The serious situation that results from COVID-19 in today’s society has led to information society 4.0., where we find ourselves stepping on the accelerator towards a 5.0 society in which the person lies at the centre of the technological transformations that must be implemented. In 2015 the Japanese government proposed society 5.0., characterized by being a super-intelligent society in which needs are clearly differentiated and satisfied, and in such a way that required products and services are supplied in the necessary quantities to the people who demand them when and as they need them (Harayama, 2017). Regardless of their age, sex, region or language, all this allows people making up this society to receive high quality services that derive from combining different systems (e.g. energy, transport), which are supported by advanced organizational management systems. This means that three types of changes must be managed: economic and geopolitical change; technological change; and a change in people’s mentality. Therefore, the organizations making up this type of society must prepare to manage these changes by assuming new organizational models that allow their innovation and the development of these super-intelligent societies.

The OECD’s Oslo Manual (OECD/Eurostat, 2018) groups all innovation types into two categories: product (substantial improvements in goods or services) and business (improvements in business processes for different functions, including business innovations). This way of understanding innovation, which is closely linked with organizations’ technological and economic performance, contrasts with its eminently social nature because it is a phenomenon that always results from interactions between people (European Commission, 1995; Pol & Ville, 2009).

Pot and Vaas (2008) distinguished among technological, economic, and social innovation in organizations. Several research works have highlighted the enormous potential of taking into account the social side of innovation (both its means and ends) to complement its technological side and to, thus, respond in parallel to social challenges and needs (Pot, 2011; Pot et al., 2012; Pot & Vaas 2008). Social innovation has been defined as “the introduction and implementation of new ideas, activities, services, and processes in order to satisfy social needs of individuals, groups, and organizations within society, and to advance in social relations and social organization” (Looise, 1996 in Bondarouk & Olivas-Luján, 2014, p.11).

Scientific literature has investigated, from a variety of disciplines, the processes that lead organisations to generate greater innovation, with knowledge management (KM) being one of the most widely studied and cited as a sustaining pillar for organisations, especially in knowledge-intensive organisations (Widén-Wulff & Suomi, 2003). Within KM, knowledge sharing (KS), the ability to share knowledge within the organisation, has emerged as a core aspect (Hislop, 2013; Widén-Wulff & Suomi, 2003).

In general, KS is understood as a form of behaviour or process that occurs at the individual level, although it may be conditioned by individual, group, or contextual/organisational variables (Ahmad, 2018; Hussein et al., 2016; Razak et al., 2016; Zheng, 2017). Some of the commonly recognised factors that can influence KS are individual traits, such as experience, beliefs, or values (Wasko & Faraj, 2005), organisational or environmental traits, such as the organisational climate (Saleh & Wang, 1993) or a reward system (Farooq, 2018), and the technologies made available to the process together with the use that is made of them (Hall-Andersen & Broberg, 2014; Lin, 2007).

However, Widén-Wulff and Suomi (2003) shifted the focus from an individual to a systemic understanding of KS by proposing a knowledge-sharing organisation (KSO) model. Farooq (2018) follows in the same line by considering KS as a process that takes place between organisational units rather than in terms of individual behaviours.

Ayestarán (2017) integrated several research findings (Ilgen et al., 2005; Pot & Vas, 2008; Witherspoon et al., 2013) into a knowledge-sharing organization model, grounded in the a number of assumptions. First, a culture based on transparency and trust will lead to the practice of knowledge sharing and, hence, to a better innovation performance. Second, teams and organizations are required to develop shared mental models and transactive memory systems among their members. Third, creativity and cooperation are the key competencies for knowledge sharing and innovation in teams. Fourth, shared digital platforms for information and knowledge sharing will leverage innovation capabilities.

This paper aims to follow the path initiated by these authors (Ayestarán, 2017; Widén-Wulff & Suomi, 2003) by proposing an organisational model for managing KS based on the model proposed by Ayestarán (2017).

This article focuses on carrying out a systematic review on the three axes on which new organizational models must be based: economic innovation, technological innovation, and social innovation. For this purpose, a thorough examination of existing scientific evidence on the variables related to innovation in organizations was carried out. Later an alternative and integrating theoretical model was proposed, a new model for the 21^st century that takes cultural, psychosocial, and technological aspects as its central elements: the model of Knowledge-Sharing Organizations.

