With the popularity of live streaming commerce, the characteristics of streamers and products subtly influence consumer behavior through visual live streaming form. Based on dual-process theory, this paper develops a comprehensive theoretical model to examine how consumer perceived streamer characteristics and product characteristics influence streamer attractiveness and product attractiveness, and explore how consumer behavior inertia is affected by streamer attractiveness and product attractiveness. An online survey consisting of 300 participants was recruited to empirically examine the proposed research model. The results indicated that consumer perceived streamer characteristics and product characteristics are important factors affecting the streamer attractiveness and product attractiveness, which in turn positively affect consumer’s shopping experience memory, which further influence consumer behavior inertia. In addition, the moderating effects of mindfulness are also examined.

The present study choose to conduct consumer behavioral research in Metaverse situation, explores factors that influence consumer shopping enjoyment and purchase intention from product, service and technology perceptive. The research team gathered the primary data through questionnaire subjecting to Chinese consumers (n=300) who know about TaoBao future city which themes on virtual buying in Metaverse. In addition, structural equation modeling is employed to examine the hypothesized relationship among the variables. The result shows that all the driving factors positive effects consumer shopping enjoyment and then influence purchase intention positively. The finding is significantly fundamental to establish theoretical framework future about virtual shopping in Metaverse, and help marketers realize how to set virtual stores in Metaverse to enhance consumer shopping experience so that they could improve consumer purchase intention in the context.

Introduction Many extant studies in the strategic management literature show that a firm’s network influences its innovation outcomes (Ahuja, Lampert, & Tandon, 2008). Networks are characterized by strong and weak ties in terms of the combination of the amount of time, intensity, intimacy, and reciprocal services (Granovetter, 1973). There is, however, a continuing debate about the relative advantages of strong and weak ties. These equivocal findings suggest that the relationship between tie strength and a firm’s innovation outcome is complex, and call for a more detailed examination of this relationship. The implications of networks for a firm’s innovation outcomes are quite significant. Nevertheless, the majority of research studies still examine networks using simple dyadic relationships (e.g., Capaldo, 2007). In reality, a firm’s networks are composed of more than a single dyadic relationship and are much more complex. Thus, dyadic approaches are limited in providing understanding of networks on a firm’s innovation performance. As such, we will take the perspective of a focal firm in a triad network. While still relatively simple, the triad network approach allows us to identify key relationships previously unexplored in network tie configuration, and to shed light on the equivocal results in the extant literature. Specifically, we will examine the position of the strong or weak ties among the firms, and also whether the strong or weak ties are adjacent or non-adjacent to the focal firm. Breakthrough innovation is defined as the basic invention, which leads to the evolution of many subsequent technological developments (Ahuja & Lambert, 2001). This definition suggests that novel and unique knowledge is required to create breakthrough innovation. Indeed, recent research shows that firms need novel knowledge created by network partners to create breakthrough innovation (e.g., Srivastava & Gnyawali, 2011). As such, we investigate how different levels of novel and diverse knowledge arising from the position of network ties impact a focal firm’s breakthrough innovation. Nevertheless, obtaining diverse and novel knowledge from networks does not guarantee the creation of successful breakthrough innovation. A firm needs a capability to learn, absorb and integrate the new knowledge into its works, which is its absorptive capacity. Thus, we examine the moderating role of a firm’s absorptive capacity on the relationship between the impact of the configurations arising from the position of strong/weak ties and a firm’s breakthrough innovation in a triad network relationship. Conceptual Framework We posit six different types of network configurations based on tie strength and position of strong/weak ties that are adjacent or non-adjacent to the focal firm in a triad (Figure 1). For example, Configuration 1 has three strong ties and