strategic adjustment capacity, sustained competitive

Research ArticleStrategic Adjustment Capacity Sustained CompetitiveAdvantage and Firm Performance An Evolutionary Perspectiveon Bird Flocking and Firm Competition

Shou Chen ShiyuanWu ChaoMao and Boya Li

School of Business Hunan University No 11 Lushan Road (South) Changsha City Hunan Province 410082 China

Correspondence should be addressed to Shiyuan Wu wsyhnueducn

Received 9 February 2017 Accepted 9 May 2017 Published 19 June 2017

Academic Editor Yakov Strelniker

Copyright copy 2017 Shou Chen et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Imitating the positioning rules in the bird flocking system the strategic adjustment capacity is decomposed into three aspects whichare the organizational learning capacity from the top firms the extent to which firms maintain or rely on the best operationalcapacity vector in history and the ability to overcome the disadvantage while maintaining the advantage of the operationalcapacity vector from the previous years respectively Financial vectors are constructed to represent the results of corporatestrategic adjustment and listed firms in the China A stock are chosen as the samples As empirical analysis reveals there is apositive correlation between the organizational learning capacity from the top firms and the firm performance and a U-shapedrelation between the learning capability from the previous best operational capacity vector and the firm performance Howeverno significant correlation between the inertia control ability of the current operational capacity vector of the firms and theirperformance improvement can be observedThis study verifies that the issue of corporate competitiveness and performance can beinvestigated by utilizing the principles of competition in nature Moreover a firm can obtain a sustainable competitive advantageby improving its ability to learn from top firms in the industry

1 Introduction

The focus of strategic management research is how enter-prises utilize appropriate strategies to create and maintaincompetitive advantages The research on competition hasgrown exponentially in recent years However Mintzberget al noted that research on strategy has been criticizedfor its overly analytical orientation upper management biaslack of attention to action and learning and neglect ofthe elements that lead to the creation of strategies [1]Shrivastava note that research on organizational learningfocuses on processes this has the potential to offer insightsinto these identified drawbacks [2] Brockman and Morganbelieve that organizational learning is the basis for gaining asustainable competitive advantage and a key variable in theenhancement of firm performance [3] Tippins and Sohi statethat firms that are able to learn have a better chance of sensingevents and trends in the marketplace [4] Furthermore somestudies provide evidence of a positive relationship between

organizational learning and firm performance For instanceBaker and Sinkula find that learning orientation has a directeffect on firm performance [5] Ussahawanitchakit uses acultural measure of learning and obtains similar results [6]

Firms with sustainable competitive advantages typicallyhave two basic characteristics the first is that they createmore economic value than themarginal firms in the industrythe second is that other companies are unable to replicateits strategy Although the strategy cannot be replicated thesefirms are trying to imitate and learn to reduce the difference[7] It is obvious that learning capacity has an impact onperformance and our paper focuses on how strategic adjust-ment affects the learning process sustainable competitiveadvantage and performance To be specific we find that a firmin the corporate competition system is very similar to a bird inthe flocking system The processes of firms pursuing marketshare and birds pursuing prey are dynamic and the processof a firm allocating its strategic resources is very similar tothe process of a bird positioning itself when pursuing prey In

HindawiMathematical Problems in EngineeringVolume 2017 Article ID 3807912 14 pageshttpsdoiorg10115520173807912

2 Mathematical Problems in Engineering

fact the principle of bird flocking has already been appliedto an algorithm of artificial intelligence by Dr Kennedy andDr Eberhart who proposed a population based stochasticoptimization technique named particle swarm optimization(PSO) [8] They note that during bird flocking the followinglocation is determined by three related factors the currentbird location closest to the food the closest location thatthey find in their own search for food and their currentlocation and speed [9] It solves a problem by having apopulation of candidate solutions here dubbed particles andmoving these particles around in the search-space accordingto simple mathematical formulae over the particlersquos positionand velocity Each particlersquos movement is influenced by itslocal best known position but is also guided toward the bestknown positions in the search-space which are updated asbetter positions are found by other particles This is expectedto move the swarm toward the best solutions [10] In thispaper we argue that a firmrsquos performance in the followingstrategic adjustment is also determined by three factors thefirmrsquos performance of strategic elements it identifies in firmswith optimal strategic performance the firmrsquos performance ofstrategic elements corresponding to the optimal performancein its own history and the current firmrsquos performance ofstrategic elements The adjustment capacity of firm strategiescan be represented by the controlling capacity in these threefactors From the perspective of the dynamic adjustmentcapacity of firm strategies we choose some indicators in thefinancial statement that reflect the firmrsquos abilities in assetoperation earning profits and cash flow management tomeasure the results of strategy adjustment [11] We use thereturn on assets (ROA) to measure the firmrsquos performanceAfter testing these indices that have a significant impacton firm performance and comparing them with the systemof bird flocking the strategic adjustment capacity displayedcan be obtained from these three aspects The relationshipbetween the adjustment capacity of a firmrsquos strategies andits performance can then be tested Finally the adjustmentcapacity of firm strategies can be evaluated

Just like the fittest organisms surviving in the ecologicalenvironment the business entities also strive for survival anddevelopment through constantly improving their sustainablecompetitiveness in the business ecological environmentEnterprises often need to face the problem of ldquohow betterdecision-making helps to improve sustainabilityrdquo seek theldquosustainable business and developmentrdquo mode and explorethe ldquosustainable leadership and managementrdquo All of theabove concern the topmost strategic planning issues of enter-prises so the ability to make strategic adjustments accordingto the internal and external environment is the key for enter-prises to stay ahead and obtain industry performance Thispaper takes the enterprise strategy adjustment ability as theentry point and answers the above questions through the birdflocking and firm competition specifically including howto divide the dimension of enterprise strategic adjustmentabilities how to quantify the enterprise strategic adjustmentabilities and what the impacts of strategy adjustment abilitiesare at different dimensions on corporate performance [12]On this basis the article aims to build the core competi-tiveness of enterprises and provide a decision-making basis

for sustainable advantages However in times of disruptivechangewithin different industries this competitiveness couldbe drastically reduced so the disruptive companies are not inthe scope of this research

This paper contributes to the research surrounding theorganizational learning uncovered in prior research Mostprior research is focused on exploring the multiple dimen-sions of organizational learning ability from the inside ofan enterprise to build an organizational learning abilityevaluation system and evaluation model and then discussesthe relationship between organizational learning ability andperformance such as the 6P-1B model of learning orga-nization [13] Multi-group Structural Equation ModelingApproach [14] and Capability Model [15] These studies takethe perspective of individual enterprises to determine theirability to learnThey emphasize the evaluation of the learningprocess and most of the qualitative analysis is based onquestionnaires Furthermore most of these studies are staticstudies which produce results only at a certain time point inthe evaluation of enterprise organizational learning abilitiesOur paper studies the dynamic adjustment of strategy andthe ability of organizational learning in a certain periodfrom the enterprisersquos external performance Since the datawe use are financial data from the financial statements thispaper proposes a quantitative analysis method to evaluate thestrategy adjustment capacity

Two points should be made in relating this paperrsquosfindings to other research First our focus is different Onthe one hand we emphasize the effect of organizationallearning which reflects the ability of organizational learningon the other hand for the ability of the enterprise strategydecision we focus on the enterprisersquos learning strategies andlearning ability in situations of market competition based onwhich we can evaluate the enterprisersquos strategic adjustmentability and study the relationship between organizationallearning and performance Second the strategic adjustmentand organizational learning are posited to be similar to thebird flocking process Furthermore the purpose of this studyis not only to evaluate the enterprise learning ability but alsoto guide firms on how to learn and to promote the strategicadjustment capacity to obtain better performance

The remainder of the paper is organized as followsSection 2 is the literature review Section 3 introduces thevector of strategic adjustment the concept of the strategicadjustment capacity and the measurement of related vari-ables Section 4 explains the data and the way to computethe strategic adjustment capacity followed by the empiricalresults Section 5 discusses managerial implications andSection 6 summarizes and concludes the paper

2 Literature Review

The way that firm strategy influences the performancehas received great attention in recent years Firm strategyexpresses a basic idea of how to reach firmrsquos objectives Finan-cial strategy as a functional strategy plays an important rolein firm strategyThere are some companiesrsquo turnover decreas-ing because of having no strategic financial management [16]However the definition of financial strategy is somewhat

Mathematical Problems in Engineering 3

vague and quantitative analyses of the financial strategy arealmost focused on the direct influence of working capitalmanagement on corporate profitability [17ndash19] which provedthat working capital management has a significant impact onboth profitability and liquidity in different countries [20 21]

The management of working capital is defined as theldquomanagement of current assets and current liabilities andfinancing these current assetsrdquo [20] Following this definitionsome researchers studied the impact of proper or optimalinventory management others studied the management ofaccounts receivables Eljelly found that there was a negativerelationship between profitability and liquidity indicatorssuch as current ratio and cash gap in the Saudi sampleexamined [22] Using correlation and regression tests Delooffound a significant negative relationship between gross oper-ating income and the number of days accounts receivableinventories and accounts payable of Belgian firms [21]Nazir and Afza found a negative relationship between theprofitability of firms and the degree of aggressiveness ofworking capital investment and financing policies from asample of 204 nonfinancial firms listed on Karachi StockExchange (KSE) for the period 1998ndash2005 [23] All of thesestudies are trying to postulate an optimalway policy that leadsto profit maximization [24ndash27] but almost none of themtry to find the optimal construction of working capital byviewing the working capital management as a firmrsquos financialstrategy and viewing the optimization configuration processof working capital as an organizational learning process

Despite a large body of studies about organizational learn-ing only a few studies focus on the organizational learningprocess Goold and Pascale show that organizational learningprocess is probably not in order and the strategic adjustmentand organizational learning are described as an emergenttrial-and-error and even random process [28 29] Tippinsand Sohi show that the five stages they distinguish withinthe organizational learning process (information acquisitioninformation dissemination shared interpretation declarativememory and procedural memory) have a positive effect onfirm performance [4] Darroch and McNaughton provideevidence that the entire process of organizational learningproduces better performance [30] Organizational learningresearch has largely remained disconnected from strategy[31 32] There are two major drawbacks The first short-coming is a conceptualization of organizational learningthat is too narrow Additionally most previous researchconsidered individual enterprises and did not investigatethe effects that competition against other firms has on theindustryThe second shortcoming is that even in cases whereorganizational learning has been applied to strategic renewalresearchers have stopped testing the related data As Crossanand Berdrow asked how does organizational learning explainthe phenomenon of strategic renewal [33]

