Reputation can be viewed as the sole most appraised organizational resource (Gibson, Gonzales, & Castanon, 2006; Walker, 2010). Similarly, Hall (1993) discovered that chief executive officer's classify reputation as the most relevant intangible asset. It is also widely accepted that reputation and competitive advantage cohere (Fombrun & Shanley, 1990; Hall, 1993; Roberts & Dowling, 2002), and multiple scholars found that reputation and firm performance are linked (Brown & Perry, 1994; Deephouse, 2000; Fombrun & Shanley, 1990; Walker, 2010). In today's competitive environments, the role of reputation is remarkably essential (Abimbola & Vallaster, 2007).

Generally, companies compete for reputational status as they compete for customers (Fombrun & Shanley, 1990), because reputation represents an important intangible resource for competitive advantage (Hall, 1993; Roberts & Dowling, 2002; Walker, 2010). A good reputation has multiple strategic benefits, because it helps firms to distinguish themselves from rivals (Peteraf, 1993), and it decreases information asymmetry and uncertainties of the consumer (Boyd et al., 2010; Weigelt & Camerer, 1988). Moreover, a good reputation increases overall firm image, the loyalty to the firm, the tolerance for failure and the propensity to pay premium prices (Henard & Dacin, 2010). Reputation is key in understanding why certain firms outperform other organizations (Boyd et al., 2010), which is viewed by many as the fundamental question of the strategic management field (Hitt, Boyd, & Li, 2004; Rumelt, Schendel, & Teece, 1994).

In competitive high-tech environments, firms actively promote their perception of being innovative entities (Henard & Dacin, 2010). Although innovative performance is an essential part of competitive advantage, its relation to reputation remains to be researched more extensively. Rindova et al. (2005) explicitly find that further research needs to determine how organizational resources attributes affect reputation. In the following hypothesis development, we will explain that this interdisciplinary setting requires elements of both theories, namely the resource-based view and signaling theory. Anything that is made by companies for the sale on competitive markets, obviously with the help of intangible assets, is logically required to be somehow perceived by its constituents. The resource based view is suited to explain particular capability of firms to develop and commercialize innovative solutions (Barney, 1991). Signaling theory and cue utilization theory are used to explain how firms shape their image across consumers (Rao et al., 1999; Spence, 1973).

Examining important studies in reputation research, the common finding is that consumers in different settings have difficulties in predicting and evaluating future firm outputs and their behavior (Lange et al., 2011). The internal workings of a firm are rather opaque with limited availability of information regarding organizational abilities and intentions (Rindova et al., 2005). Therefore, there are reasons to doubt the signaling value of less tangible facets, such as patents and R&D (Henard & Dacin, 2010). Rindova et al. (2005) explicitly highlighted the role of knowledge as an intangible asset in this context. The authors argue that “the difference [to inputs] may also be because of the fact that the quality of productive assets, especially knowledge assets, is complex and difficult to observe, therefore reducing their value as a signal that stakeholders can readily use to form expectations about quality” (p. 1045 in Rindova et al., 2005). This is most likely the case, even though the external validation of the basic technology (by the respective patent office with the patent grant) can often be seen as an external positive evaluation of the companies’ ability to develop innovation. Strictly in accordance with these assumptions, we take away that intangible resources as patents, citations and R&D spending are very complex and difficult to detect by consumers.

From a practical perspective, the above mentioned argumentation may not fully incorporate certain preconditions that characterize industries where technically innovative firms operate. High-tech firms usually act on markets with high barriers to enter (Carpenter & Petersen, 2002), shortening product cycles (Ali, Krapfel, & LaBahn, 1995) and large R&D investments (Neelankavil & Alaganar, 2003). The intellectual property protection in these industries is shaped by a trend of increased patenting activity (Hirshleifer et al., 2013), also often referred to as a “patent explosion” (Hall, 2004). The annual report “The most innovative companies” from The Boston Consulting Group (Wagner, Taylor, Zablit, & Foo, 2014) highlights that a key strategy of the firms on the report is “to leverage their IP both to exclude rivals and to build markets” (p. 11). That strategy has been found to be repeatedly successful for “breakthrough innovators” for several years (Wagner et al., 2014). In the long run, firms do likely secure its competitive advantage via patents (Cohen, Nelson, & Walsh, 2000), including established firms as well as ventures for whom intellectual property is their precious asset (Gans & Stern, 2003). The consequence is that innovative products new to markets are likely to be protected by patents (Battisti et al., 2015). In that sense, Wernerfelt (1984) finds that “most products require the services of several resources and most resources can be used in several products” (p. 171). Even tough consumers may have difficulties to accurately observe intangible assets as described above (e.g. Rindova et al., 2005), the products are still likely to be embodied by them (Archibugi & Planta, 1996). Many products are even composed of “numerous separately patentable elements” (p. 14 in Cohen et al., 2000).

