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This is causing us such misery. Jeers to B Positive’s voody awtu opening credits. Each letter Can appear only once in each horizontal row, as well as in every vertical column and in each of the 3×3 grids.

Each finished puzzle will contain the spelling of a TV star, character, setting or show. The finished grid spelled out Ratched. Periodical postage paid at Newtown Square, PA and at additional mailing offices. If possible, include the mailing label from your latest TV Guide.

Allow 5 weeks for arrival of first copy and address changes. No material in TV Guide, including program information, may be reprinted without permission of the copyright owner. With a long-lasting battery and powerful speaker, conversations are loud and clear.

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Lancet Cummings J. The Neuropsychiatric Inventory: comprehensive assessment of psychopathology in dementia. Neurology Validity of a novel computerized cognitive battery for mild cognitive impairment. BMC Geriatrics 3: Erdfelder, E. J Psychiatr Res. Clock drawing: a Neuropsychological Analysis. Oxford university press: New York. Lawton, M. Morris, J. Sarne-Fleischmann, V. Social and Humanistic Computing, Vol. F rom national and s tate onl ine e lections al l t he w ay t o the s election of v ideo c ontents i n m odern T V networks, m assively l arge num ber of us ers m akes j oint de cisions.

T he r esearch out lined i n t his proposal recognizes this distinct type of a decision support system DSS — the massively distributed DSS. Specifically, the research examines the distributed computational architecture of this type of DSS and st udies co mprehensively i ts p erformance ch aracteristics.

U sing a g round-breaking pe rformance evaluation approach it exp lores the relationships between expected DSS performance and essential attributes o f ma ssively d istributed d ecision si tuations su ch a s d ecision p ace, d ecisions interdependence, d ecision su pport co mplexity a nd number o f u sers.

T his rese arch b uilds on — and integrates — three di stinct bodi es of k nowledge, nam ely D SS, di stributed c omputational s ystems and performance evaluation of computational systems. Keywords: DSS, performance evaluation, distributed systems, system design. What is common to all these decision systems is that they are massively distributed, binding together a large n umber o f d ecision makers, who can , i n some c ases, b e mutually d ependent.

T echnology en hancements al so contribute t o t he e mergence o f co mputerized su pporting cap abilities t o t hese d ecision p rocesses, leading to a r efined type of IT application, namely: Massively Distributed Decision Support Systems or in short MD2S2. Since such DSSs support large number of users, a new challenge, that DSS research has so far been completely o blivious to , is r aised — system p erformance.

T o da te, t he body of r esearch on D SS architecture has focused mainly on single-computer stand-alone systems. This r esearch de als w ith t he pe rformance challenges by e xamining ho w di fferent di stributed architectures of decision support components — the independent variable in this study — affect total system performance — the dependent variable — in different decision support use-cases.

We adopt the view that DSS are composed of three generic components, namely dialogue management, data management a nd m odel management. T he di stribution of t hese c omponents ove r a c ommunication network r aises p ractical architectural dilemmas an d h as co nsequences i n t erms o f co mplexity o f application, response time, survivability, privacy protection, and cost of configuration. Following prior r esearch we d efine ch aracteristic d imensions i. Allowing the values of each dimension to vary, yields different architectures, which are investigated in turn with respect to their performance.

Consequently we are able to determine the most suitable architecture in t erms o f p erformance f or d ifferent d ecision si tuations an d p latforms. T his investigation is o pen-ended an d ex ploratory. If so, what is the effect? We finally confirm the model through its application in the concrete case of television under various decision situations.

In t his c ontext, we s eek t o answer how s hould our general model be applied t o “television DSS”. These dimensions are presented in Figure 1. Decision Situation dimensions. In t he s ingle-user DSS, d ecision p oints’ d ependence i. GDSSs, in contrast, involve issues of decision interdependence as they imply mechanisms that enable group s essions, ne gotiations, gr oup decision m aking, a nd ot her gr oup pr ocesses de signed t o s upport distributed decision processes Nunamaker In M D2S2, a d ecision c an af fect o ther u sers o n a r ange o f d ifferent l evels.

