Sosiaalisen median hyödyntäminen yhteiskunnallisessa markkinoinnissa

Yhteiskunnallinen markkinointi on markkinointia, jossa hyödynnetään kaupalliseen markkinointiin kehitettyjä tekniikoita, mutta jossa taloudellisen voiton sijaan tavoitellaan käyttäytymisen muutosta hyvinvoinnille edullisempaan suuntaan. Hyötyjinä ovat yksilö, yksilön lähipiiri tai yhteiskunta. Yhteiskunnallista markkinointia on arvosteltu kunnianhimottomuudesta ja sosiaalisen median käytön suhteen sen on nähty toimivan osittain vajavaisesti, johtuen muun muassa resurssien vähyydestä. Kuitenkin juuri interaktiiviset ja vuorovaikutusta lisäävät markkinointikeinot, kuten sosiaalinen media, lisäävät käyttäytymisen muutoksen mahdollisuutta. Tässä työssä tarkastellaan sosiaalisen median hyödyntämistä yhteiskunnallisessa markkinoinnissa. Työ alkaa yhteiskunnallisen markkinoinnin ja sosiaalisen median kirjallisuuskatsauksilla, joissa käsitellään muun muassa molempien määritelmiä, erityispiirteitä sekä niiden hyödyntämistä. Tutkielman empiirinen osuus toteutettiin hyödyntämällä triangulaatiota. Tutkimuksen aineistonkeruumenetelminä käytettiin netnografiaa ja sähköpostikyselyä. Kohdeorganisaatioina tutkielmalle toimi kolme yhteiskunnallista markkinointia toteuttavaa organisaatiota: Veripalvelu, Pelastakaa Lapset ry sekä Suomen Punainen Risti, jonka osalta netnografinen aineistonkeruu rajoittui Hämeen piiriin. Netnografinen aineistonkeruu toteutettiin tarkastelemalla kohdeorganisaatioiden erikseen valittuja sosiaalisen median kanavia. Netnografian kautta saatujen tuloksien pohjalta muodostettiin kyselylle runko, jolla haastateltiin organisaatioiden edustajia. Kyselyn pääasiallisena tarkoituksena oli selvittää kohdeorganisaatioiden sosiaalisen median käyttöä ja sitä, vastaako organisaatioiden oma näkemys netnografian kautta saatua kuvaa kohderyhmän reagoinnista markkinointiin. Tutkielman tulosten myötä nousi näkemys siitä, että kohderyhmää sosiaalisessa mediassa aktivoi erityisesti kotimaisten, ajankohtaisten ja koskettavien aiheiden käsittely sekä yleinen organisaatioiden reagointinopeus. Kohderyhmä aktivoitui helposti myös kun kyseessä oli akuutti avuntarve. Kääntöpuolena organisaatioiden omien agendojen käsittely kärsi osittain kohderyhmän mielenkiintoa ylläpitäessä. Tämän tutkielman tulosten mukaan organisaatioiden näkemys kohderyhmän reagoinnista markkinointiin sosiaalisessa mediassa vastasi netnografian kautta saatuja tuloksia. Markkinointi sosiaalisessa mediassa tarjoaa organisaatiolle toimivan väylän käyttäytymiseen vaikuttamiseen, sillä kohderyhmä seuraa sosiaalisen median kanavia. Oikeanlaisen, mielenkiintoisen ja ajankohtaisen viestin myötä kohderyhmä aktivoituu.

Sustainable sourcing in global supply network: assessment of geographic challenges and management methods Case: Stora Enso

The purpose of this work was to describe and compare sourcing practices and challenges in different geographies, to discuss possible options to advance sustainability of global sourcing, and to provide examples to answer why sourcing driven by sustainability principles is so challenging to implement. The focus was on comparison between Europe & Asia & South-America from the perspective of sustainability adoption. By analyzing sourcing practices of the case company it was possible to describe main differences and challenges of each continent, available sourcing options, supplier relationships and ways to foster positive chance. In this qualitative case study gathered theoretical material was compared to extensive sourcing practices of case company in a vast supplier network. Sourcing specialist were interviewed and information provided by them analyzed in order to see how different research results and theories are reflecting reality and to find answers to proposed research questions.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Value co-creation in bio-based chemicals value network

The purpose of this Master’s thesis is to study value co-creation in emerging value network. The main objective is to examine how value is co-created in bio-based chemicals value network. The study provides insights to different actors’ perceived value in the value network and enlightens their motivations to commit to the collaborative partnerships with other actors. Empirical study shows that value co-creation is creation of mutual value for both parties of the relationship by combining their non-competing resources to achieve a common goal. Value co-creation happens in interactions, and trust, commitment and information sharing are essential prerequisites for value co-creation. Value co-creation is not only common value creation, but it is also value that emerges for each actor because of the co-operation with the other actor. Even though the case companies define value mainly in economic terms, the other value elements like value of the partnership, knowledge transfer and innovation are more important for value co-creation.

Modelling the colour difference by artificial neural network

This thesis work studies the modelling of the colour difference using artificial neural network. Multilayer percepton (MLP) network is proposed to model CIEDE2000 colour difference formula. MLP is applied to classify colour points in CIE xy chromaticity diagram. In this context, the evaluation was performed using Munsell colour data and MacAdam colour discrimination ellipses. Moreover, in CIE xy chromaticity diagram just noticeable differences (JND) of MacAdam ellipses centres are computed by CIEDE2000, to compare JND of CIEDE2000 and MacAdam ellipses. CIEDE2000 changes the orientation of blue areas in CIE xy chromaticity diagram toward neutral areas, but on the whole it does not totally agree with the MacAdam ellipses. The proposed MLP for both modelling CIEDE2000 and classifying colour points showed good accuracy and achieved acceptable results.

Controlling full penetration in MAG welding by the application of infrared thermography and neural network

In this study, an infrared thermography based sensor was studied with regard to usability and the accuracy of sensor data as a weld penetration signal in gas metal arc welding. The object of the study was to evaluate a specific sensor type which measures thermography from solidified weld surface. The purpose of the study was to provide expert data for developing a sensor system in adaptive metal active gas (MAG) welding. Welding experiments with considered process variables and recorded thermal profiles were saved to a database for further analysis. To perform the analysis within a reasonable amount of experiments, the process parameter variables were gradually altered by at least 10 %. Later, the effects of process variables on weld penetration and thermography itself were considered. SFS-EN ISO 5817 standard (2014) was applied for classifying the quality of the experiments. As a final step, a neural network was taught based on the experiments. The experiments show that the studied thermography sensor and the neural network can be used for controlling full penetration though they have minor limitations, which are presented in results and discussion. The results are consistent with previous studies and experiments found in the literature.

Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.