Mit mérünk és hogyan? – A vállalati teljesítménymérés szemléletmódja és eszköztára a döntéstámogatás tükrében = Performance measurement serving decision making – approach and practice of Hungarian firms

Tanulmányunkban a hazai vállalatok teljesítménymérési és teljesítménymenedzsment gyakorlatát vizsgáljuk a Versenyben a világgal kutatási program 2009. évi felmérése adatainak felhasználásával. Célunk a döntéstámogatás hátterének vizsgálata: a vállalatok teljesítménymérési gyakorlatának jellemzése, konzisztenciájának értékelése, vizsgálva a korábbi (1996, 1999 és 2004 évi hasonló) kutatásaink során megfigyelt tendenciák további alakulását is. A vállalati teljesítménymérés gyakorlatát, a vállalatvezetők által fontosnak/hasznosnak tartott, illetve rendszeresen használt információforrásokat, teljesítménymutatókat, elemzési eszközöket a korábbi kutatásainkhoz kialakított elemzési keret (orientáció, egyensúly, konzisztencia, támogató szerep) felhasználásával értékeltük. Az információs rendszer különböző tevékenységeket támogató szerepének az értékelése során a különböző területekért felelős vezetők véleményét is összevetettük, s különböző vállalati jellemzők (vállalatméret, tulajdonosok típusa, fő tevékenység stb.) sajátosságait is vizsgáltuk. ___________ The paper analyses the performance measurement and performance management practice of Hungarian companies, based on the data of the Competitiveness research program (2009). Our goal was to evaluate the practice from the point of view of decision support, based on our previous framework, evaluating the orientation, the balance, the consistency and the supporting role of the performance measurement practice.

A tudományterületi sajátosságok következményei a kutatásban, az oktatásban és a felsőoktatási intézmények vezetésében (The consequences of disciplinary difference on teaching, research and institutional management of higher education)

A szerző tanulmányában vizsgálja a tudományterületek különbségeinek következményeit az oktatásban, a kutatásban és a felsőoktatási intézmények irányításában. A tanulmány első felében a tudományterületek episztemológiai és közösségi különbségeit tárgyalja Becher és Trowler (2001) népszerű kategorizálása alapján. A tanulmány második felében a tudományszerveződésre, a publikációs gyakorlatra, a doktori képzésre, az oktatás céljára és formáira, a diákok tudásképére, valamint a menedzsmentfelfogásra gyakorolt hatást mutatja be. A konklúzióban egyrészt a minőség- és teljesítményértékelési rendszerek differenciált megközelítésének szükségességére hívja fel a figyelmet, másrészt arra, hogy a Bologna-rendszer és az élethosszig tartó tanulás erősödésének következményeként egy-egy mesterszakon nemcsak a hallgatóság háttere, előismerete lesz sokszínű, hanem a tudásról alkotott képe is. Ez újfajta oktatói szerepeket és oktatási megközelítéseket tesz szükségessé. ________ In the article the consequences of disciplinary difference on teaching, research and institutional management is examined. In the first part of the paper the epistemological and sociological differences of scientific disciplines are summarized based on the popular typology of Becher and Trowler (2001). In the second part the influence of differences on organisation of sciences, publication practices, PhD studies, the goal and form of teaching, students’ conception of knowledge as well as on the understanding on management and leadership are discussed. In the conclusions, attention is drawn on the necessity of differentiation in quality and performance management processes. Another conclusion is that new roles for teachers and approaches for teaching is required if students’ conception of knowledge become more heterogeneous which is the result of result of life long learning and Bologna-reform.

Spatial-temporal mining of archived automatic vehicle location (AVL) data for transit service planning applications

An Automatic Vehicle Location (AVL) system is a computer-based vehicle tracking system that is capable of determining a vehicle's location in real time. As a major technology of the Advanced Public Transportation System (APTS), AVL systems have been widely deployed by transit agencies for purposes such as real-time operation monitoring, computer-aided dispatching, and arrival time prediction. AVL systems make a large amount of transit performance data available that are valuable for transit performance management and planning purposes. However, the difficulties of extracting useful information from the huge spatial-temporal database have hindered off-line applications of the AVL data. ^ In this study, a data mining process, including data integration, cluster analysis, and multiple regression, is proposed. The AVL-generated data are first integrated into a Geographic Information System (GIS) platform. The model-based cluster method is employed to investigate the spatial and temporal patterns of transit travel speeds, which may be easily translated into travel time. The transit speed variations along the route segments are identified. Transit service periods such as morning peak, mid-day, afternoon peak, and evening periods are determined based on analyses of transit travel speed variations for different times of day. The seasonal patterns of transit performance are investigated by using the analysis of variance (ANOVA). Travel speed models based on the clustered time-of-day intervals are developed using important factors identified as having significant effects on speed for different time-of-day periods. ^ It has been found that transit performance varied from different seasons and different time-of-day periods. The geographic location of a transit route segment also plays a role in the variation of the transit performance. The results of this research indicate that advanced data mining techniques have good potential in providing automated techniques of assisting transit agencies in service planning, scheduling, and operations control. ^

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Systems Engineering Perspective of the Cruise Industry

The maturation of the cruise industry has led to increased competition which demands more efficient operations. Systems engineering, a discipline that studies complex organizations of material, people, and information, is traditionally only applied in the manufacturing sector; however, it can make significant contributions to service industries such as the cruise industry. The author describes this type of engineering, explores how it can be applied to the cruise industry, and presents two case studies demonstrating applications to the cruise industry luggage delivery process and the information technology help desk process. The results show that this approach can make the processes more productive and enhance profitability for the cruise lines.