In contrast to neoclassical economic models that assumethat individuals hold preferences independently of othersthere are growing bodies of research demonstrating thatpreferences are often interdependent and based upon rela-tive comparisons Wilson and Kniffin developed and pre-sented a series of simulation models that highlight the factthat evolutionary change in human groups is not tied to

genetic change and that once one allows for ldquolearningrdquo inevolutionary models then (human) groups can change ata great speed [34] After this a wide array of evolution-ary perspectivesmdashdrawn mainly from studies of nonhumanspeciesmdashare organized while focusing attention on under-standing the nature of altruism by Wilson and Kniffin [35]Kniffin considers the sensitivity of individuals to their relativesalary standing (eg in relation to coworkers) using theconcept of relative fitness that is central to evolutionarystudies of all types of human and nonhuman species [36]Following this point of view to fill the gap identified byCrossan and Berdrow in organizational learning research[33] this paper aims to understand the specific processof strategic adjustment and uses a quantitative analysis toinvestigate the relationship between the capacity of strategicadjustment and firm performance from the perspective of thecomplex adaptive system (CAS) [37]

The complex adaptive system (CAS) proposed in 1994provided the fundamental theories andmethods for the studyof the adaptation process of a complicated system [37] Themain feature is that members in the system (called subjects)can be adaptive meaning that they can communicate withtheir environment and other subjects and learn or accumulateexperiences to change their own structure or behavior on thebasis of the communicative process The transformation orevolution of the whole system includes the generation of thenew hierarchy the emergence of divergence and diversityand the occurrence of new themes Likewise the systemof corporate competition has these features [38] First assubjects firms are active and dynamic Second firm subjectsand the environment or other firm subjects influence andinteract with one another which can be considered a majordrive for development and change in society and in aneconomy Finally the whole systemmay be affected by certainrandom factors To obtain better performance firms willadjust strategically according to the industrial environmentand their own development status From the perspectiveof the resource-based view (RBV) the strategic adjustmentstarts with changes in resource allocation [39] Firms willoptimize the allocation proportion of different resources oradjust the investment quantities of overall resources andthus the resource allocation of firms will be integrated togenerate better strategic performance [40] From the perspec-tive of strategic adjustment and implementation outcomesthe effects of this resource allocation will be ultimatelytransferred to the operational capacity namely the changes inthe operational capacity vectors [41] As a result the strategicadjustment capacity can be defined as the capacity to achievethe optimal operational capacity vector and the systemof corporate competition can be viewed as a sophisticatedsystem of adaptation

Inspired from natural biological system we can doresearch on corporate competition by bionic studies of thebird flocking [42] Assume that there is only one type of foodavailable in a certain region and a flock of birds are searchingfor food randomly At the beginning all the birds do notknow the location of the food but the distance of the foodThe simple and effective strategy for the birds to find thefood is to search for the birds that are in neighboring areas


which are close to the food at thatmoment and then approachthem By repeating the same processes the birds are able tosearch for the food more efficiently Like the bird flockingfirms also do not know what type of corporate strategy isthe best when seeking better performance [43] Also inmost industries firms tend to learn from their competitorenterprises that have optimal performance (ie the collectionof operational capacities that can reflect the formation ofthe strategic implementation within a period of time) [44]Degeus notes that organizational learning may be the onlysustainable competitive advantage [45] Subsequently likebird flocking firms adjust their own strategic orientation andthe allocation of strategic resources to obtain the operationalcapacity vector of maximum strategic performance

3 Methodology

31 Vector of Operational Capacity In market competitionfirms improve their own competitiveness continuously andseek the best profits by adjusting strategies These strate-gies may include reducing costs differentiating focusingor diversifying products or services While the strategiesadopted are varied and sophisticated the ultimate aim ofthese strategies is to increase the profit and operationalcapacity of the firm and to seize development opportunitiesbrought by external environment changes Therefore wecontend that the direct objective of firm strategic adjustmentis to increase its operational capacity From the perspectiveof financial analysis and working capital management someindices can represent the profit and operational and cashcontrolling capacities of firms [46] We first conduct astatistical analysis of operational capacity and firm strategicperformance to construct the vector of operational capacity

In terms of the profit-making capacity of firms the netprofit margin (NPM) represents the net profit brought bysales Through NPM firms can expand sales and striveto improve operational management at the same time toincrease profits and obtain better strategic performance[47] The operating cost ratio (OCR) represents the cost-controlling capacity of firms The lower the OCR is thestronger the cost-controlling capacity is In otherwords firmsare more likely to obtain better performance with a lowOCR [48] The proportion of selling expenses general andadministrative expenses and financial expenses (SGF) repre-sents the control ability of firms in sales administration andfinance A low SGF shows that the selling and administrativeefficiency and the financial strength of a firm are strongerThese firms are more likely to increase performance [49]Therefore we present our first hypothesis

(H1) The net profit margin of firms is positivelycorrelated with its performance measured by ROA(H2) The operating cost ratio is negatively correlatedwith its performance measured by ROA(H3) The proportion of selling expenses general andadministrative expenses and financial expenses isnegatively correlated with its performance measuredby ROA

In terms of cash holdings firms usually hold a certainamount of cash to maintain daily operations and seize someinvestment opportunities However holding too much cashsuggests that this part of the resources is not involvedin the profit-making of firms to generate correspondingperformance [50] As a result the fourth hypothesis is madefor CHR as follows

(H4) The cash holding ratio (CHR) is negativelycorrelated with performance measured by ROA

In the operational process the turnover capability canaffect the efficiency of value creationWe considered themostimportant indicators of short-term asset turnover capabilitysuch as inventory turnover liquid asset turnover and fixedasset turnover [51] Therefore we present our next threehypotheses

(H5) A firmrsquos inventory turnover ratio and accountsreceivable turnover ratio are positively correlatedwithits performance as measured by ROA(H6) A firmrsquos liquid asset turnover ratio is positivelycorrelated with its performance measured by ROA(H7) A firmrsquos fixed asset turnover ratio is positivelycorrelated with its performance measured by ROA

The panel data methodology is deployed to capturethe effects of these indices of operational capacity on per-formance measured by ROA In line with the previoushypotheses we take the firm size (Size119894119905) and the lagged ROA(ROA119894119905minus1) as control variables and the seven different paneldata models are estimated

ROA119894119905 = 1205721NPM119894119905 + 1205731Size119894119905 + 1205741ROA119894119905minus1 + 120583119894 + 120578119905+ 120576119894119905

(1)

ROA119894119905 = 1205722OCR119894119905 + 1205732Size119894119905 + 1205742ROA119894119905minus1 + 120583119894 + 120578119905+ 120576119894119905

(2)

ROA119894119905 = 1205723SGFR119894119905 + 1205733Size119894119905 + 1205743ROA119894119905minus1 + 120583119894 + 120578119905+ 120576119894119905

(3)

ROA119894119905 = 1205724CHR119894119905 + 1205734Size119894119905 + 1205744ROA119894119905minus1 + 120583119894 + 120578119905+ 120576119894119905

(4)

ROA119894119905 = 1205725IT119894119905 + 1205735Size119894119905 + 1205745ROA119894119905minus1 + 120583119894 + 120578119905+ 120576119894119905

(5)

ROA119894119905 = 1205726LT119894119905 + 1205736Size119894119905 + 1205746ROA119894119905minus1 + 120583119894 + 120578119905+ 120576119894119905

(6)

ROA119894119905 = 1205727FT119894119905 + 1205737Size119894119905 + 1205747ROA119894119905minus1 + 120583119894 + 120578119905+ 120576119894119905

(7)

where 120583119894 controlled the firmrsquos individual effects 120578119905 controlledthe time effects and 120576119894119905 were the error terms


After the effects of these variables are confirmed wecan use them to construct the operational capacity vector asfollows

119864119862119894119905 =

[[[[[[[[[[[[[[[

NPM119894119905OCR119894119905SGFR119894119905CHR119894119905IT119894119905LT119894119905FT119894119905

]]]]]]]]]]]]]]]

(8)

Based on this definition we can measure the opera-tional capacity and study the correlation between operationalcapacity and firm performance The details are presented inthe next section

32 Strategic Adjustment Capacity Based on the principle ofthe bird flocking system the factors affecting the followinglocation of a bird include the location of the bird closest to thefood in the current flock of birds the location closest to thefood during the search for the food and the current locationof the bird as illustrated in

119883119894119905+1 = 997888120596 1198941119884119905119868_best + 997888120596 1198942119885119894119879_best + 997888120596 1198943119883119894119905 (9)

where 119883119894119905+1 is the location vector of the bird at the momentof 119905 + 1 119884119905119868_best is the location vector of the bird closest tothe food among the individual birds at moment 119905 119885119894119879_bestis the location vector closest to the food by the bird whichsearches by itself from the start to moment 119905 and 119883119894119905 isthe location vector of the bird in its current place 997888120596 1198941 997888120596 1198942and 997888120596 1198943 represent the capacity of the bird which evaluatesanother bird in the flock that is closest to the foodmemorizesthe location closest to the food itself and controls the currentlocation respectively

Similar to the bird flocking system firms are able to adjusttheir operational capacity on the basis of the operationalcapacity of their rivals the status of their operational capacityin obtaining optimal performance in their own developmentprocess and their current capacity to increase strategicperformance The process can be shown in

119864119862119894119895119905+1 = 1205961198941198951119864119862119905119895119868119894_best + 1205961198941198952119864119862119894119895119879119905_best + 1205961198941198953119864119862119894119895119905 (10)

where 119864119862119894119895119905+1 is the value of firm 119894rsquos financial index 119895 at theyear 119905 + 1 From our viewpoint this variable was determinedby three relative aspects 119864119862119905119895119868119894_best is the value of index 119895 inthe operational capacity vector of firm 119894 in industry 119868119894 thatwas obtained from optimal strategic performance at moment119905 119864119862119894119895119879119905_best is the value of index 119895 in the operational capacityvector of firm 119894 that is obtained from optimal performanceuntil moment 119905 during the investigation period 119879119905 and 119864119862119894119895119905is the value of index 119895 in the current operational capacityvector of firm 119894 1205961198941198951 is the capacity to learn from firms withan optimal operational capacity vector when firm 119894 adjuststhe value of index 119895 of the operational capacity vector 1205961198941198952

is the weighting of the value of index 119895 of the operationalcapacity vector in its own record of the optimal performancewhich is obtained when firm 119894 adjusts the value of index119895 in the operational capacity vector and 1205961198941198953 refers to theinertial control ability of the value of index 119895 of the currentoperational capacity vector when firm 119894 adjusts the value ofindex 119895 in the operational capacity vector

