound competitive advantages can reduce the liability of foreignness, and are associated with a greater capacity to penetrate geographically distant markets and achieve a global geographic scope [7]. They are therefore the rationale for measuring the internationalisation ability of enterprises [19, 20], and act as the criterion to test whether cross-border M&A can constitute the “springboard” of internationalisation [20, 21].
State ownership has also been a topic of research in cross-border M&As of EMNEs [22]. Compared to non-SOEs, characteristics of SOEs such as non-economic motivations, long-term orientation, and different risk preferences influence the foreign entry strategies of SOEs [23]. Some literature suggests that the state can provide state-owned enterprises (SOEs) with monopolist advantages at home by creating and enforcing rules that shape market entry and transactions, and by providing preferential access to financial resources [24]. SOEs have better access to resources in their home countries than non-SOEs, and thus are less concerned about the high operating costs associated with larger geographic distances [25]. On the other hand, the state’s economic and social objectives can inhibit the SOEs’ ability to develop FSAs [26]. However, the existing literature focuses on whether SOEs can enhance their competitive advantage through cross-border M&As and what kind of advantage they can attain and remains quite controversial.
We studied the effect of cross-border M&As on enterprise competitive advantage by distinguishing between home-country-bound competitive advantage and non-location bound competitive advantage in Chinese ICT firms’ contexts. We addressed the following research questions:
Can cross-border M&As improve the non-location-bound competitive advantages of Chinese ICT firms?
Does state ownership enhance the competitive advantage of Chinese ICT firms through cross-border M&As?
This research enriches the understanding of FSAs and internalisation theory by building on insights from home country dependency and by taking into account the particular heterogeneity of EMNEs such as internationalisation experience and state ownership. We examined cross-border M&As data from Chinese firms in the information and communication technology industry for the period of 2010–2017. Using the propensity-score matching and differences-in-differences approaches, we compared variations in the competitive advantages of Chinese firm that have undertaken CBAs and those that have not, to observe the net effect of CBAs on competitive advantage. We compared the short-term and long-term effects and examined the impact mechanism of CBAs on competitive advantage.
This study contributes to the existing literature in the following ways. First, based on the transferability of competitive advantage, it distinguishes and quantifies home-country-bound competitive advantage and non-location bound competitive advantage, and establishes a framework for how cross-border M&As enhance enterprise competitive advantage. Second, it empirically analyses the effects and impacts of cross-border M&As on competitive advantage, deepening the internal logic of M&As and competitive advantage. Third, this paper focuses on the moderating effects of firm heterogeneity and internationalisation experience and enterprise ownership to find out the differences between different types of ICT enterprises in obtaining competitive advantage through cross-border M&A. It provides an explanation for the extant research on whether SOEs can enhance their competitive advantage through cross-border M&As, and what kind of advantage they attain. The empirical results have distinct implications for the Chinese government in redirecting the FDI endeavours of Chinese enterprises.
This chapter is structured as follows. We first outline the theoretical foundations, develop our hypotheses, and describe the research design and methodology. Then, we present and discuss the results. We conclude by discussing theoretical and managerial implications, recognising limitations, and suggesting future research possibilities.
Essentially, to compete successfully in any given environment, firms need to cross certain asset thresholds. Different types of complementary assets determine specific asset thresholds, which must be bundled together. The asset threshold attained by this ‘bundling’ determines an enterprise’s competitive advantage. Furthermore, having acquired external strategic assets through cross-border M&As, enterprises also need to integrate and bundle them with their own internal ones. Some enterprise assets are not firm-specific but are associated with those particular locations in which an enterprise only has ‘special access rights. ‘Bundling’ external strategic assets and assets with different attributes determines alternative asset thresholds—i.e., cross-border M&As will have contrasting impacts on regional competitive advantages (linked to specific locations) and non-regional competitive ones (that are not location-specific).
First, enterprises ‘bundle’ external strategic assets with firm-specific-ones to create a non-regional competitive advantage, which we define as a non-location-bound competitive advantage. After the completion of cross-border M&As, firms integrate external strategic assets with their own tangible specific ones in order to develop a competitive advantage that can be transferred within the enterprise itself. On the other hand, cross-border M&As also help connect with suppliers and new customers, broadening the scope of access to external complementary strategic assets and best innovation practices [27], enhancing technology and management levels, and transferring and deploying technology and management experience in the global market [2]. Once a merger is complete, the focus is on enhancing the EMNE’s weak absorption of external strategic assets. Second, an enterprise ‘bundles’ external strategic assets with non-firm-specific ones to which the enterprise only has ‘special access rights’, such as industry franchise rights granted by the home country government and actual knowledge of the home market in order to create a regional competitive advantage that we define as ‘home-country-bound’. Although the rapid growth of emerging economies has increased domestic market demand, indigenous technology applicability is generally low [28]. In order to establish economies of scale in their home countries and seize market opportunities, enterprises acquire strategic resources through cross-border M&As and introduce these resources directly into their domestic markets [13], using low manpower and resource costs, and the institutional preferential policies of their home countries to expand their scale of production. In addition, firms also promote integration into overseas business networks, enhance professional levels, and achieve geographical matching of product development, production, and sales [29]. That is to say that, when faced a huge domestic market demand, enterprises further enhance their scale efficiency advantage through cross-border M&As. Based on the above arguments, we established a theoretical framework for how cross-border M&As enhance competitive advantage (Figure 1), and hypothesised that:
Theoretical framework.
