Visual Cryptography (VC) is a category of secret sharing scheme, proposed by Naor et al. [17], that allows computation-less decoding of secret images. Mainly in a k-out-of-n visual secret sharing (VSS) scheme, a secret image is encoded into n noise-like shares and printed onto transparencies to distribute them among n participants. Secret image can be decoded by just stacking any k or more transparencies. In spite of using infinite computation power, k − 1 or fewer participants can not decode the secret image. Besides the secret sharing, visual cryptography can also be used for number of other purposes including access control, watermarking, copyright protection [5], identification [16] and visual authentication. To demonstrate the working of VSS, consider a 2-out-of-2 VSS (k = 2, n = 2) scheme shown in Fig. 1. Each pixel p of secret binary image is encoded into a pair of black and white subpixels for both shares. If p is white/black, one of the first/last two columns tabulated under the white/black pixel in Fig. 1 is selected randomly so that selection probability will be 50 %. Then, the first two subpixels in that column are alloted to share 1 and the following other two subpixels are alloted to share 2. Independent of whether p is black or white, pixel is encoded into two subpixels of black-white or white-black with equal probabilities. Thus an individual share has no idea about whether p is black or white. The last row of Fig. 1 shows the superimposition of the two shares, If the pixel p is black, the output of superimposition will be two black subpixels corresponding to a gray level 1. If p is white, then result of superimposition will be one white and one black subpixel, corresponding to a gray level 1/2. Hence by stacking two shares together, we can obtain the approximate visual information of the secret image. Figure 2 shows an example of the 2-out-of-2 VSS scheme. Figure 2a shows a secret binary image Isec to be encoded. According to the encoding scheme shown in Fig. 1, each pixel p of Isec is divided into two subpixels in each shares, as shown in Fig. 2b and c. Stacking the two shares leads to the output image shown in Fig. 2d. The decoded image is clearly revealed. There are some contrast loss and the width of the reconstructed image is just twice of the original secret image. The 2-out-of-2 VSS scheme shown above is a special case of the k-out-of-n VSS scheme. A more general model for VSS schemes based on general access structures has been designed by Ateniese et al. in [1]. An access structure is a specified set of all the qualified and forbidden subsets of the shares. The secret image can be decoded by the participants of a qualified subset only. The capabilities of VSS has also been enhanced by allowing gray scale images as secret rather than a binary image [15].

Smart manufacturing describes the new manufacturing intelligence applied to modern information technologies such as the Internet of Things, cloud computing, and artificial intelligence in the manufacturing process (Tao et al. 2018a, b). Data from various sources is becoming integrated into manufacturing intelligence to improve manufacturing in various ways. A typical example is the wide deployment of sensors in manufacturing to monitor and provide real-time manufacturing data such as temperature, humidity, speed, vibration, and acidity for better decision-making and control of the manufacturing process (Li et al. 2015). Therefore, multiple sensors of different modalities are needed in distributed locations. Wired sensor networks are extensively adopted, but the cost for their installation, testing, maintenance, and shutdown are quite high. In many cases, a wireless sensor network (WSN) is a more attractive alternative because it does not require any fixed infrastructure and can be applied over distributed areas where cabling is costly (Akyildiz et al. 2002). A WSN is composed of sensor nodes and sink nodes. Sensor nodes are responsible for collecting and forwarding data. Sink nodes either analyze data locally or forward data to a base station (Magaia et al. 2015). Each sensor node consists of small devices for sensing, processing, transceiving, and power, and it is able to communicate with other sensor nodes or directly with a base station. A WSN can be regarded as a self-supporting unit, with which unattended operation can be realized (Stankovic 2008). It is mostly applicable where power supplies and cabling are difficult, or in hostile environments that people cannot enter. With the technological developments in cloud computing and Internet of Things, WSNs are becoming even more widely deployed. They have also been used for machine health monitoring (Tiwari et al. 2007), data center monitoring (Wang et al. 2013), and data logging (Saleem et al. 2014) in various industries. Moreover, a WSN can be used for environmental monitoring (Hart and Martinez 2006), remote health care monitoring (Malasinghe et al. 2017), and other monitoring scenarios. Overall, its application in cloud computing and Internet of things and has significant social and economic benefit (Tao et al. 2014, 2011). WSNs have a key difference with the wired sensor network in that the sensor nodes in a WSN rely solely on their own battery, and thus have limited power resources. Moreover, their computing power and storage resources are also limited. As a consequence, reducing the energy consumption of each sensor node is a critical issue for WSNs (Carrabs et al. 2015). Ant colony optimization (ACO) has been used in WSNs to identify shortest paths, and thus reduce the energy consumed by a network. However, the ACO is prone to falling into local optima and converges slowly. We hence propose an improved ACO (IACO) that can be used to construct the sensor node transfer function and pheromone update rule, and adaptively construct a data route using the characteristics of a dynamic network. The major contributions of this paper are listed below. 1. The IACO consumes less energy. 2. The transmission delay is reduced using the IACO. 3. Fewer transmission packets are lost when using the IACO. This paper is organized as follows. Section 2 analyzes the current WSN routing algorithms and their weakness. In Sect. 3, we describe our model and its notation. In Sect. 4, a detailed description of the proposed routing IACO algorithm is given. In Sect. 5, simulation results comparing the proposed algorithm with several other algorithms are presented to show the effectiveness of the proposed method.

The general assumption of majority of flow-shop applications is that the sequencing of the jobs relies on buffers, otherwise they are considered as intermediate storage between machines. The no-wait flow-shop scheduling problem (NWFSP) with makespan criterion is considered in this paper. In NWFSP, each job has to be processed from the first machine to the last without any interruption and the job sequence is unique on all machines. In addition, each machine can handle no more than one job at a time and each job has to visit each machine exactly once. Therefore, the start of a job on the first machine may be delayed in order to meet the no-wait requirement. Given that the release time of all jobs is zero and set-up time on each machine is included in the processing time, the no-wait flow-shop problem is to schedule jobs that minimize the makespan over all jobs. The no-wait flow shop scheduling problem has important applications in modern industry. It has an extensive background in industrial applications, including steel production, mining, logistics, chemical industry and food processing. For example, in steel factories, to avoid cooling and defects in steel, the liquid steel undergoes a chain of operations such as molding into ingots, unmolding, and soaking. Similarly, in the food processing, the canning operation must be operated after cooking operations immediately to ensure freshness (Hall and Sriskandarajah 1996). A comprehensive survey on the research and application of no-wait flow-shop scheduling problem can be found in Hall and Sriskandarayah’s review paper (Hall and Sriskandarajah 1996). In addition, Bagchi et al. (2006) showed that it can be transformed into the asymmetric traveling salesman problem (ATSP) and presented some no-wait and blocking scheduling models. In the operation research literature, many elegant mathematical models and methods have been developed to deal with the real-world problems. Some exact approaches have been proposed to unravel the problems optimally that have the limitation of solving only small-sized problems. On the other hand, heuristics which are based on polynomial time algorithms are the most suitable methods for solving large scheduling problems. In general, heuristics attain good solutions in a reasonable time interval. Additionally, in the recent years meta-heuristics with techniques such as bee colony algorithm (Khorramizadeh and Riahi 2015), genetic algorithm (GA) (Guo et al. 2005), memetic algorithm (MA) (Frutos and Tohme´ 2013), particle swarm optimization (PSO) (Marinakis et al. 2009), Electromagnetism-like Mechanism (SEM) (Bonyadi and Li 2010), and ant colony optimization (ACO) (Riahi and Kazemi 2015) have been developed to generate competitive results for many combinatorial optimization problems. In the recent years, several ant colony algorithms have developed for various kinds of problems including the scheduling problem. Kashan and Karimi (2008) proposed two ACO algorithms with two different visibility functions for total weighted tardiness single machine environment with formation of processing batches. To minimize total completion time in a no-wait two-machine flow-shop, Shyu et al. (2004) designed some specific features including a heuristic for initializing the initial pheromone, a hybrid state transition rule and a hybrid local search. Li et al. (2011) dealt with the minimization of the sum of completion time in a sequence-dependent setups permutational flowshop. The authors used a timelimited dynamic programming algorithm to perform a post-optimization strategy. Mirabi (2011) proposed an ant colony optimization technique for the sequencedependent flowshop scheduling problem.