The studies of the relationship between the strategicadjustment capacity and performance can be developed inthese three perspectives mentioned above The first aspect ofthe strategic adjustment capacity is organizational learningfrom firms with the best performance labeled as 1198621 Gra-hovac and Miller note that the interaction between resourcevalue and the cost of imitation is complex and affected by thenumber of firms in the industry [52] We argue that learningfrom the firms with the best performance enables firms tonarrow the gap with other firms with the best operationalcapacity after which the gap of strategic performance is alsonarrowed Hence it can be inferred that the improved abilityto learn from firms with the best performance will facilitatea range of performance improvement as illustrated in thefollowing hypothesis

(H8) There is a positive correlation between organi-zational learning capacity in firms with the optimaloperational capacity vector and the future perfor-mance improvement

A second aspect of the strategic adjustment capacity is theextent to which firmsmaintain or rely on the best operationalcapacity vector in history labeled as 1198622 When the weight ofthe best operational capacity vector is determined if firmsthink their best performance is not satisfactory they willreduce theweight and support innovative strategiesTheywillexplore the optimal operational capacity vector that fits themto improve their performance Otherwise they will allocatemore weight to allow the firm to accumulate competitiveadvantages for better performance As a result we arguethat lower dependence on the optimal operational capacityvector will benefit innovation for better performance As thedependence increases the effect of improving performancewill decrease until the dependence achieves a certain levelThis will benefit the firms and allow them to build thecompetitive edge and improve the strategic performancenamely

(H9) There is a negative correlation between thedegree to which the firms rely on the best operationalcapacity and the future performance improvementhowever as the dependence exceeds a certain levelits correlation with future performance improvementwill be positive

A final aspect of the strategic adjustment capacity isthe inertia control ability labeled as 1198623 The inertia controlability represents the ability to change the disadvantage ofthe operational capacity vector from the previous year ormaintain the advantage of the operational capacity vector inthe previous year If firms have better inertia control abilitythey tend to increase their performance namely


Table 1 Variable names and definitions

Variable Definition

Return on asset (ROA) The ratio of net income to total assets Knauer and Wohrmann (2013)[46]

ΔROA ΔROA119894 = ROA1198942015 minus ROA1198942014Knauer and Wohrmann (2013)

[46]Net profit margin (NPM) Net marginoperating income Banos-Caballero et al (2014) [47]Operating cost ratio (OCR) Operating costoperating income Salman et al (2014) [48]Selling administrative andfinancial expense ratio(SGFR)

(Operating expenses + administrativeexpenses + financial expenses)operating

incomeAfrifa (2016) [49]

Cash holding rate (CHR) Monetary capitalassets total Aktas et al (2015) [50]

Inventory turnover (IT) Operating cost((initial inventory net +final inventory Ne)2) Tran et al (2017) [51]

Liquid asset turnover (LT) Operating cost((initial mobile assets +final mobile assets)2) Tranet al (2017) [51]

Fix asset turnover (FT) Operating cost((initial fixed assets +final fixed assets)2) Tran et al (2017) [51]

Firm size (size) Natural logarithm of total assets Tran et al (2017) [51]

1198621 The organizational learning capacity fromthe top firms mdash

1198622 The learning capability from the previousbest operational capacity vector mdash

1198623 The inertia control ability of the currentoperational capacity vector mdash

Note ROA and ΔROA are dependent variables in the two studies respectively the other variables are treated as independent variables in which one is used asa control variable namely firm size

(H10) There is a positive correlation between inertiacontrol ability and future performance improvement

Regressionmethodology is deployed to capture the effectsof strategic adjustment capacity on performance as measuredby ΔROA Corresponding to the previous hypotheses thethree different regression models are estimated

ΔROA = 1205728 + 12057381198621 + 119906119894 (11)

ΔROA = 1205729 + 12057391198622 + 120574911986222 + 119906119894 (12)

ΔROA = 12057210 + 120573101198623 + 119906119894 (13)

where 120583119894 controlled the firmrsquos individual effects

33 Measurement of Variables To remain consistent withprevious studies of strategic management measures pertain-ing to indices of operational capacity and strategic perfor-mance were the same as those in Deloof Raheman and Nasrand other similar studies [21 24] Table 1 summarizes thedependent explanatory and control variables

Tomeasure the strategic adjustment capacity (11986211198622 and1198623) we hypothesize that the strategic adjustment capacityof firms is consistent within a period of time and thusthe equations can be constructed Through updating thestrategic adjustment capacity of firms is calculated for eachoperational capacity vector within each period of timeThenthrough mean value the strategic adjustment capacity offirms is achieved for each operational capacity vector during

the investigation Thus for any firm 119894 (119894 isin 119868) and any index 119895(119895 isin 119869) in the operational capacity vector the equations canbe constructed as follows

1198641198621198941198952 = 12059611989411989511198641198621119895119868119894_best + 12059611989411989521198641198621198941198951198791_best + 12059611989411989531198641198621198941198951 (14)

1198641198621198941198953 = 12059611989411989511198641198622119895119868119894_best + 12059611989411989521198641198621198941198951198792_best + 12059611989411989531198641198621198941198952 (15)

1198641198621198941198954 = 12059611989411989511198641198623119895119868119894_best + 12059611989411989521198641198621198941198951198793_best + 12059611989411989531198641198621198941198953 (16)

1198641198621198941198955 = 12059611989411989511198641198624119895119868119894_best + 12059611989411989521198641198621198941198951198794_best + 12059611989411989531198641198621198941198954

(17)

119864119862119894119895119905+1 = 1205961198941198951119864119862119905119895119868119894_best + 1205961198941198952119864119862119894119895119879119905_best + 1205961198941198953119864119862119894119895119905

(18)

119864119862119894119895119879 = 1205961198941198951119864119862119879minus1119895119868119894_best + 1205961198941198952119864119862119894119895119879119879minus1_best+ 1205961198941198953119864119862119894119895119879minus1

(19)

From (14)ndash(16) 1205961198941198951 1205961198941198952 and 1205961198941198953 can be achievedmarked by 1205961198941198951119905=2 1205961198941198952119905=2 and 1205961198941198953119905=2 respectively From(15)ndash(17) 1205961198941198951 1205961198941198952 and 1205961198941198953 can be achieved marked by1205961198941198951119905=3 1205961198941198952119905=3 and 1205961198941198953119905=3 respectively In turn 1205961198941198951119905=119879minus11205961198941198952119905=119879minus1 and 1205961198941198953119905=119879minus1 can be achieved The size of thesecoefficientsrsquo absolute values represents the importance thatfirms attach to optimal operational capacity in the industry


Table 2 Descriptive statistics for the sample

Variable Obs Mean Std dev Min Max 25 75ROA 28993 0000 0984 minus8961 9341 minus0499 0411NPM 28993 0000 0984 minus14092 13802 minus0265 0295OCR 28993 0000 0984 minus5998 8727 minus0570 0663SGFR 28993 0000 0984 minus2357 14762 minus0495 0202CHR 28993 0000 0984 minus2766 12416 minus0700 0454IT 28993 0000 0984 minus4129 15589 minus0518 0160LT 28993 0000 0984 minus2734 11155 minus0670 0447FT 28993 0000 0984 minus1818 15551 minus0477 0089Size 28993 0000 0984 minus3900 3828 minus0672 0629

on record and at present during the strategic adjustmentTherefore the adjustment capacity shows the extent of theadjustment and we define the adjustment capacity of eachindex in firm 119894 as1205961198941198951 = |(1(119879minus2))sum119879minus1119905=2 1205961198941198951119905|1205961198941198952 = |(1(119879minus2))sum119879minus1119905=2 1205961198941198952119905| and 1205961198941198953 = |(1(119879 minus 2))sum119879minus1119905=2 1205961198941198953119905| Aftercalculating the adjustment capacity of each index the matrixof the strategic adjustment capacity of firm 119894 is achieved

120596i =

[[[[[[[[[[[[[[[

12059611989411120596119894211205961198943112059611989441120596119894511205961198946112059611989471

12059611989412120596119894221205961198943212059611989442120596119894521205961198946212059611989472

12059611989413120596119894231205961198943312059611989443120596119894531205961198946312059611989473

]]]]]]]]]]]]]]]

(20)

There is a gap between the effects of different indiceson performance and therefore the impact of the strategicadjustment capacity of each index on performance improve-ment varies Hence we consider the effect of both thematrix of strategic adjustment capacity and each index onperformance and the initial value of the strategic adjustmentcapacity of firm 119894 is achieved

[11986210158401198941 11986210158401198942 11986210158401198943] = [100381610038161003816100381612057211003816100381610038161003816 100381610038161003816100381612057221003816100381610038161003816 100381610038161003816100381612057231003816100381610038161003816 100381610038161003816100381612057241003816100381610038161003816 100381610038161003816100381612057251003816100381610038161003816 100381610038161003816100381612057261003816100381610038161003816 100381610038161003816100381612057271003816100381610038161003816]times 120596i

(21)

To compare the strategic adjustment capacity amongfirms the initial value is standardized and the ultimate valueof the strategic adjustment capacity is achieved

1198621198941 = 1198621015840119894111986210158401198941 + 11986210158401198942 + 11986210158401198943

1198621198942 = 1198621015840119894211986210158401198941 + 11986210158401198942 + 11986210158401198943

1198621198943 = 1198621015840119894311986210158401198941 + 11986210158401198942 + 11986210158401198943

(22)

4 Results

41 Data and Descriptive Statistics This study includes Chi-nese listed firms in the manufacturing industry in the sam-ple These firms are assigned as manufacturing specializedequipment according to the Industry Classification Standardpublished by the China Securities Regulatory CommissionOur sample includes the A share-listed firms in the Shanghaiand Shenzhen stock markets from 2002 to 2015 All of thedata are drawn from the Tinysoft database (A financialdatabase of Listed Companies in China created by ShenzhenTianyi Science and Technology Development Co Ltd) Thesample firms must satisfy the following criteria (1) firmsmust participate in the manufacturing of specialized equip-ment which includes manufacturing specialized equipmentfor petrochemicals textiles metallurgy mining electronicsagriculture forestry farming fishing and hydraulic indus-tries as defined by the China Security Regulatory Commis-sion (2) firms must be listed for more than three years and(3) the number of firms in the industry for a given yearmust be greater or equal to three We finally obtain 28993observations and we standardize the data by industry asfollows