Configuration 6 has three weak ties. Configuration 2 has two strong ties that are adjacent to a focal firm and one weak tie that is non-adjacent to a focal firm in a triad. Importantly, we use two theories (i.e., network theor and relational theory) to elaborate the impact of six configurations on a firm’s breakthrough innovation, considering the tie strength, the position of strong/weak ties, and whether the strong or weak ties are adjacent or non-adjacent to the focal firm. The first of these is network theory. Network theory and relational theory assert different effects of strong versus weak ties on a firm’s breakthrough innovation. To resolve the ambiguities in the literature on this issue, we combine network theory and relational theory and investigate the implication of the position of the strong or weak ties. We argue that the position of strong/weak ties must be considered to explain the impact of tie strength on firm breakthrough innovation in a triad context. For example, Configuration 2 has two strong ties adjacent to focal firm and one weak tie non-adjacent to focal firm (Figure 1). The non-adjacent weak tie provides potential for diverse and novel knowledge and reduced knowledge redundancy. Also, adjacent two strong ties provide the benefits of commitment and trust, and rich information flow. Additionally, these adjacent strong ties facilitate the transfer of novel knowledge generated from the non-adjacent weak tie. Thus, we argue that Configuration 2 has a potentially positive influence on firm breakthrough innovation. Configuration 5 has two adjacent weak ties and one non-adjacent strong tie. The non-adjacent strong tie has high knowledge redundancy and high trust and commitment between the two actors B and C (Figure 1). The non-adjacent strong tie between B and C induces potential opportunistic tendencies toward focal firm and inhibits information sharing with the focal firm. This indicates that the two firms form an alliance to the detriment of the focal firm. Further, adjacent weak ties provide novel and diverse information while maintaining less commitment and trust with the focal firm. Importantly, diverse information from adjacent weak ties is degraded because of the knowledge redundancy generated by non-adjacent strong tie between B and C. Thus, we argue that Configuration 5 has potentially negative influence on firm breakthrough innovation. The above discussion suggests that one cannot determine which type of network ties will tend create a firm’s breakthrough innovation simply by counting the numbers of weak and strong ties in the configurations. One must also consider the position of the strong and weak ties within the network and the focal firm’s absorptive capacity. For example, though there is a high level of knowledge redundancy created by the non-adjacent strong tie in Configuration 5, if the focal firm has strong ability to learn, the focal firm can still capture and use the limited new knowledge, depending on its absorptive capacity. Next, we explain how firm’s absorptive capacity influences the relationship between six configurations and breakthrough innovation. Methods We will collect a full sample of data from three main data sources: alliance data from the Securities Data Company (SDC) on Joint and Alliances, patent data from the National Bureau of Economic Research (NBER) U.S. Patent Citation 1997-2016, and financial data from COMPUSTAT. The initial sample will consist of all firms that announced a triad of strategic alliance firms across industries from 1997 to 2016 in the United States. Implications Our study makes two key contributions. First, we investigate the impact of various triad strong/weak tie network configurations on a firm’s breakthrough innovation. We test various effects accrued from the position of strong/weak ties that are adjacent or non-adjacent to a focal firm on the focal firm’s breakthrough innovation in the triad network. By examining these relationships, we uncover critical implications of the tie strength previously unexplored in the network literature. We provide conceptual advancement of Granovetter’s notion of the strength of weak ties, showing the importance of the position of strong or weak ties as a critical driver to influence a firm’s breakthrough innovation. Second, we investigate how a focal firm’s absorptive capacity moderates the impact of network configurations on a firm’s breakthrough innovation. This provides a more precise and fine-grained understanding of how a firm’s capability to absorb outside knowledge influences the relationship between network configurations and a focal firm’s breakthrough innovation. Combined, our two contributions help to resolve some of the equivocal results in the extant literature regarding the effects of strong or weak ties on breakthrough innovation.