With increasingly uncertain and volatile business environments, strategic flexibility, which refers to firms’ ability to reallocate and reconfigure their organizational resources, processes, and strategies to deal with environmental changes (Zhou and Wu, 2010), is a key business imperative (Sambamurthy et al., 2003; Chen et al., 2014). Firms’ chances of survival in volatile marketplaces, global competition, shortened product life cycles, and customer pressures for tailored offerings depend largely on their ability to adapt rapidly to environmental changes (Nadkarni and Herrmann, 2010; Zhou and Wu, 2010). Faced with these challenging external demands, firms’ adaptability seems to hinge on their strategic flexibility (Drnevich and Croson, 2013). Information technology (IT) is a key enabler of strategic flexibility, and firms reply on it for automation, cost reduction, and improvement of operational efficiency (Duncan, 1995; Bhatt and Grover, 2005). Beyond supporting tactical and operational impacts, IT is instrumental in helping firms support or transform strategies, business models, and relationships between companies and their partners and customers (Bharadwaj et al., 2013). IT provides companies with advanced computing capacity, information processing and analytic abilities, and stronger empowering capabilities, helping them enter new markets and launch new ways to conduct business. For example, the Chinese mobile-device maker Xiaomi has built a community of fans to collect feedback and recommendations for product designs (McKinsey, 2015). Chemical manufacturers use big data to help farmers monitor crop conditions in real time and customize their offerings to increase farm yields (McKinsey, 2015). IT is needed to support firms’ rapid product development, and collection and dissemination of market, product and process information to respond effectively to unanticipated changes in the business environment. Recent studies posit that IT capabilities (e.g., IT management and IT competencies) help firms to exploit opportunities and reconfigure IT resources to avoid disadvantageous outcomes, demonstrating IT’s key role in attaining and deploying strategic flexibility.1 Extant conceptual frameworks propose several relationships involving IT capabilities, flexibility and firm performance, and recent papers have examined the proposed relationships empirically. For example, Lu and Ramamurthy (2011) provide evidence of the links between IT capabilities and operational and marketing flexibility. Tallon and Pinsonneault (2011) consider the relations between the combination of customer, partnering and operational flexibility, and organizational performance as moderated by environmental volatility. Chen et al. (2014) show that IT capability can improve firm performance by means of process flexibility.