1199091015840119894119895119905 =119909119894119895119905 minus 120583ind119894119895119905

120590ind119894119895119905 (23)

where 119909119894119895119905 represents the variable 119895 of firm 119894 in year 119905 and120583ind119894119895119905 and120590ind119894119895119905 represent the industry averages and standarddeviations of variable 119895 to which firm 119894 belongs respectivelyTable 2 summarizes the statistics of the variables

From Table 2 it is clear that there is a considerable gapbetween the firm performance in the selected samples andeach index which shows that the operational capacity andperformance of sample firms differ significantly

42 Results of the Effect of the Operational Capacity Vector onPerformance We used linear panel data regression modelsto estimate the causal relationships between the performancemeasured by ROA and the dependent variables chosen asthe index of operational capacity and other control variablesAs the regression model includes the lagged variables thesystem GMM (generalized method of moments) is appliedto estimate the dynamic panel data model Table 3 shows theresults of the dynamic panel data regression models (1)ndash(7)


Table 3 Results of the dynamic panel data regression

Variable Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7)

NPM 0556lowastlowastlowast

12503

OCR minus0430lowastlowastlowastminus3517

SGFR minus0315lowastlowastlowast mdashminus3342CHR minus0053lowastlowastlowast

minus662IT 0148lowastlowastlowast

1326

LT 0472lowastlowastlowast

4005

FT 0257lowastlowastlowast

2195

Size minus0267lowastlowastlowast minus0188lowastlowastlowast minus0270lowastlowastlowast minus0243lowastlowastlowast minus0215lowastlowastlowast minus0051lowastlowastlowast minus0202lowastlowastlowastminus1952 minus1003 minus1432 minus1218 minus1096 minus267 minus1047

Lag ROA 0241lowastlowastlowast 0289lowastlowastlowast 0299lowastlowastlowast 0350lowastlowastlowast 0339lowastlowastlowast 0274lowastlowastlowast 0309 lowastlowastlowast

4333 3669 3784 4289 4174 3561 3801

Constant minus0054lowastlowastlowast minus0058lowastlowastlowast minus0061lowastlowastlowast minus0065lowastlowastlowast minus0059lowastlowastlowast minus0036lowastlowastlowast minus0053lowastlowastlowastminus1764 minus1368 minus1433 minus1463 minus1336 minus862 minus122

Wald test 2035260lowastlowastlowast 445081lowastlowastlowast 4293790lowastlowastlowast 295905lowastlowastlowast 312492lowastlowastlowast 477861lowastlowastlowast 354321lowastlowastlowast

Sargan test 745993lowastlowastlowast 807533lowastlowastlowast 807046lowastlowastlowast 866568lowastlowastlowast 867464lowastlowastlowast 809926lowastlowastlowast 809281lowastlowastlowast

Note lowast lowast lowast denotes 1 significance levels

It is obvious from the Wald test values that all of theregression tests are significant at the 1 level and Sargan testvalues also show that there is no overrecognition of the toolvariables in the process of model estimation The coefficientsof the variables that we viewed as the firmrsquos operationalcapacity vectors are all significantThe results show that NPMis positively related to ROA which strongly supports our firsthypothesis The coefficients of OCR and SGFR are signifi-cantly negative they provide strong evidence for Hypothesis2 Models (4)ndash(7) demonstrate that the positive correlationbetween CHR IT LT FT and ROA is validated the resultssupport hypotheses (H4)ndash(H7) The control variables of firmsize and lag ROA are also significant in each model

The testing result of Hypothesis (H1) shows that for anenterprise the higher the net profitmargin is themore profitsit can make after subtracting operational costs from businessrevenueThis may be attributed to the superiority of its prod-ucts the value of its brand the customersrsquo loyalty to it andother relevant factors that enable it to sell products at a pricehigher than themarket average or to its cost control ability sothat it could sell products at market price at a lower cost Theverification of (H2) further suggests that an enterprise cantake a cost leadership strategy to sharpen its competitive edgeto gain more returns As can be seen from the test of (H3) anenterprise can improve the return on assets (ROA) by opti-mizing its distribution channels and controlling itsmarketingexpenses reduce overheads improvemanagement efficiencyand increase ROA by establishing a modern business man-agement system or adopting a flat organizational structure

finally an enterprise with strong comprehensive strength hasstronger bargaining power in financing and is able to increaseROA by controlling the financing scale and cost rationallyEvery enterprise has been making equal efforts to maintaindaily operations seize investment opportunities and chasereturns on current investments but it cannot hold muchcash to wait for an investment opportunity Holding excesscash in hand indicates that capital is not invested in high-return projects and this may lower the total ROA This isconsistent with (H4) (H5)ndash(H7) are tenable suggesting thatan enterprisersquos capability for capital turnover has significantinfluence on ROA To improve ROA enterprises can increasethe inventory turnover liquid asset turnover and fixed assetturnover Overall the results of hypothesis testing show thatall the variables we used to construct the operational capacityvector have a significant correlation with ROA so it is animportant financial strategic factor to enterprises In allthe strategic adjustment capacity of the operational capacityvectors may naturally affect changes in firm performance

43 The Effect of Strategic Adjustment Capacity on Perfor-mance Improvement As (14)ndash(19) show the strategic adjust-ment capacity is calculated every three years We use MAT-LAB 2015b (Explored by MathWorks Natick MA UNITEDSTATES) to select the effective firms and years and calcu-late firmsrsquo strategic adjustment capacity and performanceincreases Table 4 reports the descriptive statistics of thestrategic adjustment capacity and performance improved


Table 4 Descriptive statistics for strategic adjustment capacity

Variable Obs Mean Std dev Min MaxΔROA 2412 0023 0890 minus8056 104351198621 2412 0155 0058 0025 04201198622 2412 0489 0084 0211 08261198623 2412 0357 0068 0117 0600

Table 5 Regression results of the effect of operational capacity on performance improvement

ΔROAModel (11) Model (12) Model (13)

1198621 10100lowastlowast

1395

1198622 minus78837lowastlowastlowastminus1987

11986222 78912lowastlowastlowast

1977

1198623 4437068

Constant minus1078lowastlowastlowast 19091lowastlowastlowast 0381minus902 1960 017

119865-test 19469lowastlowastlowast 19743lowastlowastlowast 039Note lowastlowast and lowast lowast lowast denote 5 and 1 significance levels respectively

Table 4 shows that the results of the performanceimprovement of the sample enterprises are both positiveand negative during the tests which suggests that theperformance of some firms increases while that of otherfirms decreases From the maximum and minimum value ofeach aspect of the strategic adjustment capacity there is asignificant gap amongfirms in each dimension of the strategicadjustment capacity The comparison of the average valueof each dimension in strategic adjustment capacity showsthat 1198621 is minimal while 1198622 is maximal which means thatthe firms have a stronger capacity to strategically adjust theoptimal operating capacity with a lower learning capacitythan the optimal operating capacity vector This is associatedwith the fact that the adjustment of the optimal operatingcapacity vector of a firm is easier than the adjustment inthe optimal operating capacity vector Regression analysisshows the impact of the gap of strategic adjustment capacityamong firms on performance Table 5 shows the results of theimpact of strategic capacity on the performance improvementcapacity by OLS (ordinary least squares)

First we focus on the effect of the strategic adjustmentcapacity on the performance improvement in the followingyear In other words the dependable variable is in our modelIn Model (11) the 119865-test value is 19469 which is significantat the 1 level showing that a significant relationship isfound between the capacity of learning in firms with optimaloperation vector and ΔROA The coefficient is 10100 whichimplies a 1 increase in the capacity for learning from firmswith an optimal operation vector which is associated with anincrease inΔROA by 1010This explains why firms observethe operation performance of the best firms in the industry in

corporate competitions and explore how the successful firmsdetermine the operation vector to increase performanceTheresults of the regression analyze Hypothesis 8 and show thatthere is a positive correlation between the capacity of thefirms that learn from those with the optimal operationalcapacity and future performance increases Figure 1 shows theregression of the Model (11)

Figure 1 depicts the relationship between enterprisesrsquoability to learn from firms with the best operation vector inthe same industry and performance improvement The 119909-axis measures the enterprisesrsquo ability to learn from the firmswith the best operation vector and the higher 1198621 indicatesthe higher weight of learning from the firms with the bestoperation vector in the same industry when they adjust thestrategy Through the distribution of sample points most ofthe samples are below 40 this may mean that these firmsrsquowillingness to learn from the best firm in the same industry isnot so strong or that the ability to learn is not very strong Alow 1198621 indicates that 1198622 or 1198623 is very high which means theenterprises tend to learn from their own optimal experience(1198622 is high) or excessive dependence on a configuration state(1198623 is high) The 119910-axis measures the change of ROA in thenext year and most of the sample is distributed between [ndash22] At the same time it can be seen that with the increasein 1198621 the number of samples of negative growth graduallyreduces

As shown in Figure 1 the slope of the regression line ispositive which indicates that enterprises that can learn fromfirmswith the best operations aremore likely to improve theirperformance but some firms with average learning abilitiesare also likely to improve their performance This might


minus8

minus6minus4minus20246810

ΔRO

A0350302502 04 045005 015010

C1

Figure 1 The relationship between the organizational learning capacity from the top firms (1198621) and performance improvement (ΔROA)

minus8

minus6

minus4

minus2

0

2

4

6

8

10

12

ΔRO

A

03 04 05 06 07 08 0902C2

Figure 2 The relationship between the learning capability from the operational capacity vector of the previous best (1198622) and performanceimprovement (ΔROA)

be because these firms have good performance comparedto other firms in the same industry whose correspondingoperational capacity is already close to the maximum Thusthey can achieve better performance by maintaining the bestoperational capacity vector in their records when making thestrategic adjustment

As (H8) is verified we can consider that there is asimilarity between an enterprise and a bird since the formeraims to achieve higher performance in competition and thelatter looks for food with the aim of finding some faster Foran enterprise it will be able to increase ROA if it adjusts itsfinancial strategies the way some industry peers with optimalperformance deploy financial strategies For investors theymay be able to obtain more returns on investment if theychoose an enterprise that is more capable of learning howto deploy financial strategies from the most outstandingenterprise in the industry