Introduction Shorter innovation cycles, the huge cost of R&D and dearth of resources compel firms to search for new innovation sources (Gassmann and Enkel 2004). Current research argues that firms need to open up their solid boundaries and seek valuable knowledge from external partners so that firms can extend the innovation function beyond their four walls (Chesbrough 2003). In this context past research has identified universities, or higher education institutions (HEIs) as an important source of innovation (e.g., Lambert 2003). Indeed, universities undertake a “third mission” in addition to their core mission of research and teaching, by focusing on “technology transfer” that engages in the process of the commercialization of science (Etzkowitz et al. 2000). Thus, firms can take huge advantages through the collaboration with universities. While relationships between firms have the risk of opportunism embedded in them, support provided by universities are hard to imitate by competitors due to the novelty and uniqueness in the ideas they provide their partner firms. Despite this important role that universities play, no systematic theoretical treatment has been attempted in academia. Ironically, university and industry links have been studied much less frequently and have been valued lesser than other sources (e.g., suppliers and customers) in terms of knowledge transfer for firm innovation (Hughes 2011). Extant research examines collaborations between universities and firms using simple descriptive analysis (e.g., Laursen and Salter 2004) and illustrates the relationship with anecdotal evidence (e.g., Cosh and Hughes 2010). Thus, extant literature provides little-to-no empirical evidence regarding firm performance, such as a firm’s innovation outcomes, when the firms are supported by universities. Our broad-based investigation makes several key contributions. First, our study is the first to demonstrate empirically what types of HEIs’ activities enhance a firm’s innovation outcomes. Because the two different types of HEI activities have different features, it helps us get a more precise understanding of which specific type of HEI-supported activity influences which firm innovation outcome. Second, our research finds that a firm’s absorptive capacity influences the relationship between HEI-supported activities and a firm’s innovation outcomes. This finding helps to identify how firm capability to absorb outside knowledge influences the relationship of HEIs’ involvement on a firm’s innovation outcomes. Conceptual Framework The most frequent form of a firm’s interaction with universities is people-based activities (Hughes 2011). Universities transfer knowledge through people-related activities such as conferences, special lectures, education programs, and social networks supporting firm innovation. Such people-based activities can influence firm innovation performance. People-based activities involve the activities conducting by firms to increase their business competitiveness. Since a firm’s employees are key to discovering new products and processes, special training programs provided by universities will help supplementing knowledge towards specific firm innovation outcomes. Additionally, other people-related activities such as placing university staff on a firm’s board of directors can also encourage exchange of knowledge and information resulting in cutting-edge new product and process innovation. Tether and Tajar (2008) found that firms that have participated in professional meetings or conferences held by HEIs have a better chance of surpassing their current innovation performance. A firm can improve its innovation performance by making human assets supported by its partners. As partners work together, this helps increasing work efficiency by improving communication, knowledge sharing, and their relative capacity to absorb knowledge for innovation. Research suggests that universities may have lower barriers to engagement with firms by removing bureaucracy, lowering transaction costs and speeding up reaction times (Mateos-Garcia and Sapsed 2011). Therefore, universities have an important role in transferring new knowledge through people-based activities, resulting in new products and processes for the firm. Thus, we hypothesize as follows: Hypothesis 1A (H1A). A firm’s people-based activities with HEIs are positively related to the introduction of new products in the firm. Hypothesis 1B (H1B). A firm’s people-based activities with HEIs are positively related to the introduction of new processes in the firm. Universities have a distinct role in affecting a firm’s innovation performance through problem-solving activities. Firms that acquire knowledge from universities improve their competitive position that helps firm acquire a competitive advantage over other firms that do not collaborate with universities (Gassmann and Enkel 2004). Universities provide problem-solving activities such as joint research, contract research, consulting services, informal advice and provision of access to specialized instrumentation, equipment or materials and of product prototyping. For example, in 2009, US firms sponsored more than $4 billion worth of university research (Kurman 2011), as a result of which U.S. universities own nearly one-quarter of new U.S. patents in the fields of nanotechnology and biotechnology. Thus, firms that collaborate with universities can achieve cutting-edge product and process innovation (Kurman 2011). Hosting workshops and performing joint research with universities are core problem-solving activities. For example, IBM, one of the most successful and established enterprises in the IT market, hosted 350 workshops per year and has had 50-100 ongoing research projects with universities, helping IBM to successfully launch new products into the market (Gassmann and Enkel 2004). Further, firms can also integrate partners (i.e., HEIs) to combine their different competencies to enrich their own innovation process (Gassmann and Enkel 2004). Based on the above, we hypothesize as follows:Hypothesis 2A (H2A). A firm’s problem-solving activities with HEIs are positively related to the introduction of new products. Hypothesis 2B (H2B). A firm’s problem-solving activities with HEIs are positively related to the introduction of new processes. Shorter time-to-market strategies, increasing R&D costs and a dearth of resources cause firms to search for new innovation strategies. This phenomenon is reinforced by a rapid churn in technology and customer demands. In this competitive environment, HEIs’ involvement is increasingly important for a firm’s innovation success because integrating external sources of knowledge from HEIs can result in major advantages for firms (Rappert et al. 1999). Further, people-based and problem-solving activities supported by HEIs do not replace a firm’s internal innovation activities and, as a result, the firm undertakes a great deal of its own innovation activities. Also, scholars argue that collaboration with other partners does not always provide better innovation performance because of the lack of a firm’s capability to processing valuable knowledge from the outside partners (Cohen and Levinthal 1990). This indicates that the mere acquisition and exploitation of knowledge from universities do not guarantee successful firm innovation outcomes. To create successful firm innovation, the firm should possess absorptive capacity, which is the learning capability to processing knowledge acquired from the HEIs into their internal work. Thus, firms can be expected to invest in their absorptive capacity in this situation (Tether and Tajar 2008). Further, Keller (1996) argues that successful R&D spillover (i.e., absorptive capacity) effects are dependent on the activities of human capital (i.e., people-based activities). Also, Cohen and Levinthal (1990) argue that firms can increase their absorptive capacity directly, as when they send personnel for advance technical training (i.e., people-based activities). Further, Kim (1998) argues that absorptive capacity is the major factor in developing problem-solving skills that allow a firm to create new knowledge that influences firm innovation performance. As such, absorptive capacity stresses the internal capability to acquire and assimilate outside knowledge into a firm while HEIs’ involvement is a resource that is created by external source enhancing a firm’s innovation outcomes. Therefore, identifying the role of absorptive capacity is a useful tool to explain the relationship of HEIs’ people-based activities and problem-solving activities on firm innovation performance. However, Nooteboom and colleagues (2007, pp. 1031) argue that “while there may be increasing returns in absorptive capacity, improving the general ability to understand and appreciate novelty value in collaboration, there are decreasing returns to knowledge in finding further novelty: the more one knows the further away one has to look for novelty.” This indicates that too much absorptive capacity in a firm negatively affects the impact of people-based activities on a firm’s innovation performance. While people attending conferences or lectures supported by universities may acquire novel knowledge that can influence a firm’s innovation performance, their activities may have negative impact on a firm’s innovation outcomes when a firm has greater absorptive capacity, due to diminishing impact of a firm’s absorptive capacity to create novel idea. Extant research suggests that the greater a firm’s absorptive capacity, the lesser the firm can find further novelty (Noteboom et al. 2007), which suggests that absorptive capacity makes firm innovation activities less efficient. Based on the above discussion, we hypothesize as follows:Hypothesis 3A (H3A). People-based activities with HEIs positively related to the introduction of new products and/or processes will become weaker at a higher level of absorptive capacity. Hypothesis 3B (H3B). People-based activities with HEIs positively related to new product radicalness will become weaker at a higher level of absorptive capacity. Hypothesis 4A (H4A). Problem-solving activities with HEIs positively related to the introduction of new products and/or processes will become stronger at a higher level of absorptive capacity. Hypothesis 4B (H4B). Problem-solving activities with HEIs positively related to new product radicalness will become stronger at a higher level of absorptive capacity. Methods We test the hypotheses presented across two studies. The purpose of Study 1 is to validate our prediction about how HEI activities affect firm innovation performance (H1A to H2B). Study 2 expands this initial research frame by validating the moderating effects of a firm’s absorptive capacity on firm innovation outcomes (H3A to H4B). Implications There is an argument to transfer knowledge from HEIs to firms due to the cultural differences between them (Lambert 2003). Nevertheless, universities are playing an increasingly strategic role in stimulating innovation in firms though the transfer of technology (Hughes 2011). Scholars have largely disregarded the more specific activities performed by HEIs such as people-based and problem-solving activities. Little attention has been paid to how people-based and problem-solving activities affect firm innovation performance. Further, firm innovation outcomes can be affected differently by some specific HEI activities because each activity supported by HEIs plays a different role in impacting certain types of firm innovation outcomes. Based on our results, problem-solving activities are related to new product innovation while people-based activities are related to new process innovation. Additionally, absorptive capacity had a negative moderating effect with people based activities and a positive moderating effect with problem solving activities on a firm’s innovation outcomes. This is important to theoretical and practical implications because a firm is able to know which activities are required to improve their new product or process innovation. This leads a firm to save huge costs to achieve successful innovation.