Re-emerging Asian countries promise to regain their high level of contributions to world production and trade, and India has the resources to innovate sustainably. The leadership provided by relatively less populous countries such as Japan, Korea, Taiwan, Singapore and Hong Kong has been motivating many countries, including larger ones such as China, India and Iran. Japan may not have the youth of 1964 (hosting the first Olympics in Asia), but has growing maturity of institutions, along with technological and innovation capabilities, to be a role model as it revitalizes for 2020. It will become the first Asian country to host the Olympics twice. From its spectacular successes in engineering (e.g. defence to space; Kalam and Rajan 2002), India is moving ahead on innovations that benefit the masses (e.g. ICT access) or profit businesses internationally (e.g. IT services). Some firms of Indian origin (FIOs) have been trying painstakingly to compete internationally, not only in software services, but also in technology-based emerging industries such as biopharmaceuticals, where cooperative strategies with institutes can be highly fruitful. Higher stages in the competitiveness journey of firms (Momaya 2001) or institutions can demand much higher flexibility (e.g. Sushil 2007) and health for systematic innovation (Krishnan 2010). The capitalist model of economic development that India has embraced depends on the industrial firm as the driver of progress. As cost advantages erode rapidly in the face of intensifying competition (reaching levels of hyper-competition in several markets in India, e.g. automotive, electronics, FMCG, telecom), focal FIOs (FFIOs) will have to discover other sources of competitive advantages or character competence (e.g. Sharma 2016). Flexibility, innovation and vitality can be some of those sources of advantages. Flexibility has been emerging as an important concept that can link functional silos (such as operations, marketing and finance) to provide agility to organizations to cope up with change. The paradigm of ‘strategic flexibility’ came to the forefront of industrial practice, which synthesized the strengths of most of the previous theories of strategic management in a novel way to fill gaps (Sushil 2015, 2016). Strategic flexibility also acts as a predictor of vitality and sustainability of the enterprise (Sushil 2011). Innovation on multiple fronts— from process and product to business model and technological—is becoming important as India aspires to accelerate on competitiveness enhancement and growth. While firms are the key drivers of growth and adapt from many alternate strategies for growth (Ghosh 2010), focal institutes can also play an important role to shape the ecosystem of competitive industries.

Innovation research (Roy and Sarkar 2016; Tanriverdi 2005; Wu and Chen 2014; Yayavaram and Chen 2015) provided insightful findings that knowledge process capabilities (KPC) were highly associated with innovation performance of firms. However, several researchers (i.e. Anderson et al. 2014; Sirmon et al. 2011; Vera et al. 2016) state that studies which empirically examine the effects of contextual variables on this relationship are rare in the field. The dynamic capabilities (DC) view suggests that a specific resource and capability deployment and/or leverage can be contingent on the firms’ dynamic resource management capabilities along with the nature of the external environment. Therefore, the effects of these contextual variables should not be isolated from a research model in which a resource or a capability and organisational performance relationship is existent (Schilke 2014; Sirmon et al. 2011; Sirmon et al. 2007; Wei et al. 2014). According to the DC view, firms can adapt to a rapidly changing environment through developing strategic flexibility (Matthyssens et al. 2005; Wu and Chen 2014; Zhou and Wu 2010) and the effects of environmental dynamism (ED) and strategic flexibility (SF) can be a determining factor on organisational performance (Schilke 2014; Sirmon et al. 2008). Innovation performance was assessed through a number of dimensions in the literature. Especially, the number of patents and/or the number of new products were commonly used to assess innovation performance (Anderson et al. 2014; Latham and Braun 2009; Wang and Libaers 2016). Similarly, a performance measure such as the number of new product configurations was employed in this study (Terziovski 2010). A new work method or a process should be considered as a type of innovation in manufacturing industries since a new method or a process can substantially change the way that the main product is produced (Leiponen and Helfat 2010; Wang et al. 2016). Therefore, improvement in work methods and processes were used as another innovation dimension in this research. Performance should be associated with success. However, simply focusing on the numbers of new products and processes may produce artificial results on the way of assessing innovation success. For this reason, success of new products and speed to market were also used as other dimensions for innovation performance (Anderson et al. 2014; Terziovski 2010). Thus, consistent with the aims of the research and through a multi-dimensional performance assessment, this study reveals the separate and combined effects of KPC, ED and SF on the four dimensions of innovation performance: number of new product configurations, success of new products, speed to market and improved work methods and processes (Liu et al. 2015; Terziovski 2010).