A t one e nd of t his continuum, a decision taken by a user can directly influence many other users. For example, if an eBay user purchased a unique item from another user, no ot her user can still buy this item. At the opposite end of t he c ontinuum, de cisions made by o ne us er ha ve no i mpact on ot her us ers.

An e xample of independent decisions i s i llustrated i n election v oting s ystems. Of co urse, m any d ecisions sy stems reflect an intermediate level of influence.

The very fact that MD2S2s ar e u sed b y l arge n umber o f u sers r aises p erformance q uestions; I deally we r equire t hat system performance will not degrade significantly as the number of simultaneous users increases. Giladi et al. We measure computational complexity as the average time to perform the required computations of a task.

W e measure data volume complexity as the time required to transfer the data. W e measure q uery complexity as the average time to execute a query.

Ariav and Ginzberg defined decision pace as the natural rhythm f or m aking a d ecision o r acco mplishing a t ask. W e o perationalize t his ch aracteristic as t he number of decisions a user makes in a period of time, labeled as DMR — decision-making rates. Our effort aims t o unde rstand how t hese di mensions affect ea ch o ther an d how t hey af fect M D2S2 architecture and ultimately impact system performance.

Each o f t hese components i s co mposed o f se veral functional c omponents. F or e xample, t he Model Management component Figure 2 is built of four sub-components: Command Processor, w hich r eceives r equests f rom the Dialogue component; Model Execution component which r etrieves an d ex ecutes the m odel; MBMS wh ich st ores m odels and a ttains t hem f or t he m odel execution component; and t he DB i nterface w hich retrieve required data for the model.

This r esearch f ollows t his d efinition and studies d ifferent d istribution p ossibilities of t hese co mponents. I n p rinciple, each distribution m odel of t hese components basically al locates t he various g eneric components of a D SS t o a c omputational component o f t he n etwork, ei ther a cl ient or o ne of t he p ossible servers.

E ach Figure 2. Model Management Component. We study a range of distribution models, from a “Fat Client” a rchitecture, w here a ll c omponents a re lo cated in th e c lient c omputer, to a ” Thin C lient” architecture, where o nly the d ialogue components a re l ocated i n t he c lient s ide Sommerville, , Ch.

Performance i ssues ar e a major p art o f t he d istributed sy stem d esign an d m anagement p rocess. T he performance of the system relies on the usage of its resources. I n M D2S2, m illions o f u sers cr eate h igh l evels o f d emand o n t he s ystem an d consequently affect performance. We seek to model the behaviour of a system with respect this measure. Performance may be analyzed using real environment experiments, simulation, analytical solution, or numerical so lution.

PEPA, d eveloped b y H illston i n t he 1 s Hillston 1 , d escribes a sy stem as a co llection o f interacting co mponents agents. E ach co mponent can ex ecute a set o f act ions, wh ich ” move” t he component between states. Different components can engage in actions. It can be regarded as a high-level model specification language for low- level st ochastic models, w hich m akes i t su itable f or ex tracting p erformance measures e.

The P EPA l anguage i s s upported b y a nu mber o f di fferent t ools of fering a va riety of a nalysis techniques. T his e xtensive s upport ha s motivated a growing n umber o f sy stem p erformance r esearchers t o u se P EPA. S pecifically, we u se the PEPA Ec lipse p lug-in to ol, w hich is a n a ddition to t he Ec lipse in tegrated d evelopment environment. The massively d istributed sy stems t hat ar e t he subject o f t his r esearch ar e t oo l arge t o b e r eadily analyzed using Markovian techniques which are suitable for up t o 1,, different states.

PEPA models have been developed for real applications and have amply demonstrated its validity. PEPA is extensively described in many papers and articles Clark et al.

The model is adjusted f or each i nvestigated ar chitecture distribution ch oice an d i s set wi th su itable p arameters underlying Markov process rates that represent the investigated decision situation and platform.

Our dependent variable is performance, which is measured by response time, as defined earlier. Research Model.

