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The purpose of this study is to explore market entry strategies in a high-tech successive generations (HTSGs) market, by investigating entry mode via entry timing and path differentiation and the performance outcomes of entry mode.

The methodology of building a theory from a longitudinal case study is adopted by using useful cases in a HTSGs market after constructing an integrated research framework to explore market entry mode. Different entry modes were investigated by studying entry timing and migration path of three firms’ case in logic semiconductor market. In addition, performance outcomes of different entry modes were measured and correlated with each other.

The results identified three major entry modes suitable for a HTSGs market. The three firms differentiated their entry modes by exploiting different entry timings from the earliest to the last and different migration paths including switching, leapfrogging and new entrance path to enter a market. First mover advantage also exists in a HTSGs market, and it was found uniquely that the financial performance denoted by entry mode outcomes was correlated with technological knowledge.

This study extends the theory of extant entry strategy from general consumer or industrial market to HTSGs market, in which intense competition exits and technological innovation is important. Moreover, this study verified that the causality between early entry and positive performance was also effective in HTSGs market with a shorter duration of early entry advantage.

This study has managerial implications for firms to establish market entry strategy in HTSGs market and other markets. To become a product leader, a fast follower or a late follower, firms can differentiate their entry mode by adjusting the entry timing and migration path in the context of market and technology.

This study examined market entry strategies suitable for HTSGs market based on its unique characteristics and extended relevant theory into HTSGs market. Further, an integrated research framework, which explores the market entry mode, was constructed to facilitate further exploration of entry mode into other markets.

The purpose of this study is to identify the types and features of business relationship when the value co-creation phenomenon is extended to an early stage of the value chain, in which technological innovation is essential, in a high-tech business-to-business (B2B) market.

The methodology of building a theory from a case study is adopted in this study to propose an early-stage value co-creation network. Qualitative data are coded on the basis of grounded theory coding after collecting the triangulation data from multiple sources.

In a high-tech B2B market, three types of business relationships (supplier–customer mutual, supplier-centric and network-based business relationships) co-create values at an early stage of the value chain. Intellectual resource, efficiency resource and supplier-centric business relationships are uniquely found in this stage.

This study provides new insight suggesting that the notion of value co-creation can be extended to early stages of the value chain in a high-tech B2B market. In addition, this research identifies vital business relationships and how these relationships develop successfully at an early-stage value co-creation network in a high-tech B2B market.

Technology development managers at an early stage of the value chain can co-create relationship benefits by building proposed business relationships integrating resources in a high-tech B2B market. In addition, marketing managers should consider the early stage as another source of value co-creation.

The notion of value co-creation is extended from the later stage to an early stage of the value chain in a high-tech B2B market. Consolidated framework of a value co-creation network integrating actors, resources and relationships, suggested in this study, will be valuable for further theoretical research and business application.

This study aimed to compare the performance of sustainable shoes made with bacterial cellulosic composite and commercial leather shoes using an experimental research design. The two specific research objectives were: (1) to examine the basic material properties of multi-layered bacterial cellulosic materials (MBC), which include green tea-based cellulosic (GBC) mats, hemp fabrics, and denim fabrics, in comparison with those of two-layered leathers (MCP) consisting of calf-skin and pig-skin – commonly used in shoe manufacturing; and (2) to explore wearers’ performance in the two types of shoes by assessing quantitative kinematic and kinetic parameters of lower body movements.

This study focused on assessing the basic materials testing and performance of sustainable shoes through a biomechanical approach, in contrast to commercially available leather shoes, through human wear trials. In this study, green tea-based cellulosic (GBC) mats were developed using the optimal combination of ingredients for cellulose growth. Subsequently, the GBC, denim fabric (100% cotton), and 100% hemp fabric were combined to create multi-layered bacterial cellulosic materials (MBC) as an alternative to leather. Additionally, calf-skin and pig-skin leathers were utilized to produce a commercially available two-layered leather (MCP), commonly employed in shoe manufacturing. 37 of the 42 human subjects who participated in wear testing were collected. A paired t-test was conducted to determine whether significant mean differences existed between the two shoe types, a paired t-test was conducted.