The methodology herein employed to review the scientific literature is the narrative review, which enables studies from different methodologies and theoretical perspectives to be related (Siddaway et al., 2019).

To perform this analysis, articles published from 2015 to 2020 in ABI-INFORM, JSTOR, Dialnet, ProQuest, and Scopus were considered. The selected keywords to search were based on innovation types: technological innovation, technical innovation, social innovation, work innovation, organizational innovation, and economical innovation. These keywords appeared in keywords and abstract fields. In the searches for the terms technical innovation and technological innovation, the term organizati* was added as a filter to any part of the text so that only the articles that at some point refer to some organizational element would emerge.

The results of the narrative analysis have been organised taking into account the levels of analysis proposed by the scientific literature on KM and KS. Many authors organise the variables surrounding KS into three levels (organisation, team or group, and individual; Ahmad & Karim, 2019; Zheng, 2017). Hussein et al. (2016) differ by identifying technological applications, in addition to employee motivation (individual level), and the organisational context (organisational level), as key to KS. However, the perspectives that view KS as an organisational process rather than as individual behaviour speak of dimensions or elements without organising them into levels. For example, Widén-Wulff and Suomi (2003) cite intellectual capital, building shared knowledge in business processes, communication as a key individual skill, a good ICT infrastructure and sufficient financial headroom as the main elements. Meanwhile, Farooq (2018) posited KS as a multidimensional construct consisting of organisational culture, organisational structure, reward systems, motivation, interpersonal trust, management support, and ICTs.

Taking the latter perspective as a starting point, we have set out the results of the analysis below, organised into key dimensions for innovation management and KS in organisations. Subsequently, in the conclusions we shall group the identified dimensions into three levels for an integrating understanding of KS management.

From a cultural perspective, an entrepreneurship culture and oriented in the long term is considered beneficial for innovative performance compared to an organization’s paternalistic and internal-oriented cultures (Laforet, 2016). Shahzad et al. (2017) found that an organization’s innovative performance is supported by a culture and climates that are characterized by flexibility and support for change. It has also been established that workers’ resistance to change and control and perception of risks negatively impact attitudes to adopt new technologies and work processes (Escobar-Rodríguez & Bartual-Sopena, 2015). García-Sánchez et al. (2018) also found that an organization’s internal flexibility benefits its performance through more innovation.

Based on the Competing Values Framework (CVF) by Cameron and Quinn (1999), Rezaei et al. (2018) found that adhocratic and clan cultures had a positive effect on both organizational learning and technological and administrative innovation. Conversely, hierarchical or market cultures act as barriers for innovation in organizations. However, Makvandi and Nejad (2018) reported that market orientation benefits an organization’s innovative capacity and performance. Finally, the existence of a culture oriented towards learning and knowledge management has been related to greater innovative capacity (Abdi et al., 2018).

Other authors have focused on investigating which capabilities of organizations can contribute to their innovative performance. They found that an organization’s learning capacity and creativity (Sutanto, 2017), learning oriented towards exploration and exploitation (D. Wang & Lam, 2019), organizational agility (Vaculik et al., 2019), business intelligence, and the ability to share knowledge (Eidizadeh et al., 2017) are all organizational capabilities or qualities that facilitate or benefit innovation.

Intellectual capital stands out as one of these organizational attributes for allowing innovation to improve. J. Li and Yu (2018) found that intellectual capital has a direct positive effect on technological innovation and an indirect effect on business model innovation through certain organizational characteristics. Y. Wang et al. (2019) observed how the efficiency of intellectual capital positively impacts an organization’s technological capacity. However, Xu et al. (2019) stated that intellectual capital (human, structural, relational) subdimensions have different effects on an organization’s performance and its technological innovation capacity, while the human capital and structural subdimensions positively impact organizational performance, but relational capital has a negative impact. Relational capital positively influences technological innovation, while human capital has a negative impact. In more generic terms, Manzaneque et al. (2017) confirmed that human, and structural and relational capitals are capable of improving a company’s performance in technological innovation. Jiang et al. (2019) demonstrated the positive impact of human capital on organizational innovation.

