Brand Communication Strategy of E-commerce SMEs in Chinese Cosmetics Market

This study concentrates on how to develop a brand communication strategy for ecommerce SMEs in Chinese cosmetic market with new media channels. This study is a qualitative research. Data collection consists of primary data and secondary data. Primary data is from the case company’s websites, observation of benchmarked companies and observation of the case company. Secondary data will be collected from relevant websites and reliable databases. In order to explore the research questions, comparative benchmarking was conducted to develop brand communication strategy for case company April. The results of the study illustrate that e-commerce SMEs have to consider brand positioning strategy, brand awareness strategy, brand attitude strategy, brand media strategy and the brand benefits as well to develop brand communication strategy.

The Anchoring Bias of Investors and the 52-Week High Momentum Strategy in the OMX Helsinki Stock Exchange: Psychological Approach to Market Efficiency

The behavioural finance literature expects systematic and significant deviations from efficiency to persist in securities markets due to behavioural and cognitive biases of investors. These behavioural models attempt to explain the coexistence of intermediate-term momentum and long-term reversals in stock returns based on the systematic violations of rational behaviour of investors. The study investigates the anchoring bias of investors and the profitability of the 52-week momentum strategy (GH henceforward). The relatively highly volatile OMX Helsinki stock exchange is a suitable market for examining the momentum effect, since international investors tend to realise their positions first from the furthest security markets by the time of market turbulence. Empirical data is collected from Thomson Reuters Datastream and the OMX Nordic website. The objective of the study is to provide a throughout research by formulating a self-financing GH momentum portfolio. First, the seasonality of the strategy is examined by taking the January effect into account and researching abnormal returns in long-term. The results indicate that the GH strategy is subject to significantly negative revenues in January, but the strategy is not prone to reversals in long-term. Then the predictive proxies of momentum returns are investigated in terms of acquisition prices and 52-week high statistics as anchors. The results show that the acquisition prices do not have explanatory power over the GH strategy’s abnormal returns. Finally, the efficacy of the GH strategy is examined after taking transaction costs into account, finding that the robust abnormal returns remain statistically significant despite the transaction costs. As a conclusion, the relative distance between a stock’s current price and its 52-week high statistic explains the profits of momentum investing to a high degree. The results indicate that intermediateterm momentum and long-term reversals are separate phenomena. This presents a challenge to current behavioural theories, which model these aspects of stock returns as subsequent components of how securities markets respond to relevant information.

A simulation model of the optimal maintenance strategy: conceptual agent-based approach

In this Master’s thesis agent-based modeling has been used to analyze maintenance strategy related phenomena. The main research question that has been answered was: what does the agent-based model made for this study tell us about how different maintenance strategy decisions affect profitability of equipment owners and maintenance service providers? Thus, the main outcome of this study is an analysis of how profitability can be increased in industrial maintenance context. To answer that question, first, a literature review of maintenance strategy, agent-based modeling and maintenance modeling and optimization was conducted. This review provided the basis for making the agent-based model. Making the model followed a standard simulation modeling procedure. With the simulation results from the agent-based model the research question was answered. Specifically, the results of the modeling and this study are: (1) optimizing the point in which a machine is maintained increases profitability for the owner of the machine and also the maintainer with certain conditions; (2) time-based pricing of maintenance services leads to a zero-sum game between the parties; (3) value-based pricing of maintenance services leads to a win-win game between the parties, if the owners of the machines share a substantial amount of their value to the maintainers; and (4) error in machine condition measurement is a critical parameter to optimizing maintenance strategy, and there is real systemic value in having more accurate machine condition measurement systems.

Price spike forecasting in a competitive day-ahead energy market

Electricity price forecasting has become an important area of research in the aftermath of the worldwide deregulation of the power industry that launched competitive electricity markets now embracing all market participants including generation and retail companies, transmission network providers, and market managers. Based on the needs of the market, a variety of approaches forecasting day-ahead electricity prices have been proposed over the last decades. However, most of the existing approaches are reasonably effective for normal range prices but disregard price spike events, which are caused by a number of complex factors and occur during periods of market stress. In the early research, price spikes were truncated before application of the forecasting model to reduce the influence of such observations on the estimation of the model parameters; otherwise, a very large forecast error would be generated on price spike occasions. Electricity price spikes, however, are significant for energy market participants to stay competitive in a market. Accurate price spike forecasting is important for generation companies to strategically bid into the market and to optimally manage their assets; for retailer companies, since they cannot pass the spikes onto final customers, and finally, for market managers to provide better management and planning for the energy market. This doctoral thesis aims at deriving a methodology able to accurately predict not only the day-ahead electricity prices within the normal range but also the price spikes. The Finnish day-ahead energy market of Nord Pool Spot is selected as the case market, and its structure is studied in detail. It is almost universally agreed in the forecasting literature that no single method is best in every situation. Since the real-world problems are often complex in nature, no single model is able to capture different patterns equally well. Therefore, a hybrid methodology that enhances the modeling capabilities appears to be a possibly productive strategy for practical use when electricity prices are predicted. The price forecasting methodology is proposed through a hybrid model applied to the price forecasting in the Finnish day-ahead energy market. The iterative search procedure employed within the methodology is developed to tune the model parameters and select the optimal input set of the explanatory variables. The numerical studies show that the proposed methodology has more accurate behavior than all other examined methods most recently applied to case studies of energy markets in different countries. The obtained results can be considered as providing extensive and useful information for participants of the day-ahead energy market, who have limited and uncertain information for price prediction to set up an optimal short-term operation portfolio. Although the focus of this work is primarily on the Finnish price area of Nord Pool Spot, given the result of this work, it is very likely that the same methodology will give good results when forecasting the prices on energy markets of other countries.