Yield Management: Where We've Been, Where We Are, Where We're Going

The author describes yield management and the technology used to implement yield management in hotels, issues in usefulness, and legal issues concerning the use of yield management. A look into the future is provided, along with a critique of what further research may be needed in order to raise the level of usefulness of yield management systems in the hotel industry to that found in the airlines.

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

Implementação da infra-estrutura e protocolo de comunicação para tratamento e disponibilização de dados do projeto ePoste

The study consists in the structure elaboration and the ePoste project communicationprotocol, which is a system of monitoring by wireless communication with the aim tosensor and act with one or many public lighting points, and also the treatment of data todetect the activity in the sensors located on the posts. Previously the communication withsensors were made in locus and when data collect was necessary or the performance inthe devices, the operator had to move until the net place. Accordingly, the purpose has theconcern to increase the system dynamic, achieving the possible integrations with the systemsalready available to the lighting management. The same technology of communicationbetween the sensors are considerate, using the ZigBee protocol running on the meshnet, the communication with this net is made be internet through a gateway by GPRS,device with two basics functionalities, which bypass for data and the firmware update inthe sensor. This functionality test of data management is being tested; integrate way withlocal net of sensors and the internet data server operates. The protocol developed, besidesincorporating the ZigBee, that it is formation of framework in lower level, where definesbeginning, size and errors check, as well as the communication of sensor with the concentrator,which it is encapsulated in ZigBee; also the protocol of upper level, where thatit is the names, it was developed a platform of service to meet GET and POST requestsbeneath HTTP protocol, this service is implemented in the data server, which availablethe communication with the clients systems, in case, lighting management systems.

Progettazione e Sviluppo di un Sistema di Proactive Alerting all'interno di una piattaforma di Big Data Analytics

Il lavoro presentato in questo elaborato tratterà lo sviluppo di un sistema di alerting che consenta di monitorare proattivamente una o più sorgenti dati aziendali, segnalando le eventuali condizioni di irregolarità rilevate; questo verrà incluso all'interno di sistemi già esistenti dedicati all'analisi dei dati e alla pianificazione, ovvero i cosiddetti Decision Support Systems. Un sistema di supporto alle decisioni è in grado di fornire chiare informazioni per tutta la gestione dell'impresa, misurandone le performance e fornendo proiezioni sugli andamenti futuri. Questi sistemi vengono catalogati all'interno del più ampio ambito della Business Intelligence, che sottintende l'insieme di metodologie in grado di trasformare i dati di business in informazioni utili al processo decisionale. L'intero lavoro di tesi è stato svolto durante un periodo di tirocinio svolto presso Iconsulting S.p.A., IT System Integrator bolognese specializzato principalmente nello sviluppo di progetti di Business Intelligence, Enterprise Data Warehouse e Corporate Performance Management. Il software che verrà illustrato in questo elaborato è stato realizzato per essere collocato all'interno di un contesto più ampio, per rispondere ai requisiti di un cliente multinazionale leader nel settore della telefonia mobile e fissa.

Digital anthropology and educational eGames: learning through behavioural patterns in digital, game-based contexts