We argue that there is a process of changes of the effectsof the extent to which firms rely on the optimal operationalcapacity vector for the performance As a result Model(12) is a nonlinear regression model In this regression ahighly significant correlation is found between ΔROA andthe extent to which firms rely on the optimal operationalcapacity vector The monomial coefficient is minus78837 andthe p value is 0000 which implies that a 1 decrease in

the extent to which firms rely on the optimal operationalcapacity vector is associated with an increase in ΔROA by7884 This shows that firms are in a weaker positionversus the competition and that by adjusting strategies theoperational capacity vector will not rely on the optimal levelInstead the firms will adapt to the current environment andimprove performance significantly However this does notmean that only this can improve performance effectivelyThe quadratic coefficient is 78912 and the p value is 0000significant at the 1 level which means that the firms have acompetitive edge in the competition and are able to improveperformance During the strategic adjustment the firms canmaintain the operational capacity vector to achieve betterperformance and performance improvement The results ofthe regression analyzeHypothesis 9 that is there is a negativecorrelation between the extent to which firms rely on theoptimal operational capacity vector and future performanceimprovement However when the dependence exceeds acertain level its correlation with the future performanceimprovement is positive Figure 2 shows the regression ofModel (12)

Figure 2 describes the relationship between the enter-prisesrsquo ability to learn from its own optimum vector and per-formance improvementThe 119909-axis measures the enterprisesrsquoability to learn from its own optimum vector the higher 1198622


and the higher weight of learning from its own optimumvector when adjusting the strategy Through the distributionof sample points most of the samples are distributed between30 and 70This indicates that most enterprises emphasizetheir experience of operating successfully and consideringthe status of the industry they explore the core compet-itiveness of enterprises conducive to the accumulation ofstrategic adjustment when making the strategic adjustmentsdecision The 119910-axis still measures the change of ROA in thenext year and it can be seen that the sample distribution ismore concentrated in the central region of 1198622 and that thefluctuation of ΔROA is small In the other region of 1198622 thefluctuation and value of ΔROA are larger

The relationship between the extents to which firmsrely on the optimal operational capacity vector to recordand improve performance to a U-shaped curve is shown inFigure 2 This suggests that to achieve performance improve-ment in terms of the optimal operational capacity vector thefirms can maintain the advantage of the optimal operationalcapacity vector develop their core competiveness or rely lesson the optimal operational capacity vector in a record toexplore the operational capacity vector that fits their currentcircumstances and their own status

(H9) is tenable as we expected Actually if the overalldirection is roughly consistent with food location for thebirds that forage the historical optimal location will havea reference value and birds will be able to find the foodfaster by updating the historical optimal location constantlyHowever if the location nearest to the food is also very faraway from the food in the foraging process this location willhave no reference value compared with the location of thebirds nearest to the food Moreover these birds nearest to thefood will however be able to find the food faster if they donot refer much to their own historical location Similarly ifan enterprisersquos historical optimal performance is very poor inthe industry its financial strategy deployment correspondingto its historical optimal performance will have no referencevalueThus such enterprises are more likely to increase ROAif they give up their dependence on the historical optimalperformance to break away from conventions However if anenterprisersquos historical optimal performance is better than therest in the industry its persistent reference to the financialstrategy deployment corresponding to its historical optimalperformance will help it sum up successful experience andaccumulate core competitiveness to increase ROA

The results from Model (13) show that there is nosignificant correlation between the inertia control ability ofthe current operational capacity vector of the firms and theirperformance improvement and thus Hypothesis 10 is nottrue The possible reason why the inertia control ability haslittle effect on performance improvement is that the purposeof the inertia control ability is to examine the importancethat the firms attach to the current operational capacityvector However performance corresponding to the currentoperational capacity vector may be very good To achievebetter performance the firmsmust have better inertia controlability That is as the corresponding coefficient 1198623 is biggerthe corresponding performance of the current operationalcapacity vector may be poor To achieve better performance

the firms must avoid inertia and the corresponding coef-ficient 1198623 must be smaller As a result the effect of theinertia control ability on performance improvement cannotbe considered in a simplistic manner Instead it is associatedwith the quality of the current operational capacity vector ineach period

In terms of the effect of the strategic adjustment capacityon performance improvement in the following year namelythe dependent variable (ΔROA) in the model Hypotheses8 and 9 are tested It is concluded that there is a positivecorrelation between the ability to learn from the firms withoptimal operations and future performance improvementWe also see that there is a negative correlation betweenthe extent to which firms rely on the best operationalcapacity vector and future performance improvement Asdependence exceeds a certain level its correlation with futureperformance improvement is positive In Hypothesis 10 thepositive correlation between the inertia control ability andfuture performance improvement is not true

In brief the empirical analysis of the data from themanufacturing firms of specialized equipment shows that thebird flocking system is similar to the corporate competitionsystem The organizational learning capacity of the bestfirms and the capacity to maintain or depend on the beststatus have an effect on performance improvement Theinterplay between the control ability of the current operationinertia capacity vector and performance improvement is notsignificant

5 Managerial Implications

In the literature on industrial organizations researchers com-bined theories in biology in amarket competition frameworkto discuss pricing and profit problems and entry and exittiming from the perspective of game theory including theDarwin phenomenon in industrial organization [53] Thesestudies are typically the research focus on how decisionsare made Our paper which is different from these stud-ies focuses on sustainable competitive advantage from theperspective of corporate strategic adjustment Following thebird flocking system the situation in which an average firmlearns from the top firms in an industry is similar to a birdflying toward the bird close to the food Firms will achievebetter performance if their learning capacities are strongInstead of implementing a market strategy to achieve short-term performance firms can create sustainable competitiveadvantage by improving strategic adjustment ability

The empirical results show that the enterprise can obtainsustainable competitive advantage through the followingsteps (1) create a vector using financial indicators whichhave significant effects on performance (2) follow themethod mentioned above to measure enterprise strategicadjustment ability in three aspects (3) set strategic goals anddetailed implantation by analyzing the results in step (2) forcompetitiveness resources and environment

In brief the empirical analysis of the data from themanufacturing firms of specialized equipment shows that thebird flocking system is similar to the corporate competitionsystem The organizational learning capacity of the best


firms and the capacity to maintain or depend on the beststatus have an effect on performance improvement Theinterplay between the control ability of the current operationinertia capacity vector and performance improvement is notsignificant

6 Conclusions

Inspired by the bird flocking system this paper investigatesthe effects of strategic adjustment in a corporate compet-itive system on its performance improvement Moreovereach strategic adjustment is decomposed into three aspectslearning from the best firms dependence or maintenance ofadvantage in their performance history and inertial controlof the current status The samples are chosen from the firmsmanufacturing specialized equipment in Chinarsquos machineryindustry The data suggest that there is a positive correlationbetween the capacity to learn from the best firms and theperformance improvement The relationship between thedependence or maintenance of a firmrsquos advantages and per-formance improvement is observed to be a U-shaped curvewhile no significant effect of inertial control on performanceimprovement is found

Besides discovering an analogy between the better perfor-mance of firms and patterns of bird flocking the operationalcapacity vector is also defined in this study Empiricallypresenting the correlation between the operational capacityvector and the firm performance the regression analysis isemployed to show a firmrsquos strategic adjustment which issimilar to the ways birds locate food and other birds inthe bird flocking system Therefore the operational capacityvector of firms with optimal performance is analogous tothe location of the bird closest to the food in bird flockingSpecifically how the operational capacity vector correspondsto the optimal performance is analogous to the bird flockingsystem where the location of the bird is always closest to themost recently located food in the memory The data havealso shown that the corporate competition system is similarto the bird flocking system First similar to the ability torecognize and approach the location of the bird in the flockclosest to the food for facilitate the searching bird to findfood firms with stronger abilities to learn from firms withbetter operational capacity are more likely to improve theirperformance Secondly if the location is closer to thememoryin bird flocking it will be closer to the location in memory Ifthe location is not closer to the memory at the moment thenit will search other directions Similarly the extent to whichthe firms rely on or maintain the operational capacity vectoris determined by whether their corresponding performanceis close to the optimal one Finally the inertia control abilityof the bird regarding the current location in bird flockingdepends onwhether its location is close to the food Likewisethe impact of inertia control on performance improvementis not significant instead it is associated with whether thecurrent performance is close to the optimal one

Finally it is assumed that the operational capacity vec-tor can fully represent the results of strategic adjustmentHowever there are actually two problems One point is thatthe operational capacity vector does not necessarily show the

result of strategic adjustment Considering the circumstancesof other firms some firms are able to take a number of strate-gic measures such as reducing costs focusing diversifyingand horizontal and vertical integration which are not alwaysrepresented by the operational capacity vector The otherpoint is that the operational capacity vector varies duringoperation due to the overall environment changes in theindustry but firms do not change their strategies accordinglyAs a result the further research should focus on the selectionand design of more scientific and comprehensive indices todepict the results of strategic adjustment Furthermore aseach capacity in strategic adjustment is determined equa-tions are adopted in this study but they do not occur incalculations or the actual operation of firms Theoreticallyinnumerable solutions or no solutions might emerge Thusthe method in this study may no longer be applicable Hencebetter solutions and methods for analyzing each capacityneed further investigation

This paper proposes a method to quantitatively measurethe strategic adjustment capacity by referring to the birdflocking system With this method the relationship betweenfirmsrsquo strategy adjustment capacity and performance canbe obtained which guides firms to improve their strategicadjustment capacity and maintain sustainable competitiveadvantages The main contributions of this paper includethe following aspects (1) By interdisciplinary research anew idea is put forward for studies on business compe-tition and sustainable development By virtue of the birdforaging system a method is proposed for the optimizationand adjustment of financial strategic resource allocationsuggesting that firms should learn from some industrycounterparts with optimal performance and locate financialstrategic resources according to their optimal performancein history (2) Factors critical to corporate financial strate-gies are determined by empirical verification The financialindicators cited in this study are key factors to corporatefinancial strategy which have direct influence on ROAThus enterprises should pay more attention to the optimalallocation of financial strategic resources Unfortunatelythis study only suggests that the enterprises that adjusttheir financial strategic resource allocation through highlyreferring to that of enterprises with optimal performanceor adhere to their own unique financial strategic resourceallocation in history can usually achieve higher performanceHowever the study does not reveal which strategy will bemore effective for what kind of industries or business entitiesin what kind of environments Therefore it is the furtherresearch direction to determine a more specific decision-making basis for enterprises by considering the industryfactors external environment and the corporate life cycleand so on (3) The method for evaluating the enterprisecapability of financial strategy adjustment in this paper mayinspire investors to make investments from a long-term andstrategic perspective Different from the conventional simpleprediction made in accordance with financial statement datathe method proposed in this paper can be utilized to evaluatean enterprisersquos endogenous capacity for strategic adjustmentof financial resource allocation According to this methodthe enterprises with strong strength are more likely to seize


every external opportunity and withstand all external risksto achieve relatively better performance even if some changesmay occur in the external environment in the future