Hypothesis 1. Cross-border M&As effectively promote the improvement of home-country-bound and non-location-bound competitive advantages.
Besides providing an important channel for firms to obtain strategic resources, cross-border M&As also increase the complexity of operations as firms need to coordinate resources located in different geographical locations [2], and corporate heterogeneity and transaction uncertainty diversify corporate strategy and performance [30, 31]. At the same time, research has been conducted in the context of a large-scale ‘going out’ of Chinese enterprises into cross-border M&As conducted by firms with government support. Thus, we chose internationalisation experience and state ownership as variables to analyse the adjustment mechanism of cross-border M&As and competitive advantage. First, empirical knowledge is an important resource component of an enterprise, as it plays a key role in international business and strategic management and constitutes a core element of the Uppsala internationalisation model [32]. Second, state ownership is the most important institutional factor affecting firms in emerging economies [22]. Different ownership systems have several home country institutional resources, such as low-interest financing and tax reduction [33], and each system’s strategic goals of internationalisation are also significantly different. Enterprises with state ownership can access more institutional resources in their home countries, such as low-interest financing and tax relief [33]. Hence, their internationalisation goals are usually different from those of private enterprises.
EMNEs face the challenges posed by their ‘liability of emergingness’ and are often the first generation of firms venturing abroad from their home countries, depending significantly on home country market scale [34]. An important challenge that firms face when they seek to enter foreign markets relates to their being outsiders in local networks and to their lack of knowledge of local business opportunities. However, it can be argued that firms with region-specific experience are less likely to suffer from such liability, as participation in regional networks in the internationalisation process provides them with better access to local resources and institutional contacts [35].
Organisational learning theory [36] suggests that firm experiences result in the creation of knowledge that significantly influences firm strategies and related outcomes. Firms are viewed as routine-based systems wherein prior experiences are coded into routines that guide future behaviours. In the context of cross-border M&As, internationalisation experience has also been closely linked to the ability of the acquiring firm to absorb new information related to potential targets, something that can be valuable in the improvement of competitive advantage. However, when acquiring firms are lacking in prior experience, their ability to absorb and assess acquisition-related information is generally limited [35].
In addition, the promotion of cross-border M&As for competitive advantage needs to be realised through resource integration and resource restructuring, which requires firms to have a rich international experience to deal with the integration challenges they face in the aftermath of M&As [33]. Transfer of capabilities between acquiring and target firm as one of the main pillars of integration process, which in turn is argued to have direct and indirect effects on other aspects of post-acquisition performance. However, due to its tacit and socially complex nature, transfer of knowledge across organisational boundaries is not an easy task [37]. Johanson and Vahlne [38] pointed out that such rich international experience helps firms to identify opportunities and risks, strengthen their ability to integrate global resources, and enables them to effectively build business units in overseas markets. Technology acquisition M&As allow firms integrate suppliers and customers in the value chain, leading to horizontal or vertical expansion and to the extension of technological knowledge. By the same token, a lack of international experience hinders the identification and application of technological knowledge [34], which may cause the emergence of technological reverse spillover effects [39]. In addition, EMNEs that lack an ownership advantage face more serious disadvantages and need greater resources and capabilities to cope with the adverse effects of institutional differences. International experience is an important knowledge resource for firms to cope with institutional conflicts in host countries and overcome the competitive disadvantages engendered by institutional uncertainty [40], creating a good external environment for the use of strategic resources. Based on the above arguments, we hypothesise that:
Hypothesis 2. Internationalisation experience moderates the effect of cross-border M&As on home-country-bound and non-location-bound competitive advantages, as these positive gains will be positively correlated to the richness of a firm’s internationalisation experience.
Particularly in global M&As, actors outside the merging organisations, such as government, unions, and investors, can become involved in merging process [41]. State-owned enterprises (SOEs) may face more prominent liabilities of emergingness due to the close relations maintained with home-country governments, the threat of financial protectionism, reduced MNE transparency with state ownership [42], loss of key national strategic assets, and whether home-based management practices can be transferred to advanced economies [34]. Although the political connections linked to government ownership help in building legitimacy and prestige in domestic markets, these firms still participate in global competition and struggle to gain legitimacy in foreign ones [43, 44].