In the advent a new market that didn’t exist a few years ago, the total sales in wearable devices could top $32.2 billion by 2019, up from $18.9 billion last year (Kharif 2015). The most anticipated new device is the Apple Smart Watch which has a function to detect pulse rate and send messages using voice commands (There is a gold version for $10,000). Further, Tag Heuer recently announces a partnership with Intel and Google to produce the world's first luxury Android Wear Smartwatch. Given that the high potential to do some research in this area (i.e., luxury brand alliances), little research examines luxury brand strategy and especially luxury ingredient branding (IB) strategy. This study explores the evaluations of and attitudes to the host luxury brand after IB alliances. An ingredient branding (IB), the incorporation of parent brand with another brand as ingredient (Desai and Keller 2002), allows two brands to have better market competitiveness (Simonin and Ruth 1998). The IB parent brand is the “host,” the main product, and the “ingredient,” a component that is integrated into the host. For example, Dell computer (the host) has a co-branding relationship with Intel as the ingredient (Intel, 2006). Both brands enjoy the benefits of the relationship that include mutual cooperation and knowledge sharing. The IB strategy has valuable benefits for both brands. For example, the host (i.e., Dell) may enjoy an enhanced market reputation, while the ingredient brand (i.e., Intel) may benefit by reducing the probability of entry by competitors. Further, Dell receives a preferential price from Intel, while Intel enjoys a stable and long-term customer. Current research on ingredient branding examines the determinants of IB success (Desai and Keller 2002) as well as the feedback effect on a parent brand subsequent to an IB alliances (Rodrigue and Biswas 2004). IB feedback effect involves changes in consumer attitudes toward the original parent brand resulting from the IB alliances. Extant research in this topic shows positive effects of IB strategy for the host (e.g., Balachander and Ghose 2003). However, some other research also shows that negative effects for the host caused by an IB alliances (e.g., Votolato and Unnava 2006). This equivocal findings suggest that there are some other conditions generating positive and negative effects of IB strategy for the host. Thus, the purpose of our study is to examine the conditions under which IB strategy influences negatively or positively to the host. We will focus uncovering this research gap on finding the conditions that influence positively or negatively to the host. Using ingredient brand strategy in luxury brand, we will examine how the fit of the host (Tag Heuer) and the ingredients (Google and Intel) influences the host’s brand attitude. We assume that the product fit (i.e., the host current product category: Tag Heuer watch vs the final product after IB alliances: Tag Heuer Android Wear Smartwatch) may positively influence the host’s brand attitude while the brand fit (i.e., luxury brand: Tag Heuer vs non-luxury brand: Google and Intel) may negatively influence the host’s brand attitude. Further, we will examine the role of Brand Engagement in Self-Concept (BESC) as a moderator in this relationship (Sprott, Czellar, and Spangenberg 2009).