Though the relationship between three aspects of strategicadjustment capacity and performance is discussed thesecapacities may not measure strategic adjustment capacitysufficiently As some connections between corporate compe-tition and other theories in biology will make the researchmore comprehensive this paper focuses on how to obtainsustainable competitive advantages by improving the strate-gic adjustment capacity Another perspective is to discusshow these three aspects of strategic adjustment capacity affectthe corporate performance Additionally firms with similarstrategic adjustment capacity possibilities share similar fea-tures Hence it is interesting to analyze their performanceand features through cluster and discriminant analyses forsupporting the current theories or even putting forward newtheories

Conflicts of Interest

The authors declare no conflicts of interest

Authorsrsquo Contributions

Shiyuan Wu developed the original idea and was responsiblefor data collection and processing Shou Chen contributedto the research design and provided guidance Chao Maodesigned the algorithm and calculated the adjustment abil-ity by MATLAB Boya Li provided additional guidanceand advice All authors have read and approved the finalmanuscript

Acknowledgments

The authorsrsquo work was supported by the Foundation forInnovative Research Groups of the National Natural ScienceFoundation of China (Grant no 71521061) National NaturalScience Foundation of China (Grant no 71031004) andNational Natural Science Foundation Youth Project (Grantno 71202137)

References

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[2] P Shrivastava Advances in Strategic Management Organiza-tional Learning and Strategic Management Emerald GroupGreenwicht England 1997

[3] B K Brockman and R M Morgan ldquoThe Role of ExistingKnowledge in New Product Innovativeness and PerformancerdquoDecision Sciences vol 34 no 2 pp 385ndash419 2003

[4] M J Tippins and R S Sohi ldquoIT competency and firm per-formance is organizational learning a missing linkrdquo StrategicManagement Journal vol 24 no 8 pp 745ndash761 2003

[5] W E Baker and J M Sinkula ldquoThe synergistic effect ofmarket orientation and learning orientation on organizational

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[6] P Ussahawanitchakit ldquoImpacts of organizational learning oninnovation orientation and firm efficiency an empirical assess-ment of accounting firms in Thailandrdquo International Journal ofBusiness Research vol 8 no 4 pp 1ndash12 2008

[7] M E Porter ldquoFrom Competitive Advantage to CorporateStrategyrdquoHarvard Business Review vol 5 no 6 pp 43ndash59 1985

[8] J Kennedy and R Eberhart ldquoParticle swarm optimizationrdquoin Proceedings of the IEEE International Conference on NeuralNetworks vol 4 pp 1942ndash1948 1995

[9] R C Eberhart and J Kennedy ldquoA new optimizer using particleswarm theoryrdquo in Proceedings of the 6th International Sympo-sium on Micro Machine and Human Science (MHS rsquo95) pp 39ndash43 Nagoya Japan October 1995

[10] J Kennedy ldquoThe particle swarm social adaptation of knowl-edgerdquo in Proceedings of the IEEE International Conference onEvolutionary Computation (ICEC rsquo97) pp 303ndash308 Indianapo-lis Ind USA April 1997

[11] A-M Talonpoika T Karri M Pirttila and S Monto ldquoDefinedstrategies for financial working capital managementrdquo Interna-tional Journal of Managerial Finance vol 12 no 3 pp 277ndash2942016

[12] C-G Li H-M Dong S Chen and Y Yang ldquoWorking capitalmanagement corporate performance and strategic choices ofthe wholesale and retail industry in Chinardquo Scientific WorldJournal vol 2014 Article ID 953945 2014

[13] G Q Chen ldquoEmpirical study on relationship among organiza-tional influential factors (organizational) learning capabilitiesand organizational performancerdquo Journal of Management Sci-ences in China vol 8 no 1 pp 48ndash61 2005

[14] M Skerlavaj and V Dimovski ldquoOrganizational Learning andPerformance in Two National Cultures A Multi-group Struc-tural EquationModelingApproachrdquo inKnowledgeManagementand Organizational Learning vol 4 of Annals of InformationSystems pp 321ndash367 Springer US Boston MA USA 2009

[15] G Chen ldquoOrganizational Learning and LearningOrganizationConcept CapabilityModel Measurement and Impact onOrga-nizational PerformancerdquoManagement Review vol 21 no 1 pp107ndash116 2009

[16] P Jaroslav and M Iaeng ldquoCorporate Financial Strategy inSMEsrdquo in Proceedings of theWorld Congress on Engineering vol3 pp 1ndash3 London UK 2009

[17] O I Falope and O T Ajilore ldquoWorking capital managementand corporate profitability Evidence from panel data analysisof selected quoted companies in Nigeriardquo Research Journal ofBusiness Management vol 3 no 3 pp 73ndash84 2009

[18] D M Mathuva ldquoThe influence of working capital managementcomponents on corporate profitability A survey on Kenyanlisted firmsrdquo Research Journal of Business Management vol 4no 1 pp 1ndash11 2010

[19] G Amarjit N Biger andNMathur ldquoTheRelationship betweenworking capital management and profitability evidence fromthe United Statesrdquo Business and Economics Journal vol 2010no 10 9 pages 2010

[20] H H Shin and L Soenen ldquoEfficiency of working capitalmanagement and corporate profitabilityrdquo Financial Practice andEducation vol 8 no 1 pp 37ndash45 1998

[21] M Deloof ldquoDoes working capital management affect prof-itability of Belgian firmsrdquo Journal of Business Finance andAccounting vol 30 no 3-4 pp 573ndash587 2003


[22] A M A Eljelly ldquoLiquidity ndash profitability tradeoff An empiricalinvestigation in an emerging marketrdquo International Journal ofCommerce and Management vol 14 no 2 pp 48ndash61 2004

[23] S Besley andR LMeyer ldquoAnEmpirical Investigation of FactorsAffecting The Cash Conversion Cyclerdquo in Paper Presented atthe Annual Meeting of the Financial Management Association1987 An empirical investigation of factors affecting the cashconversion cycle Paper Presented at the AnnualMeeting of theFinancial Management Association

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[25] M S Nazir and T Afza ldquoImpact of aggressive working capitalmanagement policy on firmsrsquo profitabilityrdquo The IUP Journal ofApplied Finance vol 15 no 8 pp 20ndash30 2009

[26] I Lazaridis and D Tryfonidis ldquoRelationship between workingcapital management and profitability of listed companies in theAthens stock exchangerdquo Journal of Financial Management andAnalysis vol 19 no 1 pp 26-25 2006

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[37] P M Frederic A F Vandome and J McBrewster ComplexAdaptive System VDM Publishing House Saarbrucken Ger-many 2010

[38] A Bennet and D Bennet ldquoOrganizational survival in the newworld the intelligent complex adaptive system a new theoryof the firmrdquo Brain A Journal of Neurology vol 138 no 8 pp2140ndash2146 2004

[39] J B Barney ldquoResource-based theories of competitive advantageA ten-year retrospective on the resource-based viewrdquo Journal ofManagement vol 27 no 6 pp 643ndash650 2001

[40] M Grant Robert ldquoThe Resource-Based Theory of CompetitiveAdvantage Implications for Strategy Formulationrdquo Knowledgeand Strategy vol 33 no 3 pp 3ndash23 1991

[41] M V Russo and P A Fouts ldquoA resource-based perspectiveon corporate environmental performance and profitabilityrdquoAcademy of Management Journal vol 40 no 3 pp 534ndash5591997

[42] I F Wilkinson ldquoThe evolution of an evolutionary perspectiveon b2b businessrdquo Journal of Business and Industrial Marketingvol 21 no 7 pp 458ndash465 2006

[43] S Leih Evolutionary Theory of The Multinational CorporationSocial Science Electronic 2016

[44] A M Colpan and G Jones ldquoBusiness groups entrepreneurshipand the growth of the Koc Group in Turkeyrdquo Business Historyvol 58 no 1 pp 69ndash88 2016

[45] A P Degeus ldquoPlanning as learningrdquo Harvard Business Reviewvol 66 no 1 pp 70ndash74 1988

[46] T Knauer and A Wohrmann ldquoWorking capital managementand firm profitabilityrdquo Journal of Management Control vol 24no 1 pp 77ndash87 2013

[47] S Banos-Caballero P J Garcıa-Teruel and P Martınez-SolanoldquoWorking capital management corporate performance andfinancial constraintsrdquo Journal of Business Research vol 67 no3 pp 332ndash338 2014

[48] A Y Salman O O Folajin and A O Oriowo ldquoWorking capitalmanagement and profitability a study of selected listed manu-facturing companies in nigerian stock exchangerdquo InternationalJournal of Academic Research in Business and Social Sciences vol4 no 8 pp 287ndash295 2014

[49] G A Afrifa ldquoNet working capital cash flow and performanceof UK SMEsrdquo Review of Accounting and Finance vol 15 no 1pp 21ndash44 2016

[50] N Aktas E Croci and D Petmezas ldquoIs working capital man-agement value-enhancing Evidence from firm performanceand investmentsrdquo Journal of Corporate Finance vol 30 no 1pp 98ndash113 2015

[51] H Tran M Abbott and C Jin Yap ldquoHow does working capitalmanagement affect the profitability of Vietnamese small- andmedium-sized enterprisesrdquo Journal of Small Business andEnterprise Development vol 24 no 1 pp 2ndash11 2017

[52] J Grahovac and D J Miller ldquoCompetitive advantage andperformance The impact of value creation and costliness ofimitationrdquo Strategic Management Journal vol 30 no 11 pp1192ndash1212 2009