Our analysis of state owned EMNEs focussed on the influence of home governments on the internationalisation strategies of domestic enterprises. As agents of their home governments, SOEs garner more policy support and have stronger institutional advantages [24], including ‘special access’ to key resources [10, 45]. However, the internationalisation of SOEs ultimately discloses the will of the government and the need to strike a balance between economic and political goals [46]. Specifically, state ownership affects a company’s resource input and internationalisation strategies and goals [47]. Both state-owned and non-SOEs face pressure from formal or informal institutions in their home countries, and their responses to these pressures are influenced by their dependency on resources. If they are highly resource-dependent, enterprises will choose to comply with institutional pressures to avoid negative consequences [48]. As SOEs are highly dependent on the government for their resource input, they are more likely to abide by political goals over economic ones. As non-state-owned firms are more focussed on profit and efficiency goals, their motivation to seek and utilise overseas strategic resources is stronger and more efficient [47]. Based on the above arguments, we hypothesise that:
Hypothesis 3. State ownership moderates the effect of cross-border M&As on home-country dependent and non-location-bound competitive advantage. The positive effects will be smaller if a company is a state-owned enterprise.
We tested the hypotheses by relying on China’s listed information and communications technology (computers, communications, and other electronic equipment) manufacturing firms. The selection of ICT firms was based on the following three considerations. Firstly, the new generation of ICT technology, representing cloud computing, big data and artificial intelligence, has become one of the most active fields of innovation, and one of the fiercest areas of technological competition among major countries. Besides, the ICT industry had substantial FDI around the world and, especially across emerging markets [5]. Secondly, a large number of ICT manufacturing enterprises have implemented cross-border M&As. Deloitte’s ‘2017 China TMT Industry Overseas M&A Report’ shows that, between 2012 and 2016, the annual compound growth rate of overseas M&A transactions in China’s TMT industry reached 20%. Thirdly, The ICT industry is also characterised by significant technological dynamism and a high number of firms engaging in internationalisation to obtain strategic assets outside the firm boundary. The new generation of the information technology industry plays a pivotal role in China’s implementation of innovation-driven development strategies and improving the country’s competitiveness. Under these conditions, can Chinese ICT manufacturing enterprises improve their competitive advantage through cross-border M&As? The answer to this core issue has fundamental practical significance.
We collected M&A data and financial data from two datasets, one of which was derived from the BVD (Zephyr) database, which is a well-known international M&As database and widely used in cross-border M&A research [49, 50]. However, distorted data points remained. Thus, it was necessary to clean the original data and removed outliers before further analysis could be conducted by using the following filtering criteria: first, we only retained M&A transactions with a ‘completed’ or ‘completed assumed’ status, and confirmed the completed M&A transactions through the company’s website, annual reports, and financial news. Regarding multiple cross-border M&As of a company within a year, there were multiple transactions recorded around the target firm for tax minimization purposes [51].
The measurement of the cross-border M&As experience advanced by Jiang [52] suggests the approval time of multinational enterprises occurs well after the time of the M&A, which may render an inaccurate recording of the subsequent M&As′ experiences that take place within the same year. Thus, we only retained the first data recorded, when the same company had implemented multiple M&As with different targets in a year. Third, we removed any samples missing M&A information and the sample of capital increase for foreign subsidiaries [53, 54]. These excluded samples were not significantly different from retained cases as far as characteristics such as the percentage of shares acquired, ownership structure of the acquiring firm, or the acquirer’s acquisition experience in a target’s country. The second dataset used was taken from the CSMAR database, and included three major accounting statements—the balance sheet, cash flow statement, and income statement, which provide detailed financial information for the company. The authors obtained an effective sample data of 98 cross-border M&As undertaken from 2010 to 2017. The sample description is shown in Table 1.
Distinguishing the correlation and causality between cross-border M&As and the growth of competitive advantage effect brings some challenges. This is particularly evident when enterprises with strong competitive advantage conduct cross-border M&As and the change of competitive advantage may be endogenous and self-selected. Therefore, performing an OLS estimation would be invalid. Following extant literature [47], we used the propensity score matching to assess the causal effect of cross-border M&As on the competitive advantage change of Chinese ICT firms.
First, we used the nearest-neighbour matching method to divide the ICT industry firms into two groups: one with those firms that had implemented cross-border M&As (denoted as treatment group), and the other with those that had not (denoted as control group), where the construction enterprise was a virtual variable
We constructed the dummy variable
where i denotes the firms in treatment group, j denotes the samples in control group, t represents time,
Following Jiang [52], we selected overall total factor productivity (
Ownership | Province of acquirer | Equity of acquirer (%) | Related M&A (If the first two SIC codes are the same) | Deal value (Million Euros) | Host country | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
SOE | 31 | Guangdong | 34 | <50 | 29 | Related M&A | 83 | <10 | 52 | U.S. | 25 |
Non-SOE | 67 | Jiangsu | 16 | 50–100 | 21 | Non-related M&A | 15 | 10–100 | 29 | Germany | 9 |
Beijing | 14 | 100 | 48 | >100 | 17 | Canada | 8 | ||||
Zhejiang | 14 | U.K | 5 | ||||||||
Shandong | 7 | Japan | 5 | ||||||||
Hebei | 5 | Italy | 4 | ||||||||
Shanghai | 3 | Malaysia | 4 | ||||||||
Sichuan | 3 | Australia | 3 | ||||||||
Fujian | 1 | Israel | 3 | ||||||||
Gansu | 1 | Others | 32 |
Sample description.