Besides seismic base isolation [1,2] and other strengthening techniques [3], the use of fluid viscous dampers (FVDs) as energy dissipation devices has rapidly increased in the last few years [4] for seismic retrofitting of existing civil engineering structures. Their appealing properties include the large energy dissipation capability, the low maintenance required, and the generation of forces that are out of phase with the elastic forces, thereby not increasing the stress in the structure. Experimental evidence [5–9] reveals that the constitutive behavior of FVDs can be described by a fractional velocity power law f = cu u | | sgn( ̇ ̇) NL α d d (1) where cd is the damping coefficient, α is a velocity exponent, u̇ represents the relative velocity at the ends of the device and sgn(⋅) is the signum function. The exponent α is responsible for the nonlinear damping of FVDs and depends upon the hydraulic circuit employed. Typically, α ranges from 0.10 to 0.50 for seismic applications. As a result of the nonlinear constitutive behavior of FVDs, linear methods of analysis, e.g., the response spectrum method, are no longer applicable. In this regard, attempts have been made to determine an equivalent viscous damping ratio due to the added nonlinear FVDs [10], or an equivalent damping coefficient of energy-equivalent FVDs associated with the same energy dissipation as the nonlinear FVDs [11]. The above-mentioned relationships, extremely useful for preliminary design purposes, are amplitude-dependent due to the nonlinear nature of FVDs. Furthermore, they are strictly valid under the hypothesis of harmonic motion. Since the earthquake-induced motion is not really harmonic but is generally modelled as a random process, the estimation of the equivalent damping coefficient can be alternatively performed within the framework of the stochastic linearization technique (SLT) [12]. The SLT was applied in a number of research papers and to a general class of nonlinear behaviors, not just confined to the one shown in (1), e.g.: in the context of tuned liquid column damper optimization [13,14], characterized by quadratic-times-signum-like damping; in the field of nonlinear energy sinks optimization [15], featured by a cubic stiffness; for optimizing the performance of hysteretic dampers [16], represented by a Bouc-Wen model. The SLT has also been successfully applied to derive equivalent linear properties of bilinear systems [17,18] in the framework of response spectrum analysis [19,20]. Coming back to the nonlinearity of FVDs given by (1), which is of interest to the present note, in the literature the SLT has been applied by introducing the stochastic nature of the earthquake input via a powerspectral-density (PSD) function S ω ü ( ) g , for example the Kanai-Tajimi PSD [21–23], or a particular spectrum-compatible PSD function [24,25]. All these quoted papers used a popular force-based Gaussian SLT (F-G-SLT). In this note, it is demonstrated that this variant of SLT is not the best option for the kind of nonlinearity induced by the FVDs. After a critical assessment of six alternative formulations of SLT, we identify a so-called equal-energy non-Gaussian SLT (EE-NG-SLT) that, without increased computational effort, is better able to capture the nonlinear response of the system.

Management forecasts are one of the primary ways managers voluntarily disclose private, future-oriented financial information to capital market participants (Healy and Palepu 2001). As such, they represent an important component of a firm’s overall information environment (Hirst et al. 2008). Research finds that management forecasts are more informative than other types of financial disclosures, including earnings announcements, filings required by the Securities and Exchange Commission (SEC), and analyst forecasts (Beyer et al. 2010). In this study, we investigate the relation between management forecasts (and the characteristics of those forecasts) and firms’ cost of equity capital (COC) in an international setting. We also examine whether these relations are influenced by country-specific institutional factors. Theory suggests that voluntary disclosure should reduce firms’ COC by reducing estimation risk, information asymmetry, or both (Diamond and Verrecchia 1991; Easley and O’Hara 2004; Hughes et al. 2007). Although some evidence using U.S. data supports this idea (Baginski and Rakow 2012; Balakrishnan et al. 2014), little empirical evidence in non-U.S. settings exists.1 Ex ante, it is unclear whether and how management forecasts affect the COC for international firms because institutional factors that might influence this relation can vary substantially across countries. We examine three institutional factors that we posit should be important for the relation between management forecasts and the COC—namely, investor protection, media penetration, and mandatory disclosure requirements. Theoretical work demonstrates that disclosure must be credible to be informative and reduce information asymmetry and the COC (Crawford and Sobel 1982; Stocken 2000; Verrecchia 2001), but little direct empirical evidence exists.2 An international setting provides an advantage when exploring this issue because country-level institutional factors can affect the level of disclosure credibility and the importance of credibility cannot be well understood within a single regime (Ball et al. 2012).3 We also examine the impact of media penetration because the media plays a critical role in distributing firm-specific information to investors and hence influences the ability of management forecasts to reduce the COC.4 While prior cross-country studies focus mainly on how media penetration influences the response to mandatory disclosure (Griffin et al. 2011; Cao et al. 2016), we suggest that the effect of the media should be stronger for voluntary disclosure because mandatory disclosure is often required to be publicly distributed through specific channels established by stock exchanges. Thus, mandatory disclosure is typically accessible to investors even when the media is not highly developed. Our findings on the importance of media penetration for the relation between voluntary disclosure and the COC complement findings from previous mandatory disclosure studies and provide support for the critical role that the media plays in the dissemination of voluntary disclosure.