The selected publications are focused on the relations between users, eGames and the educational context, and how they interact together, so that both learning and user performance are improved through feedback provision. A key part of this analysis is the identification of behavioural, anthropological patterns, so that users can be clustered based on their actions, and the steps taken in the system (e.g. social network, online community, or virtual campus). In doing so, we can analyse large data sets of information made by a broad user sample,which will provide more accurate statistical reports and readings. Furthermore, this research is focused on how users can be clustered based on individual and group behaviour, so that a personalized support through feedback is provided, and the personal learning process is improved as well as the group interaction. We take inputs from every person and from the group they belong to, cluster the contributions, find behavioural patterns and provide personalized feedback to the individual and the group, based on personal and group findings. And we do all this in the context of educational games integrated in learning communities and learning management systems. To carry out this research we design a set of research questions along the 10-year published work presented in this thesis. We ask if the users can be clustered together based on the inputs provided by them and their groups; if and how these data are useful to improve the learner performance and the group interaction; if and how feedback becomes a useful tool for such pedagogical goal; if and how eGames become a powerful context to deploy the pedagogical methodology and the various research methods and activities that make use of that feedback to encourage learning and interaction; if and how a game design and a learning design must be defined and implemented to achieve these objectives, and to facilitate the productive authoring and integration of eGames in pedagogical contexts and frameworks. We conclude that educational games are a resourceful tool to provide a user experience towards a better personalized learning performance and an enhance group interaction along the way. To do so, eGames, while integrated in an educational context, must follow a specific set of user and technical requirements, so that the playful context supports the pedagogical model underneath. We also conclude that, while playing, users can be clustered based on their personal behaviour and interaction with others, thanks to the pattern identification. Based on this information, a set of recommendations are provided Digital Anthropology and educational eGames 6 /216 to the user and the group in the form of personalized feedback, timely managed for an optimum impact on learning performance and group interaction level. In this research, Digital Anthropology is introduced as a concept at a late stage to provide a backbone across various academic fields including: Social Science, Cognitive Science, Behavioural Science, Educational games and, of course, Technology-enhance learning. Although just recently described as an evolution of traditional anthropology, this approach to digital behaviour and social structure facilitates the understanding amongst fields and a comprehensive view towards a combined approach. This research takes forward the already existing work and published research onusers and eGames for learning, and turns the focus onto the next step — the clustering of users based on their behaviour and offering proper, personalized feedback to the user based on that clustering, rather than just on isolated inputs from every user. Indeed, this pattern recognition in the described context of eGames in educational contexts, and towards the presented aim of personalized counselling to the user and the group through feedback, is something that has not been accomplished before.

Reputation performance: a portfolio selection approach

This paper introduces a normative view on corporate reputation strategic management. Reputation performance is conceptualised as the outcome of complex processes and social interactions and the lack of a holistic reputation performance management framework is identified. In an attempt to fill this gap, a portfolio-based approach is put forward. Drawing on the foundations of modern portfolio theory we create a portfolio-based reputation management algorithmic model where reputation components and priorities are weighted by decision makers and shape organisational change in an attempt to formulate a corporate reputation strategy. The rationale of this paper is based on the foundational consideration of organisations as choosing he optimal strategy by seeking to maximise their reputation performance while maintaining organisational stability and minimising organisational risk.

Reputation Performance: a portfolio selection approach

This paper introduces a normative view on corporate reputation management; an algorithmic model for reputation-driven strategic decision making is proposed and corporate reputation is conceptualized as influenced by a selection among organizational priorities. A portfolio-based approach is put forward; we draw on the foundations of portfolio theory and we create a portfolio-based reputation management model where reputation components and priorities are weighted by decision makers and shape organizational change in an attempt to formulate a corporate reputation strategy. The rationale of this paper is based on the foundational consideration of organizations as choosing the optimal strategy by seeking to maximize performance on corporate reputation capital while maintaining organizational stability and minimizing organizational risk.

Does Inappropriate Quality Control Demotivate Workers

The construction industry requires quality control and regulation of its contingent,unpredictable environment. However, taking too much control from workers candisempower and demotivate. In the 1970s Deci and Ryan developed selfdeterminationtheory which states that in order to be intrinsically motivated, threecomponents are necessary - competence, autonomy and relatedness. This study aimsto examine the way in which the three ‘nutriments’ for intrinsic motivation may beundermined by heavy-handed quality control. A critical literature review analysesconstruction, psychological and management research regarding the control andmotivation of workers, using self-determination theory as a framework. Initialfindings show that quality management systems do not always work as designed.Workers perceive that unnecessary, wasteful and tedious counter checking of theirwork implies that they are not fully trusted by management to work without oversight.Control of workers and pressure for continual improvement may lead to resistanceand deception. Controlling mechanisms can break the link between performance andsatisfaction, reducing motivation and paradoxically reducing the likelihood of thequality they intend to promote. This study will lead to a greater understanding ofcontrol and motivation, facilitating further research into improvements in theapplication of quality control to maintain employee motivation.

Social Skills Training in Flight Schools: A Proactive Tool for Management of Threats and Risks

Safety Management Systems in aviation generate training programs that develop skills needed to perform safety functions. The objective of this study is to show that, in groups, individuals need to have interpersonal skills and, in particular, ability to communicate with others, to listen, and to influence. It is for this reason that Social Skills Training is important in Aviation. Professionals trained in social skills are more likely to identify threats and risks caused by interpersonal situations, be assertive, and take appropriate action. As a contribution, this paper suggests a set of policies, procedures and practices for educating and training future professionals who will work in aviation safety.

An Empirical Analysis of Performance Impacts Resulting From Conversion to Franchise Operations

Franchising is an important form of organizational control. Possible benefits of franchising include its ability to reduce agency costs that increase with costly monitoring, and provide incentives for the use of local information by onsite managers. However, these benefits may come at a cost, as franchisees may reduce quality by choosing to free ride. While many studies have investigated the reasons for franchising, few studies have documented the impacts of franchising on unit level operating performance. Using time-series data from a number of lodging properties that were converted to franchisee control from company control, this study documents the performance impacts of franchising. The analysis reveals that conversion results in a modest decline in financial performance and an immediate sharp decline in quality.