Finally, much effort has been made to investigate the benefits of absorptive capacity for organizations. This concept was first proposed by Cohen and Levinthal (1990) as an organization’s ability to recognize the value of new and external information, assimilate it and apply it for commercial purposes. Since then, many researchers have related this ability to innovative performance in both the technological and organizational fields (Del Carpio Gallegos & Torner, 2018; García-Sánchez et al., 2018; Kittikunchotiwut, 2015; Maldonado et al., 2019; Min et al., 2016).

Apart from being studied as a process, the incorporation of external knowledge into an organization can be seen as strategic commitment or a decision made by management, which makes it is a question of governance. The management team benefits the organization either directly by searching for external knowledge (Prasad & Martens, 2015) or indirectly by promoting collective orientation towards learning in the organization (Qian Chen & Liu, 2019).

Much research work has attempted to establish what kind of governance styles or what decisions that depend on top management can foster innovation in organizations. Innovation in governance is, in fact, the mechanism that allows an organization to fully benefit from technological innovations to improve its financial performance (erne et al., 2015). Fierro Moreno and Martínez (2015) found that certain strategy-dependent innovation dimensions (or strategic innovation) serve to explain good performance in organizational-type innovations. Specifically, integrating process innovation and innovation management into an organizational strategy positively impacts the organizational innovations related to leadership, organizational commitment, and culture. It has also been proven that quality-oriented governance directly affects technological, organizational, and management innovation (F. Zhou & Gu, 2019), and the possession of internationally recognized standardization certificates leads to higher productivity (Calza et al., 2019).

Marulanda-Grisales and Montoya-Restrepo (2015) studied the impact of knowledge management to find that it benefits technological innovation. Reciprocally, the factors related to technological innovation (i.e., perceived benefits or complexity), and to organizational (i.e., culture or support from management) and competitive pressure are beneficial influences for implementing knowledge management systems into companies (Wang & Wang, 2016). Finally, the role of interactive management control systems has been investigated, which involves face-to-face discussion with superiors, subordinates, and colleagues about the data generated by the system, and accelerates organizational and process innovation (Lopez-Valeiras et al., 2016).

Other authors have delved into the impact of organizational policies on organizations by studying the role of the practices and measures promoted from the human resources or people management. Reward systems areas, mostly through extra remuneration, found widespread support in the literature as precedents for innovative and organizational performance (Chaubey & Sahoo, 2019b; Rasool et al., 2019; J. Zhou et al., 2017). However, other authors have pointed out that placing emphasis on this type of extrinsic rewards to promote innovation can have counterproductive effects on the organizational cultures that are not conservative (Choi et al., 2019). There are also reports about the dispersion of remuneration in an organization according to the position occupied by someone, which maintains an inverted U relation with participation in the organization and, in turn, contributes to greater innovation (T. Wang et al., 2015).

It has been pointed out that organizational participation can serve as a precedent for further innovation (T. Wang et al., 2015). In line with this, some research works have delved into the operation of this variable and related it to the implementation of High Performance Work Systems (HPWS) (Rasheed et al., 2017). More specifically, implementing such systems has a positive impact on innovative performance when associated with participation mechanisms according to the organization’s intellectual capital level. Thus in organizations with low intellectual capital, HPWS enhance innovation when combined with more direct participation mechanisms. For organizations with high intellectual capital, implementing participatory governance is more beneficial (Y. Zhou et al., 2019).

Promotion and professional development opportunities are another measure that contributes to improve people’s innovative performance (Rasool et al., 2019; J. Zhou et al., 2017). Likewise, making efforts in the development of those people who already occupy management positions, through more autonomy (Linder & Torp, 2017) or the acquisition of ambidextrous capacity (Choi et al., 2019), also benefits organizational outcomes in innovation. Investing in training activities for people in an organization is perhaps the variable that obtains the most empirical support as a valid measure to promote innovation (Chaubey & Sahoo, 2019b; Cui et al., 2016; Fernández Perea, 2019; Naranjo-Valencia et al., 2018).