The fact that these patented technologies have to be novel in order to get granted by the patent office (USPTO, 2016) however logically increases the introduction of “new” technology. By definition, patents have to be the first of its kind (“novelty”), a nontrivial extension of what is known and finally useful thus having commercial potential (Hall, Jaffe, & Trajtenberg, 2000; Merges, 1988). Securitizing proprietary innovation is usually fostered when technologies are likely to become economically valuable (Eusebi & Silberglitt, 2014) and are thus commercialized. Otherwise, the economic payoff for engineering, time, capital, legal and human resources into filing patents would not be given (Archibugi & Planta, 1996). The higher the ability to produce leading patented innovation increases the chance that technologically innovative products are finally brought to the market. This is also confirmed by Artz et al. (2010) who find that patents are positively associated to product introductions in a longitudinal study in 35 industries over 19 years. Above, the temporal monopoly for that certain invention secures the ability to be the only firm offering that technology (USPTO, 2016). Creating something inimitably that makes it difficult for rivals to copy with certain technology-based features enhances the value for the consumer (Hanaysha, Hilman, & Abdul-Ghani, 2014). Research also discovered that the main factor of product failure is the absence of innovation with a novel and unique aspect (Sethi, Smith, & Park, 2001). As “newness” usually largely shapes consumers perception of innovation (Garcia & Calantone, 2002; Shams et al., 2015; Tomczak, Vogt, & Frischeisen, 2016), the perception should therefore be positively influenced based on fundamentally new technology. We therefore argue that the higher the technologial performance and inventive performance of firms, the higher should be its reputation among consumers to be also technologically innovative. Weighing the arguments from above, relying on signaling theory and cue utilization theory alone would logically result in a negation of these relations (cf. Popper, 1959), meaning that consumers can most likely not evaluate and assess intangibles as patents and R&D. However, looking at the predominant circumstances in competitive markets and the inclination toward proprietary protection it is hypothesized as follows:Hypothesis H1

Technological performance (patent stock/R&D stock) is positively associated with reputation for technological innovation.

The data for this study consist of consumer data, patent data and financial data (cf. Nagel et al., 2014).

In this study, we employ a Poisson regression to research the influence of intangible assets on the reputation for technological innovation. We do so because the dependent variable of this study, the ranking for the reputation for technological innovation conducted in 2013 has count data of the most innovative firms for the dependent variable (Fahrmeir, Kneib, & Lang, 2009). Similarly, all our consumer variables are obtained in 2013. In contrast, the independent variables are bound to time lags. The reasoning originates from the fact that it takes several years until ideas emerge into technology and finally patents. Therefore, studies usually employ time lags, e.g. Nooteboom, Van Haverbeke, Duysters, Gilsing, and Van den Oord (2007) find that a 5 year time frame to measure technological impact (for patents) is appropriate. We extend this time frame and employ a maximum time lag of 7 years until 4 years (2006–2009) prior to the dependent variable in 2013.

For our study, we were able to gather information from 231 consumers, each noting five firms to be among the most innovative firms in their perception. This procedure resulted in 1155 nominations for our ranking (cf. Nagel et al., 2014). From these initial company names, 1046 were determined to be mentioned at least twice. The top five nominations are Apple (17.11%), Google, Inc. (12.43%), BMW AG (8.12%), Samsung Corp. (8.03%) and Siemens AG (6.11%). In sum, the previously mentioned firms explain approximately 51.82 percent of all recalled firms. The top fifteen combined explain 80.31 percent of all ranking nominations. Interestingly, our respondents’ nominations are very similar to the annual report “The most innovative companies” from The Boston Consulting Group (Wagner et al., 2014). To provide further information, the complete list is attached in Table 1.

Furthermore, we gathered important information regarding consumers in our sample. The gender ratio in our sample of respondents is split into 67.50 percent male and 32.50 percent female. Furthermore, the age ranges from 19 to 81 years, averaging 24.98 years. Our participants have mean work experience of 1.22 years, with a minimum of none to a maximum of 37 years. Within this time frame, 42.10 percent have had experience in high tech industries, whereas 22.80 percent have experience in low-tech industries. From an education perspective, 44.60 percent of the sample has an educational background with a university entrance qualification. Of the respondents, 31.20 percent completed a bachelor's degree, and 19.50 percent finalized a master's program. During their studies, 68.40 percent focused on “business & economics” and 12.10 percent on engineering related subjects. Table 2 provides information for the variables. The patent data were gathered for the firms mentioned in the ranking. In detail, the largest patent stock originates from Samsung with 47,711.79 patents. The average patent stock size is 12,563.67 with a standard deviation of SD=15,979.52. From the patent citation stock, we can highlight that the highest number of citations again originate from Samsung, which has 10,813.83. On average, the firms in the sample have 14,328.89 patent citations. The citation intensity ratio (citation stock/patent stock) is, on average, 0.896. The highest ratio originates from Cisco Systems with 1.608. The technological performance (patent stock/R&D stock) has a mean of 0.734. Furthermore, company variables show that the firms achieved annual revenues with a mean of 59.65 billion US-dollars. The maximum annual revenue originates from Toyota Motor Corporation, which had approximately 207.85 billion US-dollars. The firms nominated in this study emerge from an international sample. Most perceived innovative companies originate from the USA (54.68%) and from Germany (29.90%). The largest industry type of our study, measured using the SIC classification, resulted in the following split: “Transportation equipment” (22.40%), “Machinery and computer equipment” (26.70%) and “Business Services” (23.80%).