Each of these architectures types is captured in a corresponding PEPA model. For each of these types we ex amine nine v ariations determined b y ch anging p arameters i n t he P EPA model : f our sy stem sizes, measured b y t he n umber o f en d-decision-points i. This figure exemplifies the transitions between different states of the model management component. S means that the system can move from state P to state Q or to state S..

For each of the decision situation dimensions, we consider six possible value levels, and subsequently modify the evaluation model according to each set of possible values. PEPA enables us to investigate parametrical variation quite easily.

Each dimension is expressed by a rate or a set of rates in the PEPA model. Thus, we can study the effect of each dimension by changing the corresponding rates and measuring the results. As t he co mputational co mplexity r ises, f ewer computations can occur per second and thus we run our model with decreasing values of this rate. Changing the number of decision end-points i. Fig ure 5 p resents th e s ystem d efinition in PEPA , t hat is , th e relations of the system components in the Thin Client case.

Specifically, Figure 5 exemplifies a Thin Client configuration of two data management servers, two model management servers and users and t he act ions o n wh ich they co operate. I n o rder t o ch ange t he number o f u sers o r ser vers we j ust change the corresponding numbers. Using sensitivity analysis, we intend to study how changing each dimension value affects performance for each architecture configuration.

Consequently, we will be able to determine the best architecture in terms of performance, for each set of dimension values. After having completed the analysis of the generic MD2S2 case, we also intend to confirm our model through i ts a pplication i n an e xternally de fined t elevision c ontent c onsumption s cenario, us ing r eal data supplied by Orca Interactive. Figure 4. Model Management component of Thin Client configuration. Each row describe a transition between one state left side of the equation of the component to another right side of the equation.

Components relations definition of Thin Client configuration. The numbers in the square brackets represent the number of instances of each component.

In the angle brackets are the activities on which each two components cooperate. We investigated three types of DSS ar chitectures, each with different numbers of servers and d ifferent n umber o f u sers – creating a t otal o f 3 6 d ifferent ar chitectures. S ix l evels o f computation complexities were investigated for each architecture.

The r esults sh ow t hat f or s mall n umber o f u sers, ar chitecture p lays a l ittle r ole i n d etermining the response time. As the number of users increases architecture has larger effect on response time. On the other h and, as the n umber o f u sers i ncreases, t he co mputational co mplexity st ops af fecting t he response t ime.

T hat i s, t he co mputational co mplexity h as a cl ear ef fect o n r esponse t ime w ith sm all number of users, but has no effect when we reach a point where the number of users is too big. The results show that for different number o f u sers, i n gi ven computational c omplexity values, t he p referable ar chitecture changes.

A pparently the fact that the system is distributed between a si gnificantly large number of users has strong implications on the effect of computational complexities.

H owever, our u nderstanding — and c orresponding i ntuitions — of sy stem performance at t hese scales ar e o nly p reliminary. T his st udy ai med at t he d evelopment o f a so lid approach and modelling platform for the study of a particular type of information system application, the MD2S2. This research uniquely connects distribution issues to the fundamental attributes of decision support.

In its form it spans all the way from observable characteristics of a d ecision situation — “things” like decision support complexity that a DSS Analyst can observe directly during the system analysis stage — to “bottom line” performance characteristics of a planned DSS.

We would like to submit that this is a desired framework in any IS implementation, let alone in challenging MD2S2 implementations. In t his r esearch w e ha ve s tarted t he de velopment of a t ool f or M D2S2 de sign. T his t ool c onsiders decision situation attributes, platform characteristics, and various components distribution alternatives, and measures sy stem r esponse time.

Using this measuring tool we are able to evaluate and co mpare different distribution alternatives of decision support components in various decision situations, where not only decision interdependence changes, but other decision situation attributes can change as w ell, for example, decision support complexity, decision making rate and number of decision makers.

Ariav, G. Ginzberg, M. Advanced draft in preparation , Arnott, D. Bergstra, J. Fixed point semantics in process algebras. Amsterdam, Mathematical Centre, Clark A. Stochastic Process Algebras. Coulouris, G. Degemmis, M. Ding, J. Giladi, R. Heeter, C. Hillston, J. Hitlin, P. Hoare, C. Communicating Sequential Processes, Prentice-Hall, Milner, R. Communication and concurrency, Prentice Hall, Nunamaker Jr, J.