Before matching (TFP) | After matching (TFP) | Treatment group | Control group | Matching results | |||||
---|---|---|---|---|---|---|---|---|---|
Treatment group | Control group | t value | Treatment group | Control group | t value | ||||
2010 | 0.315 | 0.412 | −1.832* | 0.315 | 0.332 | −0.890 | 3 | 93 | 6 |
2011 | 0.282 | 0.337 | −0.954 | 0.282 | 0.308 | −0.271 | 9 | 115 | 20 |
2012 | 0.367 | 0.750 | −4.831*** | 0.367 | 0.323 | 0.600 | 7 | 137 | 15 |
2013 | 0.425 | 0.794 | −8.152*** | 0.425 | 0.458 | −0.321 | 8 | 146 | 13 |
2014 | 0.603 | 0.745 | −3.440*** | 0.603 | 0.663 | −0.412 | 13 | 144 | 13 |
2015 | 0.607 | 0.780 | −2.551** | 0.607 | 0.633 | −0.370 | 21 | 163 | 22 |
2016 | 0.661 | 0.631 | 0.774 | 0.661 | 0.683 | −1.000 | 24 | 202 | 28 |
2017 | 0.793 | 0.626 | 4.900*** | 0.793 | 0.746 | 0.100 | 13 | 223 | 14 |
PSM matching results.
In order to save space, the above table does not report the matching results of Scale, Capital and ROA. As the duplicate matching samples are excluded, the matching results are not presented 1:3. We performed the test of robustness matching according to the ratio of 1:1 and 1:2, and obtain similar results without affecting the conclusion of this paper.
Note: *, **, *** indicate significance level at 10%, 5% and 1%, respectively.
On the basis of the data matching results, we used the differences-in-differences method to analyse the impact of cross-border M&As on home-country dependent and non-location-bound competitive advantage, constructing the respective dummy variables CBA
As the
Then, we converted Eq. (3) into an econometric model that could be empirically tested as follows:
where the
Our dependent variables were home-country-bound competitive advantage and non-location-bound competitive advantage. Accordingly, home-country-bound competitive advantage refers to competitive advantage with location-bound attributes gained by ‘bundling’ external strategic assets with the resources, markets, and institutional environments of the home country. Non-location-bound competitive advantage refers to competitive advantage with non-location-bound attributes gained by ‘bundling’ external strategic assets with the acquirer’s firm-specific assets, such as knowledge and technology, which can be transferred between enterprises.
Within large and fast-growing emerging market economies that correspondingly have large and growing domestic demand bases, economies of scale are an important competitive advantage. EMNEs have non-traditional FSAs that enable them to better exploit scale economies of home countries [45], which is the home-country dependency advantage as defined in this paper. While all firms in home markets potentially have access to economies of scale, some can leverage it better. The home-country-bound competitive advantage between EMNEs is different as the heterogeneity exists in the ability of EMNEs to leverage scale economies [10]. The change in economies of scale decomposed from the TFP index measures how firm-level production diverges from a constant return to scale [57]. Thus, we measured the home-country-bound competitive advantage by scale efficiency following Bhaumik et al. [21]. The increase in scale efficiency reflected the use of external strategic assets to develop home country resources and markets, which constitutes the basis for competition between EMNEs and developed economy MNEs (DMNEs) in the home market.
We measured non-location-bound competitive advantage through pure technical efficiency [21]. Improvements of technical efficiency reflect the extent to which enterprises embed external strategic assets and develop their own firm-specific-ones, reflecting the strategic goal of EMNEs to use cross-border M&As as a ‘springboard’ to catch up with DMNEs [58].
We used the stochastic frontier model to measure the overall efficiency level (
where
where
where
while the overall efficiency level (
where
Our independent variable was the interaction term
Internationalisation experience (
State ownership (
We controlled for firm heterogeneity, time effects, and regional fixed effects. Firm heterogeneity was controlled through capital density (
Variables label | Variables definitions | Variables calculation | Data source |
---|---|---|---|
HCB | Home-country-bound competitive advantage | Stochastic frontier model and the Malmquist decomposition method | CSMAR database |
NLB | Non-location-bound competitive advantage | ||
CBA × CT | Cross-border M&As Implement | Dummy variables CBA and CT indicate whether a company had implemented cross-border M&As and the completion time | Bvd-zephyr database. |
Experience | Internationalisation experience | Calculated by the difference between the overseas investment approval time and the sample observation period. | China’s Ministry of Commerce’s ‘Investment List of Overseas Investment Enterprises’; Bvd-zephyr database. |
Ownership | State ownership | Dummy variable, non-SOEs was 0 and SOEs was 1. | CSMAR database |
Capital | Capital density | Expressed as the natural logarithm of the ratio of net fixed assets to the number of employees | CSMAR database |
Size | Enterprise size | Measured by the natural logarithm of the number of employees; | CSMAR database |
R&D | R&D investment | Expressed as the percentage of R&D investment in operating income | CSMAR database |
Market | Marketing expenditures | Expressed as the percentage of sales revenue in operating income | CSMAR database |
Time | Time effect | Dummy variable reflects year of samples | CSMAR database |
Region | Region effect (dummy variable) | Dummy variable reflects province of the acquirers | CSMAR database |
Variables definitions and sources.