Information on the correlations among this study's variables can be found in Table 3. To additionally check for multicollinearity, the variance inflation factors (VIF) appear to be uncritical as none of the variables has a value above ten (Kennedy, 2003; Neter, Wasserman, & Kutner, 1989). Table 3 indicates that VIF values range from 1.07 to 4.27. We have also calculated an average VIF of 2.10, which indicates no apparent multicollinearity. Because some correlation coefficients are relatively high for R&D stock and revenues, the results should be still interpreted with care.

In our regression, we estimate four different models for the different time spans to explain the fact that knowledge needs time to be incorporated in products and services. Therefore, we employ regression models beginning in 2006 (M1) until 2009 (M4). We have clustered our models by the consumer because the consumers named companies, which resulted in the ranking for the dependent variable reputation for technological innovation. With this method, we find robust standard errors. To highlight our results, we start with largest time lag (M1) to the smallest time lag (M4). The complete results can be found in Table 4.

We find significant support for the influences of technological performance (H1) on reputation for technological innovation in M1. Again, one must bear in mind that negative correlations indicate that higher values are associated with higher ranks. A lower number is actually a better place in the corresponding ranking. In M1, technological performance is positively and significantly related to reputation for technological innovation, (0.092, p<0.05). The other models M2–M4 do not show significant results and are negative. Furthermore, we find significant and negative associations of citation intensity (inventive performance) to reputation for technological innovation in all models M1–M4. In M1, citation intensity is negatively connected to the dependent variable (−0.624, p<0.001). This is similar in the following models, thereby M2 (−0.561, p<0.001), M3 (−0.595, p<0.001) and M4 (−0.662, p<0.001). For the results concerning our third hypothesis, we find that there are positive effects in the relation between marketing intensity and reputation for technological innovation in M1 (1.488, p<0.001), M2 (3.144, p<0.001), M3 (3.199, p<0.01) and finally M4. (1.758, p<0.001).

Again, one must bear in mind that positive correlations indicate that higher values are associated with lower ranks. Further, we find negative and significant effects of revenue (as size effects) in two models M2 (−0.045, p<0.05) and M3 (−0.041, p<0.05). Furthermore, the consumer variables in all regressions models do not play decisive effects on the dependent variable. For the control variables, we find when firms do not disclose available information on their R&D spending or patents, we employed a dummy variable.

We find higher levels of an important indicator to influence the reputation for technological innovation: inventive performance. Consumers likely value the costly and uncertain R&D efforts undertaken by companies and remember those firms that constantly provide innovation (Henard & Dacin, 2010). The results represent essential strategic information for both innovation and marketing functions. Practitioners have a high awareness of a sophisticated reputation as many managers and executives understand its use in the long term strategic positioning of the firm. Hall (1993) discovered that chief executive officer's classify reputation as the most relevant intangible resource. Unfortunately, reputation is very difficult to achieve and thus relatively easy to demolish. Warren Buffet, CEO of Berkshire Hathaway Inc., famously said, “It takes twenty years to build a reputation and five minutes to ruin it” (p. 111 in Lowenstein, 2013). The strong association of truly qualitative innovation with the corresponding technological reputation has been found. Innovation functions are required to deliver constant novel solutions to its customers, but this also brings enormous benefits in strategic positioning as its constituents can perceive it accordingly. R&D projects that foster new approaches and technological initiatives, likely sometimes aiming on risky breakthrough solutions, may be required in order to achieve a gains in technological reputation. Of course, those disruptions come with uncertainties for the innovator (Rindova & Petkova, 2007). Incremental changes to product offerings may not be that useful in the context of technological reputation, rather indicating to concentrate the investments and efforts on fewer and potentially cutting edge developments. Building competencies and capabilities to succeed in such new technological environments need to be planned and executed thoroughly (Leten, Belderbos, & Looy, 2016).