Group decision support systems GDSS : present and future. Rajput, W. E-Commerce systems architecture and applications. Norwood, Artech House, Schafer, J. Recommender systems in e-commerce. Proceedings of the 1st ACM conference on Electronic commerce, Simon, H.

Sommerville, I. Software Engineering. Boston, MA, Addison-Wesley. Sprague Jr, R. Walls, J. This proposal presents the evidence to the contrary, evidence showing that CMC is rich and capable of conveying complex and subtle messages. The proposal presents the analogy between the verbal and nonverbal components of traditional spoken communication, and suggests that CMC too has both verbal and nonverbal components. It then focuses on the nonverbal component CMC cues , delineates two categories of CMC cues, and presents the past as well as ongoing research of the author.

Keywords: computer-mediated communication, cues, nonverbal. It is transforming the way we live, and consequently, it is also transforming the way researchers can study the human mind, human communication, organizations, and society. CMC permeates every facet of everyday life, and since most of this digital communication can be captured and analyzed, researchers have a new window into the human mind, into the way people and organizations communicate, and into social interactions.

This window is afforded by the constant creation of a stream of digital data created through Information Systems.

These data augment the analog and mostly ephemeral behavioral and social information that the social and behavioral sciences traditionally used as a source for scientific inquiry. The digital data can be easily recorded, often in large quantities and unobtrusively, and be analyzed both qualitatively and quantitatively. Assuming these data are collected ethically, they can be used to readdress classical research questions using much larger datasets and modern analysis tools, as well as to tackle novel research questions.

Despite this unwelcoming reception, text-based CMC media are constantly at the forefront of innovation, and are the most popular media for both organizational and personal use. From e-mail to Twitter, from SMS to Facebook status updates, simple text messages are used to convey complex messages, to carry out delicate tasks, and to mediate every category of social and organizational task.

The research of text-only communication described here tries to explore the mechanisms by which simple text messages can convey the richness and the social cues that enable them to carry out these tasks.

The study of this richness is informed by the study of traditional spoken communication. Work by others on the verbal content of CMC messages showed how specific choices of words can influence and be influenced by variables such as trust, personality or mood e. The work described here focuses on the non-verbal content of text-based CMC messages. These characteristics of CMC cues are reminiscent of the characteristics of nonverbal cues in traditional communication. The author and collaborators use the term CMC cues analogically to traditional nonverbal cues, to describe those modifications of a CMC message that, within a socially shared coding system, modify the meaning of the message while preserving the words of the message and their sequence.

Chronemic cues are time related messages. Nevertheless, many more cues have been identified in text- based CMC: all uppercase spelling, asterisks, punctuation marks, repeated punctuation marks, spacing of letters and of words, repetitions of letters, typos, spelling mistakes, and more Blackman, ; Carey, ; Spitzer, All of these cues modify the meaning of the message without altering the verbal content, and thus meet the definition of CMC cues.

Several studies showed the impact of these cues e. The goal of this research is to reach an in-depth understanding of their role in online communication, and the mechanisms by which they operate. Future research will extend this study to understand chronemic dynamics in other synchronous and asynchronous environments. Future research will extend this study to experimentally study additional visual cues in various text-based CMC media.

A naturalistic study of computer-mediated communication: emergent communication patterns in online electronic messaging systems. Burgoon, J. Nonverbal signals. Daly Eds. Thousand Oaks, CA: Sage. Carey, J. Paralanguage in computer mediated communication. Paper presented at the Proceedings of the 18th annual meeting on Association for Computational Linguistics, Philadelphia, Pennsylvania.

Daft, R. Management Science, 32 5 , Gill, A. Rating e-mail personality at zero acquaintance. Personality and Individual Differences, 40 3 , Kalman, Y. Email chronemics: unobtrusive profiling of response times. Online pauses and silence: Chronemic expectancy violations in written computer-mediated communication. Communication Research. Pauses and response latencies: A chronemic analysis of asynchronous CMC.