Table 4 san shows the estimation results of the M&As′ instantaneous competitive advantage effect. Models (1) through (4) examine the impact of cross-border M&As on the home-country-bound competitive advantage. Models (5) through (8) examine the impact of cross-border M&As on the non-location-bound competitive advantage. The results of model (1) show that the coefficient of the interaction term
Dependent variable: HCB | Dependent variable: NLB | |||||||
---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
−0.054*** (0.017) | −0.063*** (0.017) | −0.048*** (0.014) | −0.050*** (0.025) | −0.034 (0.025) | −0.043* (0.025) | −0.034* (0.020) | −0.041** (0.021) | |
−0.104*** (0.014) | −0.109*** (0.014) | −0.044*** (0.011) | −0.028** (0.011) | −0.036* (0.022) | −0.050** (0.022) | −0.066*** (0.019) | −0.056*** (0.019) | |
0.044** (0.019) | 0.047** (0.019) | 0.033** (0.016) | 0.029* (0.016) | 0.033 (0.033) | 0.036 (0.033) | 0.033 (0.025) | 0.033 (0.025) | |
0.141*** (0.020) | 0.138** (0.068) | 0.104*** (0.018) | 0.001* (0.001) | 0.002 (0.001) | 0.002 (0.001) | |||
0.156*** (0.037) | 0.133*** (0.034) | 0.135** (0.064) | 0.001* (0.001) | 0.004 (0.002) | 0.003 (0.002) | |||
0.047*** (0.011) | 0.028*** (0.007) | 0.031*** (0.008) | 0.083*** (0.009) | 0.102*** (0.009) | 0.096*** (0.009) | |||
0.026** (0.013) | 0.016* (0.009) | 0.012* (0.006) | −0.001 (0.002) | −0.008 (0.009) | −0.012 (0.021) | |||
0.206*** (0.013) | 0.018 (0.139) | 0.016 (0.141) | 0.168 (0.152) | 0.655*** (0.016) | 0.272 (0.200) | 0.543*** (0.190) | 0.952*** (0.205) | |
no | no | yes | yes | no | no | yes | yes | |
no | no | no | yes | no | no | no | yes | |
1343 | 1343 | 1343 | 1343 | 1343 | 1343 | 1343 | 1343 | |
0.071 | 0.141 | 0.376 | 0.432 | 0.003 | 0.310 | 0.482 | 0.514 |
Instantaneous competitive advantage effect of cross-border M&As.
*, **, *** indicate significance level at 10%, 5% and 1%, respectively. Standard errors are in parenthesis. The samples from the above test included the lag phase samples of the treatment group and the control group, and the subsequent tables were the same.
It takes time to bundle external strategic assets with regional ones in the home country or non-regional bundled firm-specific assets. The effect of cross-border M&As on competitive advantage may be affected by a lag. Therefore, we examined the changes in the home-country-bound competitive advantage and the non-location-bound competitive advantage five years after cross-border M&As. These results are shown in Table 5. The coefficient of the interaction term
Dependent variable: HCB | Dependent variable: NLB | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
(1) Year 1 | (2) Year 2 | (3) Year 3 | (4) Year 4 | (5) Year 5 | (6) Year 1 | (7) Year 2 | (8) Year 3 | (9) Year 4 | (10) Year 5 | |
−0.041*** (0.014) | −0.028** (0.012) | −0.011 (0.010) | −0.014 (0.010) | −0.014 (0.013) | −0.038* (0.020) | −0.043** (0.020) | −0.056*** (0.019) | −0.044** (0.018) | −0.029* (0.017) | |
−0.038*** (0.012) | −0.027*** (0.010) | −0.031*** (0.010) | −0.029** (0.012) | −0.020 (0.015) | −0.033* (0.019) | −0.035* (0.019) | −0.042** (0.020) | −0.037* (0.021) | −0.024 (0.025) | |
0.022** (0.010) | 0.009* (0.005) | −0.008 (0.011) | −0.004 (0.014) | −0.018 (0.019) | 0.019 (0.025) | 0.032 (0.027) | 0.046 (0.029) | 0.035 (0.031) | 0.028 (0.039) | |
0.013 (0.147) | 0.193 (0.137) | −0.012 (0.090) | −0.025 (0.103) | −0.075 (0.109) | 0.960*** (0.226) | 1.603*** (0.248) | 1.535*** (0.260) | 1.875*** (0.243) | 2.535*** (0.244) | |
yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | |
yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | |
yes | yes | yes | yes | yes | yes | yes | yes | yes | yes | |
1140 | 945 | 769 | 607 | 458 | 1140 | 945 | 769 | 607 | 458 | |
0.402 | 0.395 | 0.251 | 0.266 | 0.273 | 0.679 | 0.732 | 0.610 | 0.552 | 0.590 |
Long-run competitive advantage effect of cross-border M&As.