With marketing intensity negatively relating to the reputation for technological innovation, we note that these investments have be evaluated critically. In our case, simply spending more relatively is certainly not sufficient for defending a strategic competitive position (Basdeo et al., 2006; Shamsie, 2003) as consumer value truly innovative products and services. Delivering what the customers perceive as innovative is certainly a further aspect of how marketing functions need to evaluate their efforts. This may also incorporate specific content cues and signals to highlight technical novelties that have been developed. We can also behold that the interplay of marketing and innovation functions is very relevant. With the results of this study we find that specifically technical and non-technical efforts by firms need to be strategically aligned in order to achieve a maximum of return. This is particularly the case regarding obtaining the desired effects to support and achieve a competitive advantage via a sophisticated technological reputation.

Generally, the incorporation of intangible resources as patents and its citations in reputation research is a necessary step forward to get a better understanding of how the topics are connected and linked. This may open a new perspective in reputation research, especially in technology-related settings. R&D and patents are aspects that are usually nested in the resource-based view (Barney, 1991; Hall, 1992, 1993). Yet, as we noted in the hypothesis section, signaling theory and cue utilization theory are used to explain how firms shape their image across consumers (Rao et al., 1999; Spence, 1973). In particular, an approach to coherently explain the effects for competitive advantage, research on the reputation for technological innovation may need to base its evaluations not on one or the other theory, but on the alignment of these theories (cf. Nagel et al., 2014). This may further advance our contemporary understanding of the reputation for technological innovation.

These empirical findings have limitations. First, large datasets such as the one we used from EPO's PATSTAT encounter inconsistencies. Because the patent data are not originally cleaned, we cannot ultimately assure the correct assignment of patents to the corresponding firms after checking the data manually. We assume that the remaining effects are negligible. Although patents are a very rich and fruitful data source for research purposes (Hall et al., 2001) being applied in numerous studies (e.g. Harhoff, Scherer, & Vopel, 2003; Serrano, 2010) they also have drawbacks. For example, not all inventions are technically patentable and hence not every invention is actually patented (Archibugi & Planta, 1996). Above, “a few patents are very valuable, and many are worth almost nothing” (p. 6 in Hall et al., 2000). Of course, there are also different types of innovation, ranging from incremental to radical perceptions of those. These different (possibly subjective) innovation classifications and types may also be regarded as influencing factor. Also, marketing intensity may not capture what the marketing messages in advertising were about, hence we relied on a measure that is widely used in scholarly publications to operationalize marketing investments (Krishnan et al., 2009; Lin et al., 2006). Second, it is useful to notice that the survey is based on a relatively educated and young sample. Above, our response rate of less than 10 percent is relatively low (Dillman et al., 2009). A truly random sample, in terms of a general population may be useful for future studies in this context. Yet, the our respondents’ nominations are very similar to the annual report “The most innovative companies” from The Boston Consulting Group asking 1512 senior executives (Presidents, C-Level Executives, Directors) “from wide variety of industries and countries” (p. 23 in Wagner et al., 2014). The firms in our sample and therewith industries/countries also match this criterion. Above, the firms in which the R&D dummy variable and the patent dummy variable were significant show that firms for which we were able to get R&D and patent data differ from firms for which we do not have this information. For example, firms with no patent data do still significantly influence reputation for technological innovation. Previous work was not concerned with such differences in these groups, e.g. studies by Hall and Oriani (2006). A firm for which there is no data on R&D does not mean that they do not spend anything on R&D, they merely do not publicly publish this information due to restricted financial policies. For the firms with no patent data, we assume that highly innovative firms may also choose different paths to innovation (Chandy & Tellis, 1998), hence their effort and may not be measurable with patent data. We assume that the remaining effects are negligible. Further, the variable marketing intensity might measure marketing generically, as some effort of these investments might also be differently attributed to e.g. corporate social responsibility, corporate branding etc. and not purely on “innovation”. For the financial data we relied mostly on Thomson Reuters. To the best of our knowledge, a detailed measure for advertising expenses strictly related to innovation purposes is not listed. We suggest noting these limitations for future studies in this context.

This research opens future opportunities in the IP and reputation areas. First, we believe that it would be interesting to investigate how firms perform in similar settings that are regarded as “non-innovative”. The question is whether the harsh evaluation of the respondents is also justified when examining the corresponding patent and financial data. If these firms are “true” innovative entities in reality, their unfortunate pursuit of the use of a favorable reputation is not based on their R&D efforts.

Second, because we found that the reputation for technological innovation is associated with the innovative performance, it would be interesting to examine the technology strategy of these firms. Prior studies found that the acquisition of knowledge can help firms in achieving a better innovative performance (Ahuja & Katila, 2001; Laursen & Salter, 2006). Thus, it would be interesting to research whether firms that buy external patents are also observing positive effects on their reputation for technological innovation. Is it possible to leverage the reputation for technological innovation through the acquisition of patents?