Green Marketing and its Role in Firm’s Strategy

Tässä kandidaatintutkielmassa tutkitaan vihreän markkinoinnin strategista roolia ja sen tuomaa lisäarvoa yritykselle. Tutkimuksessa selvitetään, mitä vihreä markkinointi on, mistä syistä yritykset harjoittavat sitä ja tuoko se lisäarvoa yritykselle. Case-yrityksinä tutkielmassa ovat UPM-Raflatac ja Lappeenrannan Energia Oy.

Supporting UK Repositories; a Cohesive Strategy

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Carrier selection and management : A case study on Neste Oil Retail

Service provider selection has been said to be a critical factor in the formation of supply chains. Through successful selection companies can attain competitive advantage, cost savings and more flexible operations. Service provider management is the next crucial step in outsourcing process after the selection has been made. Without proper management companies cannot be sure about the level of service they have bought and they may suffer from service provider's opportunistic behavior. In worst case scenario the buyer company may end up in locked-in situation in which it is totally dependent of the service provider. This thesis studies how the case company conducts its carrier selection process along with the criteria related to it. A model for the final selection is also provided. In addition, case company's carrier management procedures are reflected against recommendations from previous researches. The research was conducted as a qualitative case study on the principal company, Neste Oil Retail. A literature review was made on outsourcing, service provider selection and service provider management. On the basis of the literature review, this thesis ended up recommending Analytic hierarchy process as the preferred model for the carrier selection. Furthermore, Agency theory was seen to be a functional framework for carrier management in this study. Empirical part of this thesis was conducted in the case company by interviewing the key persons in the selection process, making observations and going through documentations related to the subject. According to the results from the study, both carrier selection process as well as carrier management were closely in line with suggestions from literature review. Analytic hierarchy process results revealed that the case company considers service quality as the most important criteria with financial situation and price of service following behind with almost identical weights with each other. Equipment and personnel was seen as the least important selection criterion. Regarding carrier management, the study resulted in the conclusion that the company should consider engaging more in carrier development and working towards beneficial and effective relationships. Otherwise, no major changes were recommended for the case company processes.

International Partnering strategy for e-learning provider in emerging economies: Evidence from East Africa learning institutions

International partnership has received growing interest in the literature during the past decades due to globalization, increased technological approaches and rapid changes in competitive environments. The study specifically determines the support provided by international partners on promotion of e-learning in East Africa, assess the motives of partner selection criteria, the determinants of selecting partners, partner models and partner competence of e-learning provider. The study also evaluates obstacles of e-learning partnering strategy in East Africa learning institutions. The research adopts a descriptive survey design. Target population involved East Africa learning institutions with a list of potential institutions generated from the Ministry of Higher Education database. Through a targeted reduction of the initial database, consisting of all learning institutions, both public and private, the study created a target sample base of 200 learning institutions. Structured questionnaires scheduled were used to collect primary data. Study findings showed the approach way East African communities in selecting their e-learning partners depend on international reputation of partners, partner with ability to negotiate with foreign governments, partner with international and local experiences, nationality of foreign partner and partners with local market knowledge.

Collaborative Customer Relationship Management in Business-to-Business Firm's Strategy

The objective of this thesis is to study the presence of collaborative customer relationship management in a firm’s strategy. In addition the thesis explains specific implementations of collaborative CRM, and CRM in general, by each case company. The sample consists of five Finnish business-to-business companies through applying multiple-case study method. The data is collected through face-to-face interviews with employees knowledgeable of the case company’s CRM processes. The qualitative data is analyzed through coding and shows that two out of five case companies have adopted and are using collaborative CRM in their strategy and operations. These case companies see collaborative CRM as an important driver for the company, through customer focus and market orientation. The rest of the case companies are either in the process of moving towards collaborative CRM or have given little consideration to it. The results show that collaborative CRM is in use, and that each company modifies it to meet their exact aspirations. The major challenge in the process is to fully grasp the importance of a shared vision that can translate into collaborative efforts in CRM and business strategy.