Finally, there are other policies that also benefit innovative performance, such as investing resources in research and development (Dan et al., 2018), or the nature of the work to be carried out (J. Zhou et al., 2017). It has also been established that incorporating talent by hiring personnel positively impacts innovative capacity (Manzaneque et al., 2017; Rasool et al., 2019).

A final aspect that has gained relevance in recent years is the relation between sustainability and innovation. It has been proven that the environmental awareness of a company’s CEO (Huang et al., 2019) or reducing the organization’s environmental impact (García-Pozo et al., 2018) positively influences innovative performance as both increase the chances of an organization performing more R&D activities. In the same way, corporate social responsibility (CSR) can be used to promote ethnic diversity in an organization, and this variable has been related to better technological innovation outcomes (Bocquet et al., 2019).

By way of conclusion, lots of measures can be adopted by the senior management or departments responsible for managing organizations’ human talent, and these measures and policies are closely intertwined with the organizational culture. Resende Junior and Fujihara (2018) stated that factors like R&D management or an innovation-oriented strategy are combined with an innovation-oriented culture and leadership.

The behaviour of leaders of teams, departments, or organizations evidently impacts their subordinates’ performance. In fact, the leader’s innovative behaviour alone can improve an organization’s innovative outcome (Waleed, 2015). By promoting interactions between employees so they ask for advice, how individuals perceive culture can improve and can be better adopted (Heinze & Heinze, 2018). Leadership oriented to people’s development can act as a mediator between adopting transactive memory systems, which allow knowledge sharing in an organization and the innovative performance of the latter (Kwon & Cho, 2016).

Transformational leadership has received solid empirical support as a promoter of organizational innovation (Ho & Fu, 2018), and this relation is moderated by people’s ability to take risks and to experiment (Mokhber et al., 2018). A positive relation has been detected between this type of leadership and employee creativity, as mediated by intrinsic motivation and self-perception of creativity (Chaubey & Sahoo, 2019a; Chaubey et al., 2019). Other leadership styles have been investigated, such as ethical leadership, which also serves as a predictor of individual and organizational innovation (Shafique et al., 2019). Finally, fostering ambidextrous capacities in leaders from middle management, that is, exercising ambidextrous leadership, has positive effects for business innovation (Choi et al., 2019).

To conclude, the current organizational environment requires leaders who not only function as bosses who control and manage, but also catalyze change to maintain and improve organizations’ innovative performance (Phornlaphatrachakorn, 2019).

Organizations’ current trend to flattening in their organizational structure and to use work teams as work basic units is an indisputable fact. The literature has shown that specialization of roles in project teams in general, and the existence of an executive and active role in particular, help to mitigate uncertainty, which improves performance in an innovation project (Kim & Shim, 2016). In the teams field, the need for good learning behaviour has been proven (i.e., the processes of seeking and providing feedback, sharing information, and preparing relevant information for the task), and exploration activities and exploitation are complementary and produce better results (Blank & Naveh, 2019).

When working in virtual teams, temporary virtual teams have been found to better perform in well-documented and methodical tasks, but lack of a relationship between members in complex tasks takes its toll on team productivity. Compared to temporary teams, permanent virtual ones better perform complex tasks that require a higher level of relationship between members (Kauffmann & Carmi, 2019).

A broad research field has studied the connections between individual innovation-oriented behaviour and specific variables to the organizational environment, such as leadership style, organizational culture and, of course, the organization’s innovative performance. Thus employees’ innovative behaviour has been empirically supported as mediators of the relation between HPWS and an organization’s innovative performance (Fu et al., 2015). Research has also been conducted on how people can foster innovation by playing different roles. For example, those individuals who support, facilitate, and stimulate innovation in their colleagues adopt a role called a “catalyst”, which allows research staff to obtain better results (Tortoriello et al., 2015).

Psychologically belonging to the organization and the empowerment of working people both have a positive effect on innovative individual behaviour. Furthermore, empowerment is a moderator for the relation between climate and organizational innovation (Liu et al., 2019).

The organizational environment can differently impact individuals through stressors of several natures. In line with this, a positive relation exists between challenging stressors (development opportunity, pressure from a supervisor, or having to meet a deadline for a task) and innovative behaviour sustained over time. Yet other stressors can act as obstacles (lack of clarity in responsibilities or conflicts with colleagues), which weighs down this type of behaviour. Self-perception of creativity acts as a mediator in these relations, while the organization’s innovative climate moderates the relation between self-perception of creativity and sustained innovative behaviour (He et al., 2019).