Note: *, **, *** indicate significance level at 10%, 5% and 1%, respectively.
The above results show that the improvement of competitive advantage is manifested in the dependency on regional resources of the home country. In order to further confirm and clarify the path of improvement, we analysed the impact mechanism of cross-border M&As by examining various post-merger business practices that affect competitive advantage based on the process perspective. Specifically, we used the interaction term
Capital | Size | R&D | Market | |
---|---|---|---|---|
0.035*** (0.003) | 0.026*** (0.003) | −0.018** (0.007) | 0.014 (0.011) | |
0.022*** (0.005) | 0.020 (0.016) | 0.016 (0.016) | 0.014*** (0.003) | |
0.026*** (0.005) | 0.020*** (0.005) | −0.016 (0.011) | 0.008** (3.733) | |
−0.314*** (0.026) | −0.214*** (0.018) | 0.212*** (0.024) | 0.114*** (0.007) | |
yes | yes | yes | yes | |
yes | yes | yes | yes | |
1343 | 1343 | 1343 | 1343 | |
0.316 | 0.422 | 0.295 | 0.384 |
Path analysis of the M&A competitive advantage effect.
Note: *, **, *** indicate significance level at 10%, 5% and 1%, respectively.
The results show that cross-border M&As play a significant role in promoting corporate capital density, enterprise size, and marketing expenses; while their impact on R&D investment is not significant, as the coefficient value is negative. The instantaneous test results in Table 4 show that capital density, enterprise size, and marketing expenses have a positive effect on home-country-bound competitive advantage, while R&D investment has a significant positive impact on non-location-bound competitive advantage. Tables 4 and 6 confirm that in the wake of implementing cross-border M&As, enterprises strengthen their investment in fixed assets, expand their size, and increase their proportion of marketing expenditures, but overreach on R&D investment. Rugman [16] pointed out that, for emerging economies such as China, where the market is growing rapidly, the period of transformation into a leading competitive advantage through technology integration is long, and the cycle of establishing economies of scale is quite short. Due to their low technical applicability [28], these firms are more likely to take advantage of the economies of scale of their home countries by acquiring overseas strategic resources and directly feeding them back into the huge local market demand [13].
Hypothesis 2 was tested by splitting the sample into two groups based on internationalisation experience and by checking whether there was a difference between firms with different degrees of international experience. The estimation results are presented in Table 7. The results show that, for the home-country-bound competitive advantage, the coefficient of interaction
experience = 1 | experience = 0 | |||
---|---|---|---|---|
(1) Dependent variable: | (2) Dependent variable: | (3) Dependent variable: | (4) Dependent variable: | |
0.037 (0.038) | 0.015** (0.008) | −0.013 (0.009) | −0.019 (0.028) | |
0.102** (0.045) | −0.008 (0.008) | 0.009 (0.006) | 0.029 (0.026) | |
0.107** (0.053) | 0.001** (0.000) | 0.006** (0.003) | 0.003 (0.035) | |
−0.191 (0.217) | 0.829*** (0.044) | −0.111** (0.053) | 0.526*** (0.159) | |
yes | yes | yes | yes | |
yes | yes | yes | yes | |
yes | yes | yes | yes | |
382 | 382 | 961 | 961 | |
0.351 | 0.398 | 0.418 | 0.360 |
Moderating effect of international experience.
Note: *, **, *** indicate significance level at 10%, 5% and 1%, respectively.
In order to test whether state ownership matters, we split the sample into two groups. Table 8 illustrates the corresponding estimation results. In relation to non-SOEs, cross-border M&As have positive effects on home-country-bound competitive advantage; however, the impact on SOEs is not significant—i.e., non-SOEs are more dependent on home country resources and markets. In addition, when both state-owned and non-SOEs are considered, cross-border M&As do not significantly promote non-location-bound competitive advantage. A possible explanation for this result is that, on the one hand, both state-owned and non-SOEs lack international management experience. Chinese enterprises often fail to integrate the strategic assets they acquire through M&As [33]. On the other hand, SOEs and their host governments may have conflicting interests. In addition to being an independent market entity, a state-owned enterprise may, in relation to certain aspects, implement the strategies of their home country governments, resulting in insensitivity to market competition [47]. The transfer of strategic assets to SOEs faces more restrictions and scrutiny [20] than those linked to the profit-seeking nature of non-SOEs, driving them to use the home market and institutions to carry out ‘short-quick’ cross-border M&As and obtain short term profit returns [13]. Thus, hypothesis 3 is partially supported.