Journal of computer-mediated communication, 12 1 , Lea, M. Paralanguage and social perception in computer-mediated communication. Spitzer, M. Writing style in computer conferences. Paper presented at the IEEE transactions on professional communication.

Sproull, L. Reducing social context cues: electronic mail in organizational communication. Management Science, 32 11 , Walther, J. Patterson Eds. Social Science Computer Review, 19 3 , Nonverbal cues in computer-mediated communication, and the effect of chronemics on relational communication. Journal of Organizational Computing, 5, It is attempted to explore how new IS environments create new challenges for marketing and understanding online consumer behavior.

The expected final outcome will be twofold. Second, to provide evidence about how various types of store layout affect the shopping process. Simultaneously, they can talk with their friends socialization or find new friends, play electronic games entertainment , build houses interior and exterior decoration , develop furniture, buy and sell both virtual and real products e-commerce and numerous others activities.

There are more than a hundred of VWs at the time of crafting the present research. Web retailing had been the descendant of brick- and- mortar stores and internet became the vaulting horse for other retailing channels such as mobile-commerce and TV- commerce. Virtual reality retailing VRR is growing rapidly and will account for a substantial proportion of retail sales in the future.

Along these lines Vrechopoulos et al. Mazursky and Vinitzky stated that highly vivid interfaces such as 3D virtual stores provide motives, emotions, meanings and communication which are represented objectively. Along these lines Papadopoulou stated that the use of virtual reality for online shopping environments provides a superior customer experience in comparison with conventional Web1.

Elaborating on these research questions as provided through the literature review and the initial exploratory study desk research the following generic research hypothesis could be formulated: H1: Virtual Reality Retailing Store Atmosphere VRSSA affects Consumer Behaviour. Consumer Survey 2. Steps of methodology Specifically, the first step we are going to follow is to define a broad statement of the general problem and identification of the specific components as far as virtual reality retailing store atmosphere is concerned.

Afterwards, we are going to refine statements of the specific components of the problem, and in case that is essential we may have to break down some components of the statements into subcomponents or research questions.

The next step we are going to follow is to design a framework detailing the necessary procedures for conducting the marketing research problem research questions, objectives and hypotheses. In order to test the specific hypothesis and examine specific relationships we are going to follow conclusive — causal research by implementing first the laboratory experiment design, sample design and data collection instrument.

As far as the experiment design is concerned, first we are going to make a virtual store in virtual world in order to test subjects and how these subjects are to be divided in homogenous subsamples, to manipulate and measure specific independent variables or treatments.

The following step would be data analysis and interpretation, using factor analysis or structural equation modeling and the final step conclusions, implications and guidelines for further research. There has been extensive literature, examining the role of store atmosphere in conventional and web retailing shopping environment. There has been extensive research about the three well known types of store layout which are grid, racetrack and free-form e.

Vrechopoulos et al. However, due to the ability of flying throughout a shop and can easily watch all, or most of the available products or services, without being necessary to fool around, the store layout may be of little importance.

Probably, the types of store layout need to shake down in virtual reality era in order to be more discreet. Similarly, there is need to emphasize more on characteristics such as telepresense, vividness, interaction and virtual object touch.

In sum, there should be considerable research on all the cues that potentially could affect significant consumer behaviour variables e. References Browne, Glenn J. Childers, T. Hedonic and utilitarian motivations for online retail shopping behaviour.

Journal of Retailing, 77 4 , Constantinides, E. Internet Research, 14 2 , Dailey, L. Journal of Business Research, 57 7 , p. Eroglu, S. Berman Eds , Retailing launching the new millennium. Griffith D. Krasonikolakis, I. Levy, M. Retailing Management. New York,: McGraw-Hill. Lewison, D. New York: Macmillan Publishing Company. Mazursky, D. What is Web 2. Applying virtual reality for trust-building ecommerce environments. Virtual Reality 11 2 , — II and Gilmore, J.