[53] J TiroleTheTheory of Industrial Organization MIT Press 1988

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of


Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of


Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of


Mathematical PhysicsAdvances in

Complex AnalysisJournal of


OptimizationJournal of


CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of


Operations ResearchAdvances in

Journal of


Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Algebra

Discrete Dynamics in Nature and Society



Decision SciencesAdvances in

Journal of


Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of


fact the principle of bird flocking has already been appliedto an algorithm of artificial intelligence by Dr Kennedy andDr Eberhart who proposed a population based stochasticoptimization technique named particle swarm optimization(PSO) [8] They note that during bird flocking the followinglocation is determined by three related factors the currentbird location closest to the food the closest location thatthey find in their own search for food and their currentlocation and speed [9] It solves a problem by having apopulation of candidate solutions here dubbed particles andmoving these particles around in the search-space accordingto simple mathematical formulae over the particlersquos positionand velocity Each particlersquos movement is influenced by itslocal best known position but is also guided toward the bestknown positions in the search-space which are updated asbetter positions are found by other particles This is expectedto move the swarm toward the best solutions [10] In thispaper we argue that a firmrsquos performance in the followingstrategic adjustment is also determined by three factors thefirmrsquos performance of strategic elements it identifies in firmswith optimal strategic performance the firmrsquos performance ofstrategic elements corresponding to the optimal performancein its own history and the current firmrsquos performance ofstrategic elements The adjustment capacity of firm strategiescan be represented by the controlling capacity in these threefactors From the perspective of the dynamic adjustmentcapacity of firm strategies we choose some indicators in thefinancial statement that reflect the firmrsquos abilities in assetoperation earning profits and cash flow management tomeasure the results of strategy adjustment [11] We use thereturn on assets (ROA) to measure the firmrsquos performanceAfter testing these indices that have a significant impacton firm performance and comparing them with the systemof bird flocking the strategic adjustment capacity displayedcan be obtained from these three aspects The relationshipbetween the adjustment capacity of a firmrsquos strategies andits performance can then be tested Finally the adjustmentcapacity of firm strategies can be evaluated

Just like the fittest organisms surviving in the ecologicalenvironment the business entities also strive for survival anddevelopment through constantly improving their sustainablecompetitiveness in the business ecological environmentEnterprises often need to face the problem of ldquohow betterdecision-making helps to improve sustainabilityrdquo seek theldquosustainable business and developmentrdquo mode and explorethe ldquosustainable leadership and managementrdquo All of theabove concern the topmost strategic planning issues of enter-prises so the ability to make strategic adjustments accordingto the internal and external environment is the key for enter-prises to stay ahead and obtain industry performance Thispaper takes the enterprise strategy adjustment ability as theentry point and answers the above questions through the birdflocking and firm competition specifically including howto divide the dimension of enterprise strategic adjustmentabilities how to quantify the enterprise strategic adjustmentabilities and what the impacts of strategy adjustment abilitiesare at different dimensions on corporate performance [12]On this basis the article aims to build the core competi-tiveness of enterprises and provide a decision-making basis

for sustainable advantages However in times of disruptivechangewithin different industries this competitiveness couldbe drastically reduced so the disruptive companies are not inthe scope of this research

This paper contributes to the research surrounding theorganizational learning uncovered in prior research Mostprior research is focused on exploring the multiple dimen-sions of organizational learning ability from the inside ofan enterprise to build an organizational learning abilityevaluation system and evaluation model and then discussesthe relationship between organizational learning ability andperformance such as the 6P-1B model of learning orga-nization [13] Multi-group Structural Equation ModelingApproach [14] and Capability Model [15] These studies takethe perspective of individual enterprises to determine theirability to learnThey emphasize the evaluation of the learningprocess and most of the qualitative analysis is based onquestionnaires Furthermore most of these studies are staticstudies which produce results only at a certain time point inthe evaluation of enterprise organizational learning abilitiesOur paper studies the dynamic adjustment of strategy andthe ability of organizational learning in a certain periodfrom the enterprisersquos external performance Since the datawe use are financial data from the financial statements thispaper proposes a quantitative analysis method to evaluate thestrategy adjustment capacity

Two points should be made in relating this paperrsquosfindings to other research First our focus is different Onthe one hand we emphasize the effect of organizationallearning which reflects the ability of organizational learningon the other hand for the ability of the enterprise strategydecision we focus on the enterprisersquos learning strategies andlearning ability in situations of market competition based onwhich we can evaluate the enterprisersquos strategic adjustmentability and study the relationship between organizationallearning and performance Second the strategic adjustmentand organizational learning are posited to be similar to thebird flocking process Furthermore the purpose of this studyis not only to evaluate the enterprise learning ability but alsoto guide firms on how to learn and to promote the strategicadjustment capacity to obtain better performance

The remainder of the paper is organized as followsSection 2 is the literature review Section 3 introduces thevector of strategic adjustment the concept of the strategicadjustment capacity and the measurement of related vari-ables Section 4 explains the data and the way to computethe strategic adjustment capacity followed by the empiricalresults Section 5 discusses managerial implications andSection 6 summarizes and concludes the paper

2 Literature Review

The way that firm strategy influences the performancehas received great attention in recent years Firm strategyexpresses a basic idea of how to reach firmrsquos objectives Finan-cial strategy as a functional strategy plays an important rolein firm strategyThere are some companiesrsquo turnover decreas-ing because of having no strategic financial management [16]However the definition of financial strategy is somewhat











3 Methodology










(1)


(2)


(3)


(4)


(5)


(6)


(7)




119864119862119894119905 =

[[[[[[[[[[[[[[[


]]]]]]]]]]]]]]]

(8)



119883119894119905+1 = 997888120596 1198941119884119905119868_best + 997888120596 1198942119885119894119879_best + 997888120596 1198943119883119894119905 (9)



119864119862119894119895119905+1 = 1205961198941198951119864119862119905119895119868119894_best + 1205961198941198952119864119862119894119895119879119905_best + 1205961198941198953119864119862119894119895119905 (10)










Variable Definition

















ΔROA = 1205728 + 12057381198621 + 119906119894 (11)

ΔROA = 1205729 + 12057391198622 + 120574911986222 + 119906119894 (12)

ΔROA = 12057210 + 120573101198623 + 119906119894 (13)





1198641198621198941198952 = 12059611989411989511198641198621119895119868119894_best + 12059611989411989521198641198621198941198951198791_best + 12059611989411989531198641198621198941198951 (14)

1198641198621198941198953 = 12059611989411989511198641198622119895119868119894_best + 12059611989411989521198641198621198941198951198792_best + 12059611989411989531198641198621198941198952 (15)

1198641198621198941198954 = 12059611989411989511198641198623119895119868119894_best + 12059611989411989521198641198621198941198951198793_best + 12059611989411989531198641198621198941198953 (16)

1198641198621198941198955 = 12059611989411989511198641198624119895119868119894_best + 12059611989411989521198641198621198941198951198794_best + 12059611989411989531198641198621198941198954

(17)

119864119862119894119895119905+1 = 1205961198941198951119864119862119905119895119868119894_best + 1205961198941198952119864119862119894119895119879119905_best + 1205961198941198953119864119862119894119895119905

(18)


(19)






120596i =

[[[[[[[[[[[[[[[

12059611989411120596119894211205961198943112059611989441120596119894511205961198946112059611989471

12059611989412120596119894221205961198943212059611989442120596119894521205961198946212059611989472

12059611989413120596119894231205961198943312059611989443120596119894531205961198946312059611989473

]]]]]]]]]]]]]]]

(20)


[11986210158401198941 11986210158401198942 11986210158401198943] = [100381610038161003816100381612057211003816100381610038161003816 100381610038161003816100381612057221003816100381610038161003816 100381610038161003816100381612057231003816100381610038161003816 100381610038161003816100381612057241003816100381610038161003816 100381610038161003816100381612057251003816100381610038161003816 100381610038161003816100381612057261003816100381610038161003816 100381610038161003816100381612057271003816100381610038161003816]times 120596i

(21)


1198621198941 = 1198621015840119894111986210158401198941 + 11986210158401198942 + 11986210158401198943

1198621198942 = 1198621015840119894211986210158401198941 + 11986210158401198942 + 11986210158401198943

1198621198943 = 1198621015840119894311986210158401198941 + 11986210158401198942 + 11986210158401198943

(22)

4 Results


1199091015840119894119895119905 =119909119894119895119905 minus 120583ind119894119895119905

120590ind119894119895119905 (23)








12503




1326


4005


2195



4333 3669 3784 4289 4174 3561 3801















1395



1977

1198623 4437068









minus8


ΔRO

A0350302502 04 045005 015010

C1


minus8

minus6

minus4

minus2

0

2

4

6

8

10

12

ΔRO

A

03 04 05 06 07 08 0902C2





















6 Conclusions













Acknowledgments


References































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra





Journal of














3 Methodology










(1)


(2)


(3)


(4)


(5)


(6)


(7)




119864119862119894119905 =

[[[[[[[[[[[[[[[


]]]]]]]]]]]]]]]

(8)



119883119894119905+1 = 997888120596 1198941119884119905119868_best + 997888120596 1198942119885119894119879_best + 997888120596 1198943119883119894119905 (9)



119864119862119894119895119905+1 = 1205961198941198951119864119862119905119895119868119894_best + 1205961198941198952119864119862119894119895119879119905_best + 1205961198941198953119864119862119894119895119905 (10)










Variable Definition

















ΔROA = 1205728 + 12057381198621 + 119906119894 (11)

ΔROA = 1205729 + 12057391198622 + 120574911986222 + 119906119894 (12)

ΔROA = 12057210 + 120573101198623 + 119906119894 (13)





1198641198621198941198952 = 12059611989411989511198641198621119895119868119894_best + 12059611989411989521198641198621198941198951198791_best + 12059611989411989531198641198621198941198951 (14)

1198641198621198941198953 = 12059611989411989511198641198622119895119868119894_best + 12059611989411989521198641198621198941198951198792_best + 12059611989411989531198641198621198941198952 (15)

1198641198621198941198954 = 12059611989411989511198641198623119895119868119894_best + 12059611989411989521198641198621198941198951198793_best + 12059611989411989531198641198621198941198953 (16)

1198641198621198941198955 = 12059611989411989511198641198624119895119868119894_best + 12059611989411989521198641198621198941198951198794_best + 12059611989411989531198641198621198941198954

(17)

119864119862119894119895119905+1 = 1205961198941198951119864119862119905119895119868119894_best + 1205961198941198952119864119862119894119895119879119905_best + 1205961198941198953119864119862119894119895119905

(18)


(19)






120596i =

[[[[[[[[[[[[[[[

12059611989411120596119894211205961198943112059611989441120596119894511205961198946112059611989471

12059611989412120596119894221205961198943212059611989442120596119894521205961198946212059611989472

12059611989413120596119894231205961198943312059611989443120596119894531205961198946312059611989473

]]]]]]]]]]]]]]]

(20)