Relationship marketing is an important topic in marketing theory because of its crucial role in helping develop and maintain mutually profitable and sustainable relationships among buyers and sellers (Gruen, Summers, & Acito, 2000; Morgan & Hunt, 1994; Samaha, Beck, & Palmatier, 2014). In this context, a recent meta-analysis of 170 studies in 36 countries reveals significant differences in the effects of relational mediators such as trust and commitment across different cultures (Samaha et al., 2014). However, there is not much research into the underlying socio-cultural mechanisms that may drive these differences, such as some unique cultural factors that drive relationship marketing process around the world (Gap 1). Interestingly, there is also a large body of research on guanxi, a social exchange mechanism built on mutual favors, which is considered an important strategic asset by most Chinese people and businesses (Davies, Leung, Luk, & Wong, 1995; Park & Luo, 2001). Guanxi has played a major role in the Chinese society throughout its long history (Alston, 1989) and the modernization and globalization of the Chinese economy in the last few decades has even forced multinational companies to look for ways to establish their own guanxi networks in China (Davies et al., 1995). However, despite some attempts to compare the concepts of relationship marketing and guanxi (e.g., Ambler, Styles, & Xiucun, 1999; Leung, Lai, Chan, & Wong, 2005; Shaalan, Reast, Johnson, & Tourky, 2013; Wong & Chan, 1999), the exact roles played by guanxi and its underlying dimensions in mainstream relationship marketing, particularly their impact on customer loyalty in the services context, are still not very clear or well-established (Gap 2). People’s Republic of China (PRC) is the most populous country in the world and the largest economy, as measured on purchasing power parity (PPP) basis, well ahead of United States and European Union (Central Intelligence Agency, 2016). China is also culturally very different from the Western countries, with strong collectivistic values and high long-term orientation (Hofstede, 2001). Hence, it is not surprising to see that international relationship marketing studies find Chinese firms twice as effective in using relationship marketing to drive performance as those in the United States (e.g., Samaha et al., 2014). However, despite growing evidence about the differences in the business and cultural practices between China and the rest of the world (Sharma, 2010), past research on relationship marketing, especially its impact on customer loyalty, has generally ignored China and focused on Western cultures (Sharma, 2011) (Gap 3). Finally, past research on the role of guanxi in the Chinese context has mainly focused on buyer-seller relationships in the business-to business (B2B) context and on organizational performance outcomes such as sales growth (e.g., Barnes, Leonidou, Siu, & Leonidou, 2015; Barnes, Yen, & Zhou, 2011; Leung, Chan, Lai, & Ngai, 2011; Park & Luo, 2001; Yang & Wang, 2011). Samiee, Chabowski, and Hult’s (2015) meta-analysis of international cross-border relationship marketing studies also shows that most studies focus on B2B context with very few studies on business-to-consumer (B2C) relationships. Hence, it is not clear to what extent the same relationship marketing constructs and models can apply in B2B and B2C contexts, and what could be the theoretical differences between them (Gap 4).