Postrel, V. The Substance of Style. HarperCollins, New York. Shin, H. Understanding purchasing behaviors in a virtual economy: Consumer behavior involving virtual currency in Web 2. Interacting with Computers, 20, Thompson, S. Attitudes toward Online Shopping and the Internet. Vrechopoulos, A. Virtual Store Atmosphere in Internet Retailing. In Klein, S. Nevertheless t he i mplementation o f su ch syst ems h as b een p roved t o be difficult, demanding m any r esources w ith l ong dur ation.

The f ollowing co mprehensive r esearch is based t he e xamination of ten known i nformation syst ems mo dels: 1 t echnology a cceptance mo del TAM , 2 task technology fit model TTF , 3 a model combining the technology acceptance model and the task technology fit model, 4 Delone and Mclean IS success model, 5 computer self efficacy model CSE , 6 D iffusion of I nnovation m odel DOI , 7 a m odel c ombining t he t echnology acceptance m odel, t ask t echnology f it m odel a nd co mputer sel f e fficacy mo del, 8 u nified t heory o f acceptance and use of technology UTAUT , 9 conceptual ERP model, 10 TIMES model.

This research aims to: 1 review the literature on information systems acceptance models generated by two different perspectives: individual e. Technology acceptance model and non individual e. Task Technology Fit characteristics, 2 empirically compare ten models and their extensions in the field of ERP systems, 3 examine the relationships among fundamental constructors independent and dependant variables and seek for patterns in order to understand the relationship between them, 4 examine the affect of moderators on these relationships such as: age, gender, education, experience, technological or business or ientation, l evel of management and v oluntariness of us e, 5 develop, formulate and emp irically exa mine a m odel t hat i ntegrates elements a cross t he ten models and be st describes the acceptance of enterprise resource planning systems.

T herefore, many large an d mid-sized or ganizations r un a ll o f their major functional a nd pr ocess ope rations using e nterprise resource planning ERP s ystems Peslak The hi gh adoption a nd pe netration of in formation s ystems in g eneral a nd ER P in particular dur ing the l ast d ecade h ave e mphasized t he i mportance o f its acceptance evaluation.

The research in the field of Information systems have e merged two main research streams. The first stream focuses and examines the individual psychological characteristics that influence the technology acceptance.

The second stream focuses and examines the technology acceptance as i n overall due to the t echnological an d o rganizational ch aracteristics. Each o f t hese st ream make an i mportant an d unique contributions to the literature on us er technology acceptance and implementation success and this research first of all aims to present the streams and correlated theories and models, comparing and synthesizing them. The original technology accep tance model TAM is ba sed on pr inciples adopted from Fishbein a nd A jzen’s t heory of r easoned act ion TRA model an d i s a sp ecific adjustment to the field of information systems specifying the causal relationships between perceived usefulness, p erceived ease of us e, attitude t oward us ing, i ntention t o us e and a ctual us age be havior.

The TAM model is based on the perception that technology acceptance determined by the level of the individual acceptance and therefore was developed as a mean to measure user usage. T AM i s ba sed on behavioral elements, ass uming that so meone’s intention to use an information system is free of limitations. In practice constraints such as limited ability, time, environmental or organizational limits, and unconscious habits will limit the freedom to act. These models pr ovided a theoretical framework for evaluating t he e xplanatory pow er of the interrelated factors regarding t he pr ocess of i nformation s ystem implementation a nd i ts i mplications on t he pe rformance, of t he i ndividual a nd i n t he overall.

These models of fer di fferent, though overlapping perspectives on ut ilization be havior. The TAM model focuses o n p erceived ea se o f u se and perceived usefulness as p redictors of attitudes toward and intention of using a particular IS. The Delone an d M cLean IS success model ex tends t he sco pe wi th s ystem characteristics o n o ne h and predicting intention of using a particular IS and user satisfaction and gained benefits on the other hand.