On advancing business intelligence in the electricity retail market

In recent decades, business intelligence (BI) has gained momentum in real-world practice. At the same time, business intelligence has evolved as an important research subject of Information Systems (IS) within the decision support domain. Today’s growing competitive pressure in business has led to increased needs for real-time analytics, i.e., so called real-time BI or operational BI. This is especially true with respect to the electricity production, transmission, distribution, and retail business since the law of physics determines that electricity as a commodity is nearly impossible to be stored economically, and therefore demand-supply needs to be constantly in balance. The current power sector is subject to complex changes, innovation opportunities, and technical and regulatory constraints. These range from low carbon transition, renewable energy sources (RES) development, market design to new technologies (e.g., smart metering, smart grids, electric vehicles, etc.), and new independent power producers (e.g., commercial buildings or households with rooftop solar panel installments, a.k.a. Distributed Generation). Among them, the ongoing deployment of Advanced Metering Infrastructure (AMI) has profound impacts on the electricity retail market. From the view point of BI research, the AMI is enabling real-time or near real-time analytics in the electricity retail business. Following Design Science Research (DSR) paradigm in the IS field, this research presents four aspects of BI for efficient pricing in a competitive electricity retail market: (i) visual data-mining based descriptive analytics, namely electricity consumption profiling, for pricing decision-making support; (ii) real-time BI enterprise architecture for enhancing management’s capacity on real-time decision-making; (iii) prescriptive analytics through agent-based modeling for price-responsive demand simulation; (iv) visual data-mining application for electricity distribution benchmarking. Even though this study is from the perspective of the European electricity industry, particularly focused on Finland and Estonia, the BI approaches investigated can: (i) provide managerial implications to support the utility’s pricing decision-making; (ii) add empirical knowledge to the landscape of BI research; (iii) be transferred to a wide body of practice in the power sector and BI research community.

Cooperative strategy in emerging markets - analysis of interfirm r&d cooperation and performance in Russian manufacturing companies

Emerging markets have come to play a significant role in the world, not only due to their strong economic growth but because they have been able to foster an increasing number of innovative high technology oriented firms. However, as the markets continue to change and develop, there remain many companies in emerging markets that struggle with their competitiveness and innovativeness. To improve competitive capabilities, many scholars have come to favor interfirm cooperation, which is perceived to help companies access new knowledge and complementary resources and, by so doing, enables them to catch up quickly with Western competitors. Regardless of numerous attempts by strategic management scholars, the research field remains very fragmented and lacks understanding on how and when interfirm cooperation contributes to firm performance and competiveness in emerging markets. Furthermore, the reasons why interfirm R&D sometimes succeeds but fails at other times frequently remain unidentified. This thesis combines the extant literature on competitive and cooperative strategy, dynamic capabilities, and R&D cooperation while studying interfirm R&D relationships in and between Russian manufacturing companies. Employing primary survey data, the thesis presents numerous novel findings regarding the effect of R&D cooperation and different types of R&D partner on firms’ exploration and exploitation performance. Utilizing a competitive strategy framework enables these effects to be explained in more detail, and especially why interfirm cooperation, regardless of its potential, has had a modest effect on the general competitiveness of emerging market firms. This thesis contributes especially to the strategic management literature and presents a more holistic perspective on the usefulness of cooperative strategy in emerging markets. It provides a framework through which it is possible to assess the potential impacts of different R&D cooperation partners and to clarify the causal relationships between cooperation, performance, and long term competitiveness.

Application of the differential display RT-PCR strategy for the identification of inflammation-related mouse genes

The inflammatory response elicited by various stimuli such as microbial products or cytokines is determined by differences in the pattern of cellular gene expression. We have used the differential display RT-PCR (DDRT-PCR) strategy to identify mRNAs that are differentially expressed in various murine cell types stimulated with pro-inflammatory cytokines, microbial products or anti-inflammatory drugs. Mouse embryonic fibroblasts (MEFs) were treated with IFNs, TNF, or sodium salicylate. Also, peritoneal macrophages from C3H/Hej mice were stimulated with T. cruzi-derived GPI-mucin and/or IFN-g. After DDRT-PCR, various cDNA fragments that were differentially represented on the sequencing gel were recovered, cloned and sequenced. Here, we describe a summary of several experiments and show that, when 16 of a total of 28 recovered fragments were tested for differential expression, 5 (31%) were found to represent mRNAs whose steady-state levels are indeed modulated by the original stimuli. Some of the identified cDNAs encode for known proteins that were not previously associated with the inflammatory process triggered by the original stimuli. Other cDNA fragments (8 of 21 sequences, or 38%) showed no significant homology with known sequences and represent new mouse genes whose characterization might contribute to our understanding of inflammation. In conclusion, DDRT-PCR has proven to be a potent technology that will allow us to identify genes that are differentially expressed when cells are subjected to changes in culture conditions or isolated from different organs.