To develop a biodegradable and compostable material that could be used as a leather alternative for the footwear industry, we proposed MBC and examined its properties compared with those of MCP, a product often used when making shoes. These findings confirmed the similar properties of MBC and MCP from the material testing and the possibility of using a men’s sustainable shoe prototype as a leather alternative, in terms of kinematics and kinetics.

The new multi-layered bacterial cellulosic materials (MBC) could be an alternative to commercial leathers such as innovative sustainable material construction, advanced design, and advanced techniques to optimize the overall performance of sustainable footwear.

Investigating the integration of smart textile technologies, ergonomic design principles, and personalized customization will contribute to developing MBC and making sustainable shoes using MBC compared with commercial leather shoes. This study provides valuable insights into further refinement and innovation in the sustainable footwear industry.

The purpose of this paper is to contribute knowledge on the issues and benefits associated with managing brand presence and relationships through social media. UK football clubs are big businesses, with committed communities of fans, so are an ideal context from which to develop an understanding of the issues and challenges facing organisations as they seek to protect and promote their brand online.

Due to the emergent nature of social media, and the criticality of the relationships between clubs and their fans, an exploratory study using a multiple case study approach was used to gather rich insights into the phenomenon.

Clubs agreed that further development of social media strategies had potential to deliver interaction and engagement, community growth and belonging, traffic flow to official web sites and commercial gain. However, in developing their social media strategies they had two key concerns. The first concern was the control of the brand presence and image in social media, and how to respond to the opportunities that social media present to fans to impact on the brand. The second concern was how to strike an appropriate balance between strategies that deliver short-term revenue, and those that build longer term brand loyalty.

This research is the first to offer insights into the issues facing organisations when developing their social media strategy.

The authors aim to propose a novel plane extraction algorithm for geometric 3D indoor mapping with range scan data.

The proposed method utilizes a divide-and-conquer step to efficiently handle huge amounts of point clouds not in a whole group, but in forms of separate sub-groups with similar plane parameters. This method adopts robust principal component analysis to enhance estimation accuracy.

Experimental results verify that the method not only shows enhanced performance in the plane extraction, but also broadens the domain of interest of the plane registration to an information-poor environment (such as simple indoor corridors), while the previous method only adequately works in an information-rich environment (such as a space with many features).

The proposed algorithm has three advantages over the current state-of-the-art method in that it is fast, utilizes more inlier sensor data that does not become contaminated by severe sensor noise and extracts more accurate plane parameters.

It is difficult to build our own social data set because data in social media is generally too vast and noisy. The aim of this study is to specify design and implementation details of the Twitter data collecting tool with a rule‐based filtering module. Additionally, the paper aims to see how people communicate with each other through social networks in a case study with rule‐based analysis.

The authors developed a java‐based data gathering tool with a rule‐based filtering module for collecting data from Twitter. This paper introduces the design specifications and explain the implementation details of the Twitter Data Collecting Tool with detailed Unified Modeling Language (UML) diagrams. The Model View Controller (MVC) framework is applied in this system to support various types of user interfaces.

The Twitter Data Collecting Tool is able to gather a huge amount of data from Twitter and filter the data with modest rules for complex logic. This case study shows that a historical event creates buzz on Twitter and people's interests on the event are reflected in their Twitter activity.

Applying data‐mining techniques to the social network data has so much potential. A possible improvement to the Twitter Data Collecting Tool would be an adaptation of a built‐in data‐mining module.

This paper focuses on designing a system handling massive amounts of Twitter Data. This is the first approach to embed a rule engine for filtering and analyzing social data. This paper will be valuable to those who may want to build their own Twitter dataset, apply customized filtering options to get rid of unnecessary, noisy data, and analyze social data to discover new knowledge.