TTF focuses on the match between user task needs and the available functionality of the IT. The literature is f ull w ith researches t hat e xamine t he e xplanatory pow er of e ach model a lone t o a n information system implementation a nd ut ilization be havior. The incentive for the integration of models arose by t he need to provide significant i mprovement of the explanatory pow er of i nformation s ystem utilization be havior over e ither model alone.

This l eads t o t he second g oal; n amely an analysis o f what ar e the patterns of fundamental constructors dependant and independent variables based on the relationships among them. Both goals a re examined al so in l ight of moderators affect such as: age, g ender, education, ex perience, technological or business orientation, level of management and voluntariness of use on such relationships.

For this purpose, a survey of ERP models is conducted these days with the appropriate stakeholders in organizations with a broad experience of implementing ERP systems. Data sample 3B Ten different que stionnaires, each f or each model, were mailed t o approximately potential respondents, classified into ten groups, where responses were returned with a net response rate of Interviews with the respondents were conducted from January to May , based on a mailed structured i nterview method w hich e nsured that the answers could be r eliably ag gregated an d t hat comparisons could be made with confidence between sample groups.

Each survey for each model includes several constructors for which several statements are presented. First, t he respondents wer e ask ed t o f ulfill g eneral ch aracteristics su ch as: age, ge nder, e ducation, experience, technological or business orientation, level of management and voluntariness of use.

Next, the r espondents we re a sked t o asses s t o wh at ex tent t hey ag ree wi th t he st atements p resented. Th e questionnaire was b ased o n a 7-point L ikert scal e plus ” irrelelevant”.

T he o verall asse ssments obtained in this study are presented in Table 1. Overall Number of Number of No. Data analysis strategy 4B First, reliability and validity tests were d esigned to empirically evaluate, validate a nd pr opose an enhanced survey that best describes each model. The reliability test determines the extent to which the measurements resulting from a test are the result of characteristics of the features being measured.

In other words, it assesses the degree to which test scores for a g roup of test takers are consistent over repeated ap plications o f a measurement p rocedure and h ence a re d ependable an d r epeatable f or an individual test taker. Measurement validity refers to the extent to which the questionnaire represents the overall dimensions of the measured items.

Although a test is proven to be reliable, its’ results may not b e v alid si nce st able results d o n ot n ecessarily share v alidity. T hus, the r esearch measurement instrument was examined for completeness and clarity in a series of interviews with two experienced ERP consultants.

Based on their comments, a second examination was conducted after a pilot test, in which 10 different respondents, for each model, were interviewed and asked to assess the meaning of each statement in the model. After validation by the preliminary interviews and the pilot survey, it was administered to the respondents. Next, we analyze whether we can reduce the number of statements in each model.

Such an analysis helps to shorten survey filling duration since some of the surveys include large number of statements assessed by the respondents without damaging the survey or lowering its reliability and validity. Furthermore, s everal models s hare t he same i ndependent c onstructors, although s ometime named i n different t erms, i ncluding t he c onstruct na med “Intention t o use”.

The tem e xamined models share some independent constructors and “Intention to use” constructor. Table 2 pr esents the shared i ndependent c onstructors f or e ach s et of models, t he d ependent v ariable a nd t he nu mber of respondents assessing their statements.

Moreover, the au thors we examine the af fect o n t hese relationships by moderators s uch a s: age, ge nder, e ducation, e xperience, t echnological or bus iness orientation, level of management and voluntariness of use. Based on the results of these analyses the authors will formulate and empirically examine a model that integrates the r esearch i nsights an d b est d escribes the acc eptance o f en terprise resource planning systems.

References Calisir, F. The relation of interface usability characteristics, perceived usefulness, and perceived ease of use to end-user satisfaction with enterprise resource planning ERP systems.

Computers in Human Behavior, 20 4 , pp. Compeau, D. A a. MIS Quarterly 19 2 , pp. Davis, F. MIS Quarterly, 13 3 , pp. International Journal of Man-Machine Studies, 38 3 , pp. If you only take off, you’ll surely run into some rough hurdles, but if you take off and head for the clouds, the launch pad won’t be a problem. If you want a smoother trip, try to engage the sidewinder, you can use this feature to make it a smoothest of trips.

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