The Impact of Food Trends on Menu Innovation

Introduction

Over the years, restaurateurs had managed to tap into trendy menus. In recent years, however, the waves of food trends have been increasingly unpredictable. This poses a great challenge for restaurateurs and chefs to be equipped with latest innovation ‘know-how’ in managing their new menus innovation. This is because, as globalization enters a borderless world, diverse trends in global consumer food preferences and acceptances become apparent, and such trends create wider menus repertoires. Yet, innovating what are ‘hot’ and ‘trendy’ food items is not an easy business, as nowadays, global consumers are increasingly becoming well-traveled and affluent from a palette standpoint. This issue of concerns poses challenges for menu planners to stay afloat in their business. Simply put, exiting menus that able to capture of today’s consumers’ preferences may have to be rejuvenate likely unable to sustain longer in the market as new rival competitors with similar menu concept joined the fray (Thompson & Strickland, 1996). Thereby, what is known to be a lucrative menu has turned to a mundane one as the act of ‘copy-cat’ saturates the consumer market (Jones & Wan, 1992). Such a scenario has led to fierce competition amongst restaurateurs along with new entrants, battling to gain market shares, which in turn make the consumer market reaches its saturation point even quicker. A point that marks the end of the product life cycle in the consumer market largely due to abandon supplies of similar products (Hashimoto, 2003). Strategically, in shaping competition, innovation is widely used to resurrect the curve of the product life cycle upwards (Cobbenhagen, 2000).

Generally, innovation management to new product development (NPD) falls either being driven by radical or incremental orientations. Notably, these two product innovation orientations are widely used across different industries in shaping the NPD process. Theoretically, such a relationship between innovation orientations and NPD process is deemed inseparable as innovation orientation spurs the outcome of the NPD in the marketplace (Drucker, 1985). However, to which innovation orientations to be integrated in shaping the NPD process is also dependent on menu planners’ adaptations to the surrounding environmental factors (Ettlie & Subramaniam, 2004). Technological innovations have been widely used across different restaurant sectors in shaping NMD to be more effective and efficient in term of speed, frequency, standardization and consistency (Reid & Sandler, 1992; Nerker & Roberts, 2004). While the use of technological innovations in managing menu innovation has been long incorporated in production of new menus in many ways (Porter, 1985), another external factor that is equally important is the rapid changing trends of global food consumptions.

This chapter then put forward a discussion of the impact of global food trends and its implication for restaurant management. The objective of this chapter is to provide empirical evidence on the impact of food trends, as a moderator, between the links of innovation orientations and NPD process in chain restaurants based on a recently concluded research study in the region of Klang Valley, Selangor, Malaysia. In this chapter, trends of food acceptances, preferences and habits highlighted in literature were qualitatively analyzed, adopting a content analysis method, to identify the emerging themes of global food trends. Seven types of food trends emerged from this analysis, which is shown in Table 1¹, which then used as a framework for the respondents to describe its influences in their recent engagement of menu innovation and development process. In order to accurately assess the moderating effect of food trends on managing menu innovation, in this chapter, Partial Least Square (PLS-SEM) path modeling (or component-based structural equation modeling) was used to estimate the interaction effect and the reflective hierarchical model of NMD process as this would achieve more theoretical parsimony and less model complexity (Chin, 2010; Wetzels, Schoder & Oppen, 2009; Edward, 2001; Mackenzie, Podsakoff & Podsakoff, 2005).