[11986210158401198941 11986210158401198942 11986210158401198943] = [100381610038161003816100381612057211003816100381610038161003816 100381610038161003816100381612057221003816100381610038161003816 100381610038161003816100381612057231003816100381610038161003816 100381610038161003816100381612057241003816100381610038161003816 100381610038161003816100381612057251003816100381610038161003816 100381610038161003816100381612057261003816100381610038161003816 100381610038161003816100381612057271003816100381610038161003816]times 120596i

(21)


1198621198941 = 1198621015840119894111986210158401198941 + 11986210158401198942 + 11986210158401198943

1198621198942 = 1198621015840119894211986210158401198941 + 11986210158401198942 + 11986210158401198943

1198621198943 = 1198621015840119894311986210158401198941 + 11986210158401198942 + 11986210158401198943

(22)

4 Results


1199091015840119894119895119905 =119909119894119895119905 minus 120583ind119894119895119905

120590ind119894119895119905 (23)








12503




1326


4005


2195



4333 3669 3784 4289 4174 3561 3801















1395



1977

1198623 4437068









minus8


ΔRO

A0350302502 04 045005 015010

C1


minus8

minus6

minus4

minus2

0

2

4

6

8

10

12

ΔRO

A

03 04 05 06 07 08 0902C2





















6 Conclusions













Acknowledgments


References































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra





Journal of






119864119862119894119905 =

[[[[[[[[[[[[[[[


]]]]]]]]]]]]]]]

(8)



119883119894119905+1 = 997888120596 1198941119884119905119868_best + 997888120596 1198942119885119894119879_best + 997888120596 1198943119883119894119905 (9)



119864119862119894119895119905+1 = 1205961198941198951119864119862119905119895119868119894_best + 1205961198941198952119864119862119894119895119879119905_best + 1205961198941198953119864119862119894119895119905 (10)










Variable Definition

















ΔROA = 1205728 + 12057381198621 + 119906119894 (11)

ΔROA = 1205729 + 12057391198622 + 120574911986222 + 119906119894 (12)

ΔROA = 12057210 + 120573101198623 + 119906119894 (13)





1198641198621198941198952 = 12059611989411989511198641198621119895119868119894_best + 12059611989411989521198641198621198941198951198791_best + 12059611989411989531198641198621198941198951 (14)

1198641198621198941198953 = 12059611989411989511198641198622119895119868119894_best + 12059611989411989521198641198621198941198951198792_best + 12059611989411989531198641198621198941198952 (15)

1198641198621198941198954 = 12059611989411989511198641198623119895119868119894_best + 12059611989411989521198641198621198941198951198793_best + 12059611989411989531198641198621198941198953 (16)

1198641198621198941198955 = 12059611989411989511198641198624119895119868119894_best + 12059611989411989521198641198621198941198951198794_best + 12059611989411989531198641198621198941198954

(17)

119864119862119894119895119905+1 = 1205961198941198951119864119862119905119895119868119894_best + 1205961198941198952119864119862119894119895119879119905_best + 1205961198941198953119864119862119894119895119905

(18)


(19)






120596i =

[[[[[[[[[[[[[[[

12059611989411120596119894211205961198943112059611989441120596119894511205961198946112059611989471

12059611989412120596119894221205961198943212059611989442120596119894521205961198946212059611989472

12059611989413120596119894231205961198943312059611989443120596119894531205961198946312059611989473

]]]]]]]]]]]]]]]

(20)


[11986210158401198941 11986210158401198942 11986210158401198943] = [100381610038161003816100381612057211003816100381610038161003816 100381610038161003816100381612057221003816100381610038161003816 100381610038161003816100381612057231003816100381610038161003816 100381610038161003816100381612057241003816100381610038161003816 100381610038161003816100381612057251003816100381610038161003816 100381610038161003816100381612057261003816100381610038161003816 100381610038161003816100381612057271003816100381610038161003816]times 120596i

(21)


1198621198941 = 1198621015840119894111986210158401198941 + 11986210158401198942 + 11986210158401198943

1198621198942 = 1198621015840119894211986210158401198941 + 11986210158401198942 + 11986210158401198943

1198621198943 = 1198621015840119894311986210158401198941 + 11986210158401198942 + 11986210158401198943

(22)

4 Results


1199091015840119894119895119905 =119909119894119895119905 minus 120583ind119894119895119905

120590ind119894119895119905 (23)








12503




1326


4005


2195



4333 3669 3784 4289 4174 3561 3801















1395



1977

1198623 4437068









minus8


ΔRO

A0350302502 04 045005 015010

C1


minus8

minus6

minus4

minus2

0

2

4

6

8

10

12

ΔRO

A

03 04 05 06 07 08 0902C2





















6 Conclusions













Acknowledgments


References































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra





Journal of






Variable Definition

















ΔROA = 1205728 + 12057381198621 + 119906119894 (11)

ΔROA = 1205729 + 12057391198622 + 120574911986222 + 119906119894 (12)

ΔROA = 12057210 + 120573101198623 + 119906119894 (13)





1198641198621198941198952 = 12059611989411989511198641198621119895119868119894_best + 12059611989411989521198641198621198941198951198791_best + 12059611989411989531198641198621198941198951 (14)

1198641198621198941198953 = 12059611989411989511198641198622119895119868119894_best + 12059611989411989521198641198621198941198951198792_best + 12059611989411989531198641198621198941198952 (15)

1198641198621198941198954 = 12059611989411989511198641198623119895119868119894_best + 12059611989411989521198641198621198941198951198793_best + 12059611989411989531198641198621198941198953 (16)

1198641198621198941198955 = 12059611989411989511198641198624119895119868119894_best + 12059611989411989521198641198621198941198951198794_best + 12059611989411989531198641198621198941198954

(17)

119864119862119894119895119905+1 = 1205961198941198951119864119862119905119895119868119894_best + 1205961198941198952119864119862119894119895119879119905_best + 1205961198941198953119864119862119894119895119905

(18)


(19)






120596i =

[[[[[[[[[[[[[[[

12059611989411120596119894211205961198943112059611989441120596119894511205961198946112059611989471

12059611989412120596119894221205961198943212059611989442120596119894521205961198946212059611989472

12059611989413120596119894231205961198943312059611989443120596119894531205961198946312059611989473

]]]]]]]]]]]]]]]

(20)


[11986210158401198941 11986210158401198942 11986210158401198943] = [100381610038161003816100381612057211003816100381610038161003816 100381610038161003816100381612057221003816100381610038161003816 100381610038161003816100381612057231003816100381610038161003816 100381610038161003816100381612057241003816100381610038161003816 100381610038161003816100381612057251003816100381610038161003816 100381610038161003816100381612057261003816100381610038161003816 100381610038161003816100381612057271003816100381610038161003816]times 120596i

(21)


1198621198941 = 1198621015840119894111986210158401198941 + 11986210158401198942 + 11986210158401198943

1198621198942 = 1198621015840119894211986210158401198941 + 11986210158401198942 + 11986210158401198943

1198621198943 = 1198621015840119894311986210158401198941 + 11986210158401198942 + 11986210158401198943

(22)

4 Results


1199091015840119894119895119905 =119909119894119895119905 minus 120583ind119894119895119905

120590ind119894119895119905 (23)








12503




1326


4005


2195



4333 3669 3784 4289 4174 3561 3801















1395



1977

1198623 4437068









minus8


ΔRO

A0350302502 04 045005 015010

C1


minus8

minus6

minus4

minus2

0

2

4

6

8

10

12

ΔRO

A

03 04 05 06 07 08 0902C2





















6 Conclusions













Acknowledgments


References































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra





Journal of








120596i =

[[[[[[[[[[[[[[[

12059611989411120596119894211205961198943112059611989441120596119894511205961198946112059611989471

12059611989412120596119894221205961198943212059611989442120596119894521205961198946212059611989472

12059611989413120596119894231205961198943312059611989443120596119894531205961198946312059611989473

]]]]]]]]]]]]]]]

(20)


[11986210158401198941 11986210158401198942 11986210158401198943] = [100381610038161003816100381612057211003816100381610038161003816 100381610038161003816100381612057221003816100381610038161003816 100381610038161003816100381612057231003816100381610038161003816 100381610038161003816100381612057241003816100381610038161003816 100381610038161003816100381612057251003816100381610038161003816 100381610038161003816100381612057261003816100381610038161003816 100381610038161003816100381612057271003816100381610038161003816]times 120596i

(21)


1198621198941 = 1198621015840119894111986210158401198941 + 11986210158401198942 + 11986210158401198943

1198621198942 = 1198621015840119894211986210158401198941 + 11986210158401198942 + 11986210158401198943

1198621198943 = 1198621015840119894311986210158401198941 + 11986210158401198942 + 11986210158401198943

(22)

4 Results


1199091015840119894119895119905 =119909119894119895119905 minus 120583ind119894119895119905

120590ind119894119895119905 (23)








12503




1326


4005


2195



4333 3669 3784 4289 4174 3561 3801















1395



1977

1198623 4437068









minus8


ΔRO

A0350302502 04 045005 015010

C1


minus8

minus6

minus4

minus2

0

2

4

6

8

10

12

ΔRO

A

03 04 05 06 07 08 0902C2





















6 Conclusions













Acknowledgments


References































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra





Journal of








12503




1326


4005


2195



4333 3669 3784 4289 4174 3561 3801















1395



1977

1198623 4437068









minus8


ΔRO

A0350302502 04 045005 015010

C1


minus8

minus6

minus4

minus2

0

2

4

6

8

10

12

ΔRO

A

03 04 05 06 07 08 0902C2





















6 Conclusions













Acknowledgments


References































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra





Journal of










1395



1977

1198623 4437068









minus8


ΔRO

A0350302502 04 045005 015010

C1


minus8

minus6

minus4

minus2

0

2

4

6

8

10

12

ΔRO

A

03 04 05 06 07 08 0902C2





















6 Conclusions













Acknowledgments


References































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra





Journal of





minus8


ΔRO

A0350302502 04 045005 015010

C1


minus8

minus6

minus4

minus2

0

2

4

6

8

10

12

ΔRO

A

03 04 05 06 07 08 0902C2





















6 Conclusions













Acknowledgments


References































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra





Journal of


















6 Conclusions













Acknowledgments


References































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra





Journal of











Acknowledgments


References































































Volume 2014




Journal of











Journal of


Function Spaces






Algebra





Journal of












































Volume 2014




Journal of











Journal of


Function Spaces






Algebra





Journal of




strategic adjustment capacity, sustained competitive

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