Ownership = 1 | Ownership = 0 | |||
---|---|---|---|---|
(1) Dependent variable: | (2) Dependent variable: | (3) Dependent variable: | (4) Dependent variable: | |
0.015 (0.059) | −0.032 (0.050) | −0.052*** (0.013) | −0.004 (0.021) | |
0.002 (0.027) | −0.079** (0.040) | −0.037*** (0.012) | −0.024 (0.020) | |
0.005 (0.056) | −0.009 (0.061) | 0.034** (0.015) | −0.002 (0.027) | |
0.436 (0.364) | 1.751*** (0.403) | 0.258 (0.183) | 0.855*** (0.257) | |
yes | yes | yes | yes | |
yes | yes | yes | yes | |
yes | yes | yes | yes | |
354 | 354 | 989 | 989 | |
0.506 | 0.528 | 0.433 | 0.568 |
Moderating effect of the nature of ownership.
Note: *, **, *** indicate significance level at 10%, 5% and 1%, respectively.
Following Fan and Tian [61], we conducted a placebo test on the relationship between cross-border M&As and competitive advantage by constructing false cross-border M&A implementation times. Specifically, we advanced the cross-border M&A times by one, two, and three years and examined the impact of the interaction terms
Dependent variable: | Dependent variable: | |||||
---|---|---|---|---|---|---|
−0.058*** (0.012) | −0.044*** (0.012) | −0.046*** (0.013) | −0.040* (0.023) | −0.032 (0.024) | −0.038 (0.032) | |
−0.093 (0.085) | −0.038 (0.047) | −0.024 (0.025) | −0.042*** (0.007) | −0.056*** (0.010) | −0.047*** (0.016) | |
0.004 (0.003) | 0.003 (0.003) | 0.002 (0.002) | 0.003 (0.002) | 0.003 (0.002) | 0.003 (0.003) | |
0.171*** (0.031) | 0.167*** (0.027) | 0.125*** (0.022) | 0.002* (0.001) | 0.003** (0.001) | 0.002*** (0.001) | |
0.207*** (0.025) | 0.177*** (0.022) | 0.180*** (0.023) | 0.002 (0.002) | 0.005 (0.005) | 0.004* (0.002) | |
0.043** (0.020) | 0.025* (0.015) | 0.029* (0.017) | 0.076*** (0.008) | 0.093*** (0.011) | 0.089*** (0.014) | |
0.025*** (0.008) | 0.015*** (0.005) | 0.011*** (0.004) | −0.001 (0.023) | 0.007 (0.006) | 0.011* (0.007) | |
0.031 (0.099) | 0.026 (0.097) | 0.285 (0.331) | 0.462*** (0.086) | 0.924*** (0.192) | 1.618*** (0.320) | |
yes | yes | yes | yes | yes | yes | |
yes | yes | yes | yes | yes | yes | |
1225 | 1186 | 1030 | 1225 | 1186 | 1030 | |
0.385 | 0.335 | 0.125 | 0.468 | 0.438 | 0.282 |
Results of the robustness test.
Note: *, **, *** indicate significance level at 10%, 5% and 1%, respectively.
This paper has studied the effect of cross-border M&As on enterprise competitive advantage by distinguishing it between home-country-bound competitive advantage and non-location bound competitive advantage in the context of Chinese ICT firms. We have examined two highly relevant research questions:
Based on the framework of the influence mechanism of cross-border M&As on the competitive advantages of enterprises, we used the propensity score matching and the differences-in-differences methods to empirically analyse the relationship between cross-border M&As and corporate competitive advantages, the path of improvement, the moderating effect of international experience, and the nature of ownership. We found strong evidence that cross-border M&As significantly improved home-country-bound competitive advantage rather than non-location bound competitive advantage. The results of the mechanism tests suggest that this is due to a crowding-out effect of cross-border M&As on R&D investment, which inhibits the development of non-location bound advantages. It also results from state-owned enterprises, which are considered to have institutional advantages not always effective in using cross-border M&As to enhance their competitive advantages. These findings enrich the understanding of FSAs in internalisation theory by building on insights from the home country dependency and considering the unique heterogeneity of EMNEs such as internationalisation experience and state ownership.
Although recent studies have paid attention to the adaptation of internalisation theory to EMNEs [3, 8], whether EMNEs can acquire FSA through CBAs remains controversial. Some researchers believe that EMNEs that conducted cross-border M&As can redeploy and integrate foreign strategic assets to establish a competitive advantage [2, 4]. However, others point out that the competitive advantage obtained can be used solely in the home country and cannot be transferred into the global market [13, 14]. Benefiting from the rapid economic growth of their home countries, EMNEs can expand into local markets and gain considerable economic returns through cross-border M&As [14]. We have provided a clearer perspective on this controversial issue by distinguishing and defining two types of competitive advantage of EMNEs according to their boundedness to their home country, and their home-country-bound competitive advantages versus their non-location-bound competitive advantages. In addition, we have found empirical evidence consistent with the views of Ramamurti and Hillemann [14], that cross-border M&As significantly improve home-country-bound competitive advantage rather than the non-location bound competitive advantage of EMNEs in the Chinese ICT industry. We have explained the reason through mechanism analysis, and therefore we have supplemented this research area. The results coming from the mechanism test suggests the presence of a crowding-out effect of cross-border M&As on R&D investment of ICT firms themselves which inhibits the expansion of independent R&D and innovation motivation.
Another key concept in internalisation theory that follows Rugman is country-specific advantages (CSAs). CSAs cover a wide range of external factors at country-level that affect firm performance, such as labour, technology levels, natural resources, or the institutional environment [23]. FSAs and CSAs are interlinked as MNEs tap into CSAs to utilise or develop their FSAs [7]. Although existing research suggests that CSA is available to all firms in the same country, while all firms in home markets potentially have access to CSAs, Bhaumik et al. [21] found that some firms leverage CSA better than others. We defined the home-country-bound competitive advantage in this paper, which is of great value for exploring the heterogeneity of EMNEs’ ability to utilise CSA to develop FSA. That is, EMNEs that can better exploit CSAs in their home country have stronger home-country-bound competitive advantage. From the perspective of enterprise heterogeneity, we found that internationally experienced companies can more effectively use CBAs to enhance their home-country-bound competitive advantage. When compared with state-owned enterprises, non-state-owned companies can enhance more their home-country-bound competitive advantage through CBAs. This research has therefore enriched the understanding of FSAs in internalisation theory by building on insights from home country dependency and taking into account the particular heterogeneity of EMNEs such as internationalisation experience, and state ownership.
The new generation of ICT technology has become one of the fiercest areas of technological competition among countries around the world. In the current global investment environment, it is necessary to reconsider whether cross-border M&As are the best way for Chinese ICT enterprises to enhance their core competitiveness. Our findings have important practical implications for cross-border M&As of Chinese ICT firms. At the firm level, enterprises should rationally implement cross-border M&As. Cross-border M&As have not improved the non-location-bound competitive advantage of Chinese ICT firms currently. After cross-border M&As, the development of enterprises depends more on the rapid growth of the home market scale than on improving internal technical efficiency. Enterprises may invest more resources in developing the domestic market to obtain short-term benefits rather than long-term strategic goals such as technology research and development [28]. Improvements in the home-country-bound and non-location-bound competitive advantages cannot be achieved automatically, and the heterogeneity between enterprises will lead to differences in M&As. Therefore, enterprises should not blindly follow the trend but combine their own conditions and actively ‘go out’ while accumulating international experience, laying the foundation for a leap forward to advanced internationalisation. At the government level, even though state ownership can secure financial resources for enterprises, government intervention may have a negative impact on corporate FDI. Home governments can encourage firms to ‘go out’ by providing market and online information, rather than excessive institutional and financial support. Governments should also strengthen the supervision model of FDI, and encourage enterprises with the ability and international experience to conduct foreign investment. Governments should caution those enterprises that do not satisfy the conditions needed to invest overseas and to conduct the ‘arbitrage-type’ M&As that rely on the resources, markets, and institutional advantages of their home country to tread carefully.
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.
",metaTitle:"Retraction and Correction Policy",metaDescription:"Retraction and Correction Policy",metaKeywords:null,canonicalURL:"/page/retraction-and-correction-policy",contentRaw:'[{"type":"htmlEditorComponent","content":"IntechOpen’s Retraction and Correction Policy has been developed in accordance with the Committee on Publication Ethics (COPE) publication guidelines relating to scientific misconduct and research ethics:
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\\n\\n3.1. ERRATUM
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\\n\\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n3.2. CORRIGENDUM
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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.
\\n\\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
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\\n\\nPolicy last updated: 2017-09-11
\\n"}]'},components:[{type:"htmlEditorComponent",content:'IntechOpen’s Retraction and Correction Policy has been developed in accordance with the Committee on Publication Ethics (COPE) publication guidelines relating to scientific misconduct and research ethics:
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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.
\n\n3. CORRECTIONS
\n\nA Correction will be issued by the Academic Editor when:
\n\n3.1. ERRATUM
\n\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\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.
\n\nAny suggestions or comments on this Policy are welcome and may be sent to permissions@intechopen.com.
\n\nPolicy last updated: 2017-09-11
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Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"24",type:"subseries",title:"Computer Vision",keywords:"Image Analysis, Scene Understanding, Biometrics, Deep Learning, Software Implementation, Hardware Implementation, Natural Images, Medical Images, Robotics, VR/AR",scope:"The scope of this topic is to disseminate the recent advances in the rapidly growing field of computer vision from both the theoretical and practical points of view. Novel computational algorithms for image analysis, scene understanding, biometrics, deep learning and their software or hardware implementations for natural and medical images, robotics, VR/AR, applications are some research directions relevant to this topic.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",hasOnlineFirst:!0,hasPublishedBooks:!1,annualVolume:11420,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"1177",title:"Prof.",name:"Antonio",middleName:"J. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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