Finally, some research works have highlighted the positive effect on new companies of the entrepreneur’s passion to improve innovative performance and organizational performance (Ma, Gu, & Liu, 2017; Ma, Gu, Liu, et al., 2017).

Implementing and employing information and communication technologies (ICT) in an organization are closely related to either its innovative performance or the organizational-type innovations that occur (Divisekera & Nguyen, 2018; Quan Chen et al., 2020). Investing in ICT acquisition and hiring specialized personnel in its use positively impact ICT adoption in organizations (Loukis, Arvanitis, et al., 2017). In the corporate website context, the complexity and intensity of information, perceived benefits and management’s support favour the greater diffusion of innovation in this means (Zollet & Back, 2015).

Of all the different technologies that can be placed at the service of organizational innovation thanks to information processing, adopting ERP (enterprise resource planning) stands out for being associated with better organizational performance. This relation is also mediated by the usability of both the system and user satisfaction (Kharuddin et al., 2015).

In the interorganizational collaboration context, cloud computing has been identified as a means to not only reduce costs, but to also increase organizations’ capacities and flexibility (Loukis, Kyriakou, et al., 2017). The adoption of such technology by an organization is favoured by an ICT cost reduction strategy, the adoption of an innovation-oriented strategy, and management’s support of an organization’s ICT structure (Kyriakou & Loukis, 2019).

A closely related field of study to absorptive capacity has emerged that investigates how to improve innovative results through cooperation or collaboration with other organizations, which has led to the term open innovation. Lichtenthaler and Lichtenthaler (2009) stated that absorption capacity is only one of the knowledge capacities available for open innovation that an organization must develop, together with transformative, connecting, and inventive capacity. Shin et al. (2018) linked these capacities with performance in companies’ technological innovation.

Many authors have shown that organizations from different sectors and typologies (public institutions, start-ups, etc.) can benefit from interactions and collaborations. For instance, collaboration with citizens, universities, government organizations, industries in general, high-tech companies, and other organizations that are similar to them, professional associations or suppliers (Bandera & Thomas, 2019; Divisekera & Nguyen, 2018; Fuentes & Soto, 2015; Geldes, Heredia, et al., 2017; C. Li & Ghirardi, 2019; Parahoo & Al -Nakeeb, 2001; Radicic & Pinto, 2019). Despite the clear empirical support for its benefits, this process is not exempt of difficulties. For instance, the difficulty of social organizations to integrate and implement the knowledge generated by collaboration with other organizations into their processes has been verified (Phillips et al., 2019).

In the same direction, abundant research demonstrates the benefits of technological and non-technological innovation to incorporate external knowledge into organizations because it complements internal research activities (Anzola-Román et al., 2018; Fernández Perea, 2019; Hartono & Kusumawardhani, 2018; Simao & Franco, 2018). However, it has also been established that oversearching for external knowledge can negatively affect innovative performance (Hartono & Kusumawardhani, 2018). Similarly, exploration activities to incorporate new knowledge have been shown to be complementary to exploitation activities performed to improve innovative performance (Guisado-González et al., 2017; Torugsa & O’Donohue, 2019).

Al-Busaidi and Olfman (2017) found that factors related to knowledge workers and the support received from colleagues largely determine these workers’ intention to share knowledge through interorganizational systems designed for this purpose. They suggested that fostering a knowledge-oriented culture, a structure designed for this purpose and technological skills, can indirectly facilitate these knowledge exchange flows between workers from different organizations.

This paper proposes an explanatory model of innovation in the organizational context from the organizational psychology perspective and continues the work of Ayestarán (2017) by focusing on the variables that intervene in the psychological, social, and cultural spaces that exist in all organizations merely because they are made up of people. The three differentiated levels in the model intend to collect all the elements that determine organizations’ innovative in the culture field, governance and people’s interactions with one another, with their leaders or subordinates, and also with the surrounding environment. A technological level is added to take into account the psychological and social aspects that come into play when employing technologies designed